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3 episodes with ray ozzie
2 episodes with ray ozzie
2 episodes with ray ozzie
My guest today is Ray Ozzie, one of the great technologists, software developers, and entrepreneurs of our time. Ray is perhaps best known as the creator of Lotus Notes, a collaboration tool that revolutionized business communication in the 1990s. He later succeeded Bill Gates as Chief Software Architect at Microsoft, where he played a key part in the development of Azure, Microsoft's cloud computing platform. Ray's work has earned him numerous accolades, including induction into the Computer History Museum Hall of Fellows and the National Academy of Engineering. Throughout his career, Ray has been at the forefront of technology innovation and paradigm shifts, founding multiple companies, including Iris Associates, Groove Networks, and most recently, Blues Wireless, which focuses on connectivity in the physical world. His insights on cloud computing, collaboration tools, and the future of technology have shaped the industry for decades. In our conversation, we explore Ray's journey through the evolving landscape of software development, his perspectives on the current state of technology, and his vision for the future of connectivity and collaboration. Please enjoy this fascinating discussion with Ray Ozzie. Subscribe to Glue Guys! For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- This episode is brought to you by Ramp. Ramp's mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Ramp is the fastest growing FinTech company in history and it's backed by more of my favorite past guests (at least 16 of them!) than probably any other company I'm aware of. It's also notable that many best-in-class businesses use Ramp—companies like Airbnb, Anduril, and Shopify, as well as investors like Sequoia Capital and Vista Equity. They use Ramp to manage their spending, automate tedious financial processes, and reinvest saved dollars and hours into growth. At Colossus and Positive Sum, we use Ramp for exactly the same reason. Go to Ramp.com/invest to sign up for free and get a $250 welcome bonus. — This episode is brought to you by Tegus, where we're changing the game in investment research. Step away from outdated, inefficient methods and into the future with our platform, proudly hosting over 100,000 transcripts – with over 25,000 transcripts added just this year alone. Our platform grows eight times faster and adds twice as much monthly content as our competitors, putting us at the forefront of the industry. Plus, with 75% of private market transcripts available exclusively on Tegus, we offer insights you simply can't find elsewhere. See the difference a vast, quality-driven transcript library makes. Unlock your free trial at tegus.com/patrick. ----- Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. Follow us on Twitter: @patrick_oshag | @JoinColossus Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com). Show Notes: (00:00:00) Introduction to Ray's Story (00:06:44) The Role of Technology in Modern Warfare (00:10:26) The RadNote Device Explained (00:15:32) The Origin of SafeCast (00:22:26) Challenges in Building Intelligent Machines (00:32:23) The Evolution of IoT and Blues (00:39:01) The Future of Connected Machines (00:46:03) Technology Paradigm Shifts and Azure (00:50:56) The Birth of Azure (00:52:08) The Unique Dynamics of Bill and Steve (00:56:54) AI and the Future of Use Cases (00:59:00) Real-world Applications of IoT (01:05:31) The Evolution of AI and IoT (01:20:03) The Importance of Systems Thinking (01:28:52) Advice for Young Entrepreneurs (01:32:55) The Kindest Thing Anyone Has Ever Done For Ray
It's a BIG news episode this week, as Leo Laporte and Paul Thurrott discuss the sudden departure of Panos Panay from Microsoft to Amazon. Did he jump or get pushed out of the company? Leo and Paul also dive into a massive leaked memo about Microsoft's Xbox roadmap and plans over the next 10 years. Other topics include Microsoft's upcoming event focused on AI integration across products like Windows, Office 365, and Surface, as well as online account consolidation and Google's Takeout service. We're down a Panay After his curiously off-kilter performance at Build 2023 this past May, Microsoft reveals that it is parting ways with Panos Panay. Are these things related? He's landing at Amazon devices, which makes sense given David Limp is retiring Multiple Microsoft executives and employees have reached out privately about this Comparisons to Terry Myerson's exit Blockbuster Xbox leak Major Xbox leak reveals Xbox Series X|S mid-season upgrades, next-gen console, new controller, and more This is one of the top three Microsoft leaks that's happened in Paul's nearly 30 years of covering this company An analysis of just one of the documents in this leak reveals an incredible amount of strategy information for the next 10 years And one about the reaction to the PS5 reveal Microsoft's upcoming AI event Expectations for this event, the kick-off for Microsoft's full-on client AI push Sub-analysis: This could be Microsoft CTO Kevin Scott's "Ray Ozzie moment." There's a profile of this mostly unknown in the WSJ Intel announces NPU-powered Core Ultra CPUs - off schedule? Related to the MSFT event? Bing Chat gains two new mobile integrations Paint keeps getting AI features. Remember when Paint was the laughing stock of Windows 11 apps? Windows No new builds (of substance, there was an RP build). Windows Photos, Snipping Tool, and Phone Link are all getting new features in Insider. Is Microsoft finally taking its in-box apps seriously (again)? Google extends the support lifecycle for ChromeOS, solving the single biggest criticism of this platform. This is problematic for Microsoft Microsoft 365 EU will reject Microsoft's offer to unbundle Teams from Microsoft 365 Surface Surface Laptop Studio 2 and Laptop Go 3 leak. They will likely be announced at the special event Xbox Microsoft announces more Xbox Game Pass titles for September Bethesda's The Elder Scrolls VI will be an Xbox exclusive. Tips and picks Tip of the week: Consolidate and organize your online accounts App pick of the week: Google Takeout Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to this show at https://twit.tv/shows/windows-weekly Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Sponsors: nureva.com/twit Miro.com/podcast
It's a BIG news episode this week, as Leo Laporte and Paul Thurrott discuss the sudden departure of Panos Panay from Microsoft to Amazon. Did he jump or get pushed out of the company? Leo and Paul also dive into a massive leaked memo about Microsoft's Xbox roadmap and plans over the next 10 years. Other topics include Microsoft's upcoming event focused on AI integration across products like Windows, Office 365, and Surface, as well as online account consolidation and Google's Takeout service. We're down a Panay After his curiously off-kilter performance at Build 2023 this past May, Microsoft reveals that it is parting ways with Panos Panay. Are these things related? He's landing at Amazon devices, which makes sense given David Limp is retiring Multiple Microsoft executives and employees have reached out privately about this Comparisons to Terry Myerson's exit Blockbuster Xbox leak Major Xbox leak reveals Xbox Series X|S mid-season upgrades, next-gen console, new controller, and more This is one of the top three Microsoft leaks that's happened in Paul's nearly 30 years of covering this company An analysis of just one of the documents in this leak reveals an incredible amount of strategy information for the next 10 years And one about the reaction to the PS5 reveal Microsoft's upcoming AI event Expectations for this event, the kick-off for Microsoft's full-on client AI push Sub-analysis: This could be Microsoft CTO Kevin Scott's "Ray Ozzie moment." There's a profile of this mostly unknown in the WSJ Intel announces NPU-powered Core Ultra CPUs - off schedule? Related to the MSFT event? Bing Chat gains two new mobile integrations Paint keeps getting AI features. Remember when Paint was the laughing stock of Windows 11 apps? Windows No new builds (of substance, there was an RP build). Windows Photos, Snipping Tool, and Phone Link are all getting new features in Insider. Is Microsoft finally taking its in-box apps seriously (again)? Google extends the support lifecycle for ChromeOS, solving the single biggest criticism of this platform. This is problematic for Microsoft Microsoft 365 EU will reject Microsoft's offer to unbundle Teams from Microsoft 365 Surface Surface Laptop Studio 2 and Laptop Go 3 leak. They will likely be announced at the special event Xbox Microsoft announces more Xbox Game Pass titles for September Bethesda's The Elder Scrolls VI will be an Xbox exclusive. Tips and picks Tip of the week: Consolidate and organize your online accounts App pick of the week: Google Takeout Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to this show at https://twit.tv/shows/windows-weekly Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Sponsors: nureva.com/twit Miro.com/podcast
It's a BIG news episode this week, as Leo Laporte and Paul Thurrott discuss the sudden departure of Panos Panay from Microsoft to Amazon. Did he jump or get pushed out of the company? Leo and Paul also dive into a massive leaked memo about Microsoft's Xbox roadmap and plans over the next 10 years. Other topics include Microsoft's upcoming event focused on AI integration across products like Windows, Office 365, and Surface, as well as online account consolidation and Google's Takeout service. We're down a Panay After his curiously off-kilter performance at Build 2023 this past May, Microsoft reveals that it is parting ways with Panos Panay. Are these things related? He's landing at Amazon devices, which makes sense given David Limp is retiring Multiple Microsoft executives and employees have reached out privately about this Comparisons to Terry Myerson's exit Blockbuster Xbox leak Major Xbox leak reveals Xbox Series X|S mid-season upgrades, next-gen console, new controller, and more This is one of the top three Microsoft leaks that's happened in Paul's nearly 30 years of covering this company An analysis of just one of the documents in this leak reveals an incredible amount of strategy information for the next 10 years And one about the reaction to the PS5 reveal Microsoft's upcoming AI event Expectations for this event, the kick-off for Microsoft's full-on client AI push Sub-analysis: This could be Microsoft CTO Kevin Scott's "Ray Ozzie moment." There's a profile of this mostly unknown in the WSJ Intel announces NPU-powered Core Ultra CPUs - off schedule? Related to the MSFT event? Bing Chat gains two new mobile integrations Paint keeps getting AI features. Remember when Paint was the laughing stock of Windows 11 apps? Windows No new builds (of substance, there was an RP build). Windows Photos, Snipping Tool, and Phone Link are all getting new features in Insider. Is Microsoft finally taking its in-box apps seriously (again)? Google extends the support lifecycle for ChromeOS, solving the single biggest criticism of this platform. This is problematic for Microsoft Microsoft 365 EU will reject Microsoft's offer to unbundle Teams from Microsoft 365 Surface Surface Laptop Studio 2 and Laptop Go 3 leak. They will likely be announced at the special event Xbox Microsoft announces more Xbox Game Pass titles for September Bethesda's The Elder Scrolls VI will be an Xbox exclusive. Tips and picks Tip of the week: Consolidate and organize your online accounts App pick of the week: Google Takeout Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to this show at https://twit.tv/shows/windows-weekly Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Sponsors: nureva.com/twit Miro.com/podcast
It's a BIG news episode this week, as Leo Laporte and Paul Thurrott discuss the sudden departure of Panos Panay from Microsoft to Amazon. Did he jump or get pushed out of the company? Leo and Paul also dive into a massive leaked memo about Microsoft's Xbox roadmap and plans over the next 10 years. Other topics include Microsoft's upcoming event focused on AI integration across products like Windows, Office 365, and Surface, as well as online account consolidation and Google's Takeout service. We're down a Panay After his curiously off-kilter performance at Build 2023 this past May, Microsoft reveals that it is parting ways with Panos Panay. Are these things related? He's landing at Amazon devices, which makes sense given David Limp is retiring Multiple Microsoft executives and employees have reached out privately about this Comparisons to Terry Myerson's exit Blockbuster Xbox leak Major Xbox leak reveals Xbox Series X|S mid-season upgrades, next-gen console, new controller, and more This is one of the top three Microsoft leaks that's happened in Paul's nearly 30 years of covering this company An analysis of just one of the documents in this leak reveals an incredible amount of strategy information for the next 10 years And one about the reaction to the PS5 reveal Microsoft's upcoming AI event Expectations for this event, the kick-off for Microsoft's full-on client AI push Sub-analysis: This could be Microsoft CTO Kevin Scott's "Ray Ozzie moment." There's a profile of this mostly unknown in the WSJ Intel announces NPU-powered Core Ultra CPUs - off schedule? Related to the MSFT event? Bing Chat gains two new mobile integrations Paint keeps getting AI features. Remember when Paint was the laughing stock of Windows 11 apps? Windows No new builds (of substance, there was an RP build). Windows Photos, Snipping Tool, and Phone Link are all getting new features in Insider. Is Microsoft finally taking its in-box apps seriously (again)? Google extends the support lifecycle for ChromeOS, solving the single biggest criticism of this platform. This is problematic for Microsoft Microsoft 365 EU will reject Microsoft's offer to unbundle Teams from Microsoft 365 Surface Surface Laptop Studio 2 and Laptop Go 3 leak. They will likely be announced at the special event Xbox Microsoft announces more Xbox Game Pass titles for September Bethesda's The Elder Scrolls VI will be an Xbox exclusive. Tips and picks Tip of the week: Consolidate and organize your online accounts App pick of the week: Google Takeout Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to this show at https://twit.tv/shows/windows-weekly Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Sponsors: nureva.com/twit Miro.com/podcast
It's a BIG news episode this week, as Leo Laporte and Paul Thurrott discuss the sudden departure of Panos Panay from Microsoft to Amazon. Did he jump or get pushed out of the company? Leo and Paul also dive into a massive leaked memo about Microsoft's Xbox roadmap and plans over the next 10 years. Other topics include Microsoft's upcoming event focused on AI integration across products like Windows, Office 365, and Surface, as well as online account consolidation and Google's Takeout service. We're down a Panay After his curiously off-kilter performance at Build 2023 this past May, Microsoft reveals that it is parting ways with Panos Panay. Are these things related? He's landing at Amazon devices, which makes sense given David Limp is retiring Multiple Microsoft executives and employees have reached out privately about this Comparisons to Terry Myerson's exit Blockbuster Xbox leak Major Xbox leak reveals Xbox Series X|S mid-season upgrades, next-gen console, new controller, and more This is one of the top three Microsoft leaks that's happened in Paul's nearly 30 years of covering this company An analysis of just one of the documents in this leak reveals an incredible amount of strategy information for the next 10 years And one about the reaction to the PS5 reveal Microsoft's upcoming AI event Expectations for this event, the kick-off for Microsoft's full-on client AI push Sub-analysis: This could be Microsoft CTO Kevin Scott's "Ray Ozzie moment." There's a profile of this mostly unknown in the WSJ Intel announces NPU-powered Core Ultra CPUs - off schedule? Related to the MSFT event? Bing Chat gains two new mobile integrations Paint keeps getting AI features. Remember when Paint was the laughing stock of Windows 11 apps? Windows No new builds (of substance, there was an RP build). Windows Photos, Snipping Tool, and Phone Link are all getting new features in Insider. Is Microsoft finally taking its in-box apps seriously (again)? Google extends the support lifecycle for ChromeOS, solving the single biggest criticism of this platform. This is problematic for Microsoft Microsoft 365 EU will reject Microsoft's offer to unbundle Teams from Microsoft 365 Surface Surface Laptop Studio 2 and Laptop Go 3 leak. They will likely be announced at the special event Xbox Microsoft announces more Xbox Game Pass titles for September Bethesda's The Elder Scrolls VI will be an Xbox exclusive. Tips and picks Tip of the week: Consolidate and organize your online accounts App pick of the week: Google Takeout Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to this show at https://twit.tv/shows/windows-weekly Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Sponsors: nureva.com/twit Miro.com/podcast
Nelumbo nucifera, or the sacred lotus, is a plant that grows in flood plains, rivers, and deltas. Their seeds can remain dormant for years and when floods come along, blossom into a colony of plants and flowers. Some of the oldest seeds can be found in China, where they're known to represent longevity. No surprise, given their level of nitrition and connection to the waters that irrigated crops by then. They also grow in far away lands, all the way to India and out to Australia. The flower is sacred in Hinduism and Buddhism, and further back in ancient Egypt. Padmasana is a Sanskrit term meaning lotus, or Padma, and Asana, or posture. The Pashupati seal from the Indus Valley civilization shows a diety in what's widely considered the first documented yoga pose, from around 2,500 BCE. 2,700 years later (give or take a century), the Hindu author and mystic Patanjali wrote a work referred to as the Yoga Sutras. Here he outlined the original asanas, or sitting yoga poses. The Rig Veda, from around 1,500 BCE, is the oldest currently known Vedic text. It is also the first to use the word “yoga”. It describes songs, rituals, and mantras the Brahmans of the day used - as well as the Padma. Further Vedic texts explore how the lotus grew out of Lord Vishnu with Brahma in the center. He created the Universe out of lotus petals. Lakshmi went on to grow out of a lotus from Vishnu as well. It was only natural that humans would attempt to align their own meditation practices with the beautiful meditatios of the lotus. By the 300s, art and coins showed people in the lotus position. It was described in texts that survive from the 8th century. Over the centuries contradictions in texts were clarified in a period known as Classical Yoga, then Tantra and and Hatha Yoga were developed and codified in the Post-Classical Yoga age, and as empires grew and India became a part of the British empire, Yoga began to travel to the west in the late 1800s. By 1893, Swami Vivekananda gave lectures at the Parliament of Religions in Chicago. More practicioners meant more systems of yoga. Yogendra brought asanas to the United States in 1919, as more Indians migrated to the United States. Babaji's kriya yoga arrived in Boston in 1920. Then, as we've discussed in previous episodes, the United States tightened immigration in the 1920s and people had to go to India to get more training. Theos Bernard's Hatha Yoga: The Report of a Personal Experience brought some of that knowledge home when he came back in 1947. Indra Devi opened a yoga studio in Hollywood and wrote books for housewives. She brought a whole system, or branch home. Walt and Magana Baptiste opened a studio in San Francisco. Swamis began to come to the US and more schools were opened. Richard Hittleman began to teach yoga in New York and began to teach on television in 1961. He was one of the first to seperate the religious aspect from the health benefits. By 1965, the immigration quotas were removed and a wave of teachers came to the US to teach yoga. The Beatles went to India in 1966 and 1968, and for many Transcendental Meditation took root, which has now grown to over a thousand training centers and over 40,000 teachers. Swamis opened meditation centers, institutes, started magazines, and even magazines. Yoga became so big that Rupert Holmes even poked fun of it in his song “Escape (The Piña Colada Song)” in 1979. Yoga had become part of the counter-culture, and the generation that followed represented a backlash of sorts. A common theme of the rise of personal computers is that the early pioneers were a part of that counter-culture. Mitch Kapor graduated high school in 1967, just in time to be one of the best examples of that. Kapor built his own calculator in as a kid before going to camp to get his first exposure to programming on a Bendix. His high school got one of the 1620 IBM minicomputers and he got the bug. He went off to Yale at 16 and learned to program in APL and then found Computer Lib by Ted Nelson and learned BASIC. Then he discovered the Apple II. Kapor did some programming for $5 per hour as a consultant, started the first east coast Apple User Group, and did some work around town. There are generations of people who did and do this kind of consulting, although now the rates are far higher. He met a grad student through the user group named Eric Rosenfeld who was working on his dissertation and needed some help programming, so Kapor wrote a little tool that took the idea of statistical analysis from the Time Shared Reactive Online Library, or TROLL, and ported it to the microcomputer, which he called Tiny Troll. Then he enrolled in the MBA program at MIT. He got a chance to see VisiCalc and meet Bob Frankston and Dan Bricklin, who introduced him to the team at Personal Software. Personal Software was founded by Dan Fylstra and Peter Jennings when they published Microchips for the KIM-1 computer. That led to ports for the 1977 Trinity of the Commodore PET, Apple II, and TRS-80 and by then they had taken Bricklin and Franston's VisiCalc to market. VisiCalc was the killer app for those early PCs and helped make the Apple II successful. Personal Software brought Kapor on, as well as Bill Coleman of BEA Systems and Electronic Arts cofounder Rich Mellon. Today, software developers get around 70 percent royalties to publish software on app stores but at the time, fees were closer to 8 percent, a model pulled from book royalties. Much of the rest went to production of the box and disks, the sales and marketing, and support. Kapor was to write a product that could work with VisiCalc. By then Rosenfeld was off to the world of corporate finance so Kapor moved to Silicon Valley, learned how to run a startup, moved back east in 1979, and released VisiPlot and VisiTrend in 1981. He made over half a million dollars in the first six months in royalties. By then, he bought out Rosenfeld's shares in what he was doing, hired Jonathan Sachs, who had been at MIT earlier, where he wrote the STOIC programming language, and then went to work at Data General. Sachs worked on spreadsheet ideas at Data General with a manager there, John Henderson, but after they left Data General, and the partnership fell apart, he worked with Kapor instead. They knew that for software to be fast, it needed to be written in a lower level language, so they picked the Intel 8088 assembly language given that C wasn't fast enough yet. The IBM PC came in 1981 and everything changed. Mitch Kapor and Jonathan Sachs started Lotus in 1982. Sachs got to work on what would become Lotus 1-2-3. Kapor turned out to be a great marketer and product manager. He listened to what customers said in focus groups. He pushed to make things simpler and use less jargon. They released a new spreadsheet tool in 1983 and it worked flawlessly on the IBM PC and while Microsoft had Multiplan and VisCalc was the incumbent spreadsheet program, Lotus quickly took market share from then and SuperCalc. Conceptually it looked similar to VisiCalc. They used the letter A for the first column, B for the second, etc. That has now become a standard in spreadsheets. They used the number 1 for the first row, the number 2 for the second. That too is now a standard. They added a split screen, also now a standard. They added macros, with branching if-then logic. They added different video modes, which could give color and bitmapping. They added an underlined letter so users could pull up a menu and quickly select the item they wanted once they had those orders memorized, now a standard in most menuing systems. They added the ability to add bar charts, pie charts, and line charts. One could even spread their sheet across multiple monitors like in a magazine. They refined how fields are calculated and took advantage of the larger amounts of memory to make Lotus far faster than anything else on the market. They went to Comdex towards the end of the year and introduced Lotus 1-2-3 to the world. The software could be used as a spreadsheet, but the 2 and 3 referred to graphics and database management. They did $900,000 in orders there before they went home. They couldn't even keep up with the duplication of disks. Comdex was still invitation only. It became so popular that it was used to test for IBM compatibility by clone makers and where VisiCalc became the app that helped propel the Apple II to success, Lotus 1-2-3 became the app that helped propel the IBM PC to success. Lotus was rewarded with $53 million in sales for 1983 and $156 million in 1984. Mitch Kapor found himself. They quickly scaled from less than 20 to 750 employees. They brought in Freada Klein who got her PhD to be the Head of Employee Relations and charged her with making them the most progressive employer around. After her success at Lotus, she left to start her own company and later married. Sachs left the company in 1985 and moved on to focus solely on graphics software. He still responds to requests on the phpBB forum at dl-c.com. They ran TV commercials. They released a suite of Mac apps they called Lotus Jazz. More television commercials. Jazz didn't go anywhere and only sold 20,000 copies. Meanwhile, Microsoft released Excel for the Mac, which sold ten times as many. Some blamed the lack os sales on the stringent copy protection. Others blamed the lack of memory to do cool stuff. Others blamed the high price. It was the first major setback for the young company. After a meteoric rise, Kapor left the company in 1986, at about the height of their success. He replaced himself with Jim Manzi. Manzi pushed the company into network applications. These would become the center of the market but were just catching on and didn't prove to be a profitable venture just yet. A defensive posture rather than expanding into an adjacent market would have made sense, at least if anyone knew how aggressive Microsoft was about to get it would have. Manzi was far more concerned about the millions of illegal copies of the software in the market than innovation though. As we turned the page to the 1990s, Lotus had moved to a product built in C and introduced the ability to use graphical components in the software but not wouldn't be ported to the new Windows operating system until 1991 for Windows 3. By then there were plenty of competitors, including Quattro Pro and while Microsoft Excel began on the Mac, it had been a showcase of cool new features a windowing operating system could provide an application since released for Windows in 1987. Especially what they called 3d charts and tabbed spreadsheets. There was no catching up to Microsoft by then and sales steadily declined. By then, Lotus released Lotus Agenda, an information manager that could be used for time management, project management, and as a database. Kapor was a great product manager so it stands to reason he would build a great product to manage products. Agenda never found commercial success though, so was later open sourced under a GPL license. Bill Gross wrote Magellan there before he left to found GoTo.com, which was renamed to Overture and pioneered the idea of paid search advertising, which was acquired by Yahoo!. Magellan cataloged the internal drive and so became a search engine for that. It sold half a million copies and should have been profitable but was cancelled in 1990. They also released a word processor called Manuscript in 1986, which never gained traction and that was cancelled in 1989, just when a suite of office automation apps needed to be more cohesive. Ray Ozzie had been hired at Software Arts to work on VisiCalc and then helped Lotus get Symphony out the door. Symphony shipped in 1984 and expanded from a spreadsheet to add on text with the DOC word processor, and charts with the GRAPH graphics program, FORM for a table management solution, and COM for communications. Ozzie dutifully shipped what he was hired to work on but had a deal that he could build a company when they were done that would design software that Lotus would then sell. A match made in heaven as Ozzie worked on PLATO and borrowed the ideas of PLATO Notes, a collaboration tool developed at the University of Illinois Champagne-Urbana to build what he called Lotus Notes. PLATO was more more than productivity. It was a community that spanned decades and Control Data Corporation had failed to take it to the mass corporate market. Ozzie took the best parts for a company and built it in isolation from the rest of Lotus. They finally released it as Lotus Notes in 1989. It was a huge success and Lotus bought Iris in 1994. Yet they never found commercial success with other socket-based client server programs and IBM acquired Lotus in 1995. That product is now known as Domino, the name of the Notes 4 server, released in 1996. Ozzie went on to build a company called Groove Networks, which was acquired by Microsoft, who appointed him one of their Chief Technology Officers. When Bill Gates left Microsoft, Ozzie took the position of Chief Software Architect he vacated. He and Dave Cutler went on to work on a project called Red Dog, which evolved into what we now know as Microsoft Azure. Few would have guessed that Ozzie and Kapor's handshake agreement on Notes could have become a real product. Not only could people not understand the concept of collaboration and productivity on a network in the late 1980s but the type of deal hadn't been done. But Kapor by then realized that larger companies had a hard time shipping net-new software properly. Sometimes those projects are best done in isolation. And all the better if the parties involved are financially motivated with shares like Kapor wanted in Personal Software in the 1970s before he wrote Lotus 1-2-3. VisiCalc had sold about a million copies but that would cease production the same year Excel was released. Lotus hung on longer than most who competed with Microsoft on any beachhead they blitzkrieged. Microsoft released Exchange Server in 1996 and Notes had a few good years before Exchange moved in to become the standard in that market. Excel began on the Mac but took the market from Lotus eventually, after Charles Simonyi stepped in to help make the product great. Along the way, the Lotus ecosystem created other companies, just as they were born in the Visi ecosystem. Symantec became what we now call a “portfolio” company in 1985 when they introduced NoteIt, a natural language processing tool used to annotate docs in Lotus 1-2-3. But Bill Gates mentioned Lotus by name multiple times as a competitor in his Internet Tidal Wave memo in 1995. He mentioned specific features, like how they could do secure internet browsing and that they had a web publisher tool - Microsoft's own FrontPage was released in 1995 as well. He mentioned an internet directory project with Novell and AT&T. Active Directory was released a few years later in 1999, after Jim Allchin had come in to help shepherd LAN Manager. Notes itself survived into the modern era, but by 2004 Blackberry released their Exchange connector before they released the Lotus Domino connector. That's never a good sign. Some of the history of Lotus is covered in Scott Rosenberg's 2008 book, Dreaming in Code. Others are documented here and there in other places. Still others are lost to time. Kapor went on to invest in UUNET, which became a huge early internet service provider. He invested in Real Networks, who launched the first streaming media service on the Internet. He invested in the creators of Second Life. He never seemed vindictive with Microsoft but after AOL acquired Netscape and Microsoft won the first browser war, he became the founding chair of the Mozilla Foundation and so helped bring Firefox to market. By 2006, Firefox took 10 percent of the market and went on to be a dominant force in browsers. Kapor has also sat on boards and acted as an angel investor for startups ever since leaving the company he founded. He also flew to Wyoming in 1990 after he read a post on The WELL from John Perry Barlow. Barlow was one of the great thinkers of the early Internet. They worked with Sun Microsystems and GNU Debugging Cypherpunk John Gilmore to found the Electronic Frontier Foundation, or EFF. The EFF has since been the nonprofit who leads the fight for “digital privacy, free speech, and innovation.” So not everything is about business.
Mary Jo Foley moves on! No, not entirely from Microsoft - away from writing several times a week about the latest enterprise topics. MJ is going to Directions on Microsoft, where she will spend more time on analysis but still communicate it to the world. The conversation digs into current subjects like the state of the cloud and Microsoft's focus on building industry-specific cloud implementations, and precisely what is happening with Windows these days? Lots of great thinking from MJ, as usual, and a hint of what's to come!Links:Mary Jo Foley Leaves ZDNetDirections on MicrosoftMicrosoft Industry CloudWindows 10 22H2Windows Server 2022Azure ArcGoogle AnthosRecorded November 11, 2022
Ray Ozzie is the founder and CEO of Blues Wireless, a cellular IoT company that makes it easier to send data to the cloud. Ray joins Chris to talk about his background in the software industry, including a role as Chief Software Architect of Microsoft.
With over 2.6 billion active users ad 4.6billion active accounts email has become a significant means of communication in the business, professional, academic, and personal worlds. Before email we had protocols that enabled us to send messages within small splinters of networks. Time Sharing systems like PLATO at the University of Champaign-Urbana, DTSS at Dartmouth College, BerkNet at the University of California Berkeley, and CTTS at MIT pioneered electronic communication. Private corporations like IBM launched VNET We could create files or send messages that were immediately transferred to other people. The universities that were experimenting with these messaging systems even used some of the words we use today. MIT's CTSS used the MAIL program to send messages. Glenda Schroeder from there documented that messages would be placed into a MAIL BOX in 1965. She had already been instrumental in implementing the MULTICS shell that would later evolve into the Unix shell. Users dialed into the IBM 7094 mainframe and communicated within that walled garden with other users of the system. That was made possible after Tom Van Vleck and Noel Morris picked up her documentation and turned it into reality, writing the program in MAD or the Michigan Algorithm Decoder. But each system was different and mail didn't flow between them. One issue was headers. These are the parts of a message that show what time the message was sent, who sent the message, a subject line, etc. Every team had different formats and requirements. The first attempt to formalize headers was made in RFC 561 by Abhay Bhutan and Ken Pogran from MIT, Jim White at Stanford, and Ray Tomlinson. Tomlinson was a programmer at Bolt Beranek and Newman. He defined the basic structure we use for email while working on a government-funded project at ARPANET (Advanced Research Projects Agency Network) in 1971. While there, he wrote a tool called CYPNET to send various objects over a network, then ported that into the SNDMSG program used to send messages between users of their TENEX system so people could send messages to other computers. The structure he chose was Username@Computername because it just made sense to send a message to a user on the computer that user was at. We still use that structure today, although the hostname transitioned to a fully qualified domain name a bit later. Given that he wanted to route messages between multiple computers, he had a keen interest in making sure other computers could interpret messages once received. The concept of instantaneous communication between computer scientists led to huge productivity gains and new, innovative ideas. People could reach out to others they had never met and get quick responses. No more walking to the other side of a college campus. Some even communicated primarily through the computers, taking terminals with them when they went on the road. Email was really the first killer app on the networks that would some day become the Internet. People quickly embraced this new technology. By 1975 almost 75% of the ARPANET traffic was electronic mails, which provided the idea to send these electronic mails to users on other computers and networks. Most universities that were getting mail only had one or two computers connected to ARPANET. Terminals were spread around campuses and even smaller microcomputers in places. This was before the DNS (Domain Name Service), so the name of the computer was still just a hostname from the hosts file and users needed to know which computer and what the correct username was to send mail to one another. Elizabeth “Jake” Feinler had been maintaining a hosts file to keep track of computers on the growing network when her employer Stanford was just starting the NIC, or Network Information Center. Once the Internet was formed that NIC would be the foundation or the InterNIC who managed the buying and selling of domain names once Paul Mockapetris formalized DNS in 1983. At this point, the number of computers was increasing and not all accepted mail on behalf of an organization. The Internet Service Providers (ISPs) began to connect people across the world to the Internet during the 1980s and for many people, electronic mail was the first practical application they used on the internet. This was made easier by the fact that the research community had already struggled with email standards and in 1981 had defined how servers sent mail to one another using the Simple Mail Transfer Protocol, or SMTP, in RFC 788, updated in 1982 with 821 and 822. Still, the computers at networks like CSNET received email and users dialed into those computers to read the email they stored. Remembering the name of the computer to send mail to was still difficult. By 1986 we also got the concept routing mail in RFC 974 from Craig Partridge. Here we got the first MX record. Those are DNS records that define the computer that received mail for a given domain name. So stanford.edu had a single computer that accepted mail for the university. These became known as mail servers. As the use of mail grew and reliance on mail increased, some had multiple mail servers for fault tolerance, for different departments, or to split the load between servers. We also saw some split various messaging roles up. A mail transfer agent, or MTA, sent mail between different servers. The received field in the header is stamped with the time the server acting as the MTA got an email. MTAs mostly used port 25 to transfer mail until SSL was introduced when port 587 started to be used for encrypted connections. Bandwidth and time on these computers was expensive. There was a cost to make a phone call to dial into a mail provider and providers often charged by the minute. So people also wanted to store their mail offline and then dial in to send messages and receive messages. Close enough to instant communication. So software was created to manage email storage, which we call a mail client or more formally a Mail User Agent, or MUA. This would be programs like Microsoft Outlook and Apple Mail today or even a web mail client as with Gmail. POP, or Post Office Protocol was written to facilitate that transaction in 1984. Receive mail over POP and send over SMTP. POP evolved over the years with POPv3 coming along in 1993. At this point we just needed a username and the domain name to send someone a message. But the number of messages was exploding. As were the needs. Let's say a user needed to get their email on two different computers. POP mail needed to know to leave a copy of messages on servers. But then those messages all showed up as new on the next computer. So Mark Crispin developed IMAP, or Internet Message Access Protocol, in 1986, which left messages on the server and by IMAPv4 in the 1990s, was updated to the IMAPv4 we use today. Now mail clients had a few different options to use when accessing mail. Those previous RFCs focused on mail itself and the community could use tools like FTP to get files. But over time we also wanted to add attachments to emails so MIME, or Multipurpose Internet Mail Extensions became a standard with RFC 1341 in 1993. Those mail and RFC standards would evolve over the years to add better support for encapsulations and internationalization. With the more widespread use of electronic mail, the words were shortened and to email and became common in everyday conversations. With the necessary standards, the next few years saw a number of private vendors jump on the internet bandwagon and invest in providing mail to customers America Online added email in 1993, Echomail came along in 1994, Hotmail added advertisements to messages, launching in 1996, and Yahoo added mail in 1997. All of the portals added mail within a few years. The age of email kicked into high gear in the late 1990s, reaching 55 million users in 1997 and 400 million by 1999. During this time having an email address went from a luxury or curiosity to a societal and business expectation, like having a phone might be today. We also started to rely on digital contacts and calendars, and companies like HP released Personal Information Managers, or PIMs. Some companies wanted to sync those the same way they did email, so Microsoft Exchange was launched in 1996. That original concept went all the way back to PLATO in the 1960s with Dave Wooley's PLATO NOTES and was Ray Ozzie's inspiration when he wrote the commercial product that became Lotus Notes in 1989. Microsoft inspired Google who in turn inspired Microsoft to take Exchange to the cloud with Outlook.com. It hadn't taken long after the concept of sending mail between computers was possible that we got spam. Then spam blockers and other technology to allow us to stay productive despite the thousands of messages from vendors desperately trying to sell us their goods through drip campaigns. We've even had legislation to limit the amount of spam, given that at one point over 9 out of 10 emails was spam. Diligent efforts have driven that number down to just shy of a third at this point. Email is now well over 40 years old and pretty much ubiquitous around the world. We've had other tools for instant messaging, messaging within every popular app, and group messaging products like bulletin boards online and now group instant messaging products like Slack and Microsoft Teams. We even have various forms of communication options integrated with one another. Like the ability to initiate a video call within Slack or Teams. Or the ability to toggle the Teams option when we send an invitation for a meeting in Outlook. Every few years there's a new communication medium that some think will replace email. And yet email is as critical to our workflows today as it ever was.
Josh and Kurt talk about a Home Depot plan to put DRM on power tools. Anyone can add a computer to anything for a few dollars now. How secure is any of this. What does it mean when the things we buy start to acquire DRM? There are a lot of new questions we don't have any real answers for. Show Notes Home Depot power tools Ray Ozzie's IoT board First-sale doctrine
PLATO (Programmed Logic for Automatic Teaching Operations) was an educational computer system that began at the University of Illinois Champaign Urbana in 1960 and ran into the 2010s in various flavors. Wait, that's an oversimplification. PLATO seemed to develop on an island in the corn fields of Champaign Illinois, and sometimes precedes, sometimes symbolizes, and sometimes fast-follows what was happening in computing around the world in those decades. To put this in perspective - PLATO began on ILLIAC in 1960 - a large classic vacuum tube mainframe. Short for the Illinois Automatic Computer, ILLIAC was built in 1952, around 7 years after ENIAC was first put into production. As with many early mainframe projects PLATO 1 began in response to a military need. We were looking for new ways to educate the masses of veterans using the GI Bill. We had to stretch the reach of college campuses beyond their existing infrastructures. Computerized testing started with mechanical computing, got digitized with the introduction of Scantron by IBM in 1935, and a number of researchers were looking to improve the consistency of education and bring in new technology to help with quality teaching at scale. The post-World War II boom did this for industry as well. Problem is, following the launch of Sputnik by the USSR in 1957, many felt the US began lagging behind in education. So grant money to explore solutions flowed and CERL was able to capitalize on grants from the US Army, Navy, and Air Force. By 1959, physicists at Illinois began thinking of using that big ILLIAC machine they had access to. Daniel Alpert recruited Don Bitzer to run a project, after false starts with educators around the campus. Bitzer shipped the first instance of PLATO 1 in 1960. They used a television to show images, stored images in Raytheon tubes, and a make-shift keyboard designed for PLATO so users could provide input in interactive menus and navigate. They experimented with slide projectors when they realized the tubes weren't all that reliable and figured out how to do rudimentary time sharing, expanding to a second concurrent terminal with the release of PLATO II in 1961. Bitzer was a classic Midwestern tinkerer. He solicited help from local clubs, faculty, high school students, and wherever he could cut a corner to build more cool stuff, he was happy to move money and resources to other important parts of the system. This was the age of hackers and they hacked away. He inspired but also allowed people to follow their own passions. Innovation must be decentralized to succeed. They created an organization to support PLATO in 1966 - as part of the Graduate College. CERL stands for the Computer-Based Education Research Laboratory (CERL). Based on early successes, they got more and more funding at CERL. Now that we were beyond a 1:1 ratio of users to computers and officially into Time Sharing - it was time for Plato III. There were a number of enhancements in PLATO III. For starters, the system was moved to a CDC 1604 that CEO of Control Data William Norris donated to the cause - and expanded to allow for 20 terminals. But it was complicated to create new content and the team realized that content would be what drove adoption. This was true with applications during the personal computer revolution and then apps in the era of the App Store as well. One of many lessons learned first on PLATO. Content was in the form of applications that they referred to as lessons. It was a teaching environment, after all. They emulated the ILLIAC for existing content but needed more. People were compiling applications in a complicated language. Professors had day jobs and needed a simpler way to build content. So Paul Tenczar on the team came up with a language specifically tailored to creating lessons. Similar in some ways to BASIC, it was called TUTOR. Tenczar released the manual for TUTOR in 1969 and with an easier way of getting content out, there was an explosion in new lessons, and new features and ideas would flourish. We would see simulations, games, and courseware that would lead to a revolution in ideas. In a revolutionary time. The number of hours logged by students and course authors steadily increased. The team became ever more ambitious. And they met that ambition with lots of impressive achievements. Now that they were comfortable with the CDC 1604 they new that the new content needed more firepower. CERL negotiated a contract with Control Data Corporation (CDC) in 1970 to provide equipment and financial support for PLATO. Here they ended up with a CDC Cyber 6400 mainframe, which became the foundation of the next iteration of PLATO, PLATO IV. PLATO IV was a huge leap forward on many levels. They had TUTOR but with more resources could produce even more interactive content and capabilities. The terminals were expensive and not so scalable. So in preparation for potentially thousands of terminals in PLATO IV they decided to develop their own. This might seem a bit space age for the early 1970s, but what they developed was a touch flat panel plasma display. It was 512x512 and rendered 60 lines per second at 1260 baud. The plasma had memory in it, which was made possible by the fact that they weren't converting digital signals to analog, as is done on CRTs. Instead, it was a fully digital experience. The flat panel used infrared to see where a user was touching, allowing users some of their first exposure to touch screens. This was a grid of 16 by 16 rather than 512 but that was more than enough to take them over the next decade. The system could render basic bitmaps but some lessons needed more rich, what we might call today, multimedia. The Raytheon tubes used in previous systems proved to be more of a CRT technology but also had plenty of drawbacks. So for newer machines they also included a microfiche machine that produced images onto the back of the screen. The terminals were a leap forward. There were other programs going on at about the same time during the innovative bursts of PLATO, like the Dartmouth Time Sharing System, or DTSS, project that gave us BASIC instead of TUTOR. Some of these systems also had rudimentary forms of forums, such as EIES and the emerging BBS Usenet culture that began in 1973. But PLATO represented a unique look into the splintered networks of the Time Sharing age. Combined with the innovative lessons and newfound collaborative capabilities the PLATO team was about to bring about something special. Or lots of somethings that culminated in more. One of those was Notes. Talkomatic was created by Doug Brown and David R. Woolley in 1973. Tenczar asked the 17-year old Woolley to write a tool that would allow users to report bugs with the system. There was a notes file that people could just delete. So they added the ability for a user to automatically get tagged in another file when updating and store notes. He expanded it to allow for 63 responses per note and when opened, it showed the most recent notes. People came up with other features and so a menu was driven, providing access to System Announcements, Help Notes, and General Notes. But the notes were just the start. In 1973, seeing the need for even more ways to communicate with other people using the system, Doug Brown wrote a prototype for Talkomatic. Talkomatic was a chat program that showed when people were typing. Woolley helped Brown and they added channels with up to five people per channel. Others could watch the chat as well. It would be expanded and officially supported as a tool called Term-Talk. That was entered by using the TERM key on a console, which allowed for a conversation between two people. You could TERM, or chat a person, and then they could respond or mark themselves as busy. Because the people writing this stuff were also the ones supporting users, they added another feature, the ability to monitor another user, or view their screen. And so programmers, or consultants, could respond to help requests and help get even more lessons going. And some at PLATO were using ARPANET, so it was only a matter of time before word of Ray Tomlinson's work on electronic mail leaked over, leading to the 1974 addition of personal notes, a way to send private mail engineered by Kim Mast. As PLATO grew, the amount of content exploded. They added categories to Notes in 1975 which led to Group Notes in 1976, and comments and linked notes and the ability to control access. But one of the most important innovations PLATO will be remembered for is games. Anyone that has played an educational game will note that school lessons and games aren't always all that different. Since Rick Blomme had ported Spacewar! to PLATO in 1969 and added a two-player option, multi-player games had been on the rise. They made leader boards for games like Dogfight so players could get early forms of game rankings. Games like airtight and airace and Galactic Attack would follow those. MUDs were another form of games that came to PLATO. Collosal Cave Adventure had come in 1975 for the PDP, so again these things were happening in a vacuum but where there were influences and where innovations were deterministic and found in isolation is hard to say. But the crawlers exploded on PLATO. We got Moria, Oubliette by Jim Schwaiger, Pedit5, crypt, dungeon, avatar, and drygulch. We saw the rise of intense storytelling, different game mechanics that were mostly inspired by Dungeons and Dragons, As PLATO terminals found their way in high schools and other universities, the amount of games and amount of time spent on those games exploded, with estimates of 20% of time on PLATO being spent playing games. PLATO IV would grow to support thousands of terminals around the world in the 1970s. It was a utility. Schools (and even some parents) leased lines back to Champagne Urbana and many in computing thought that these timesharing systems would become the basis for a utility model in computing, similar to the cloud model we have today. But we had to go into the era of the microcomputer to boomerang back to timesharing first. That microcomputer revolution would catch many, who didn't see the correlation between Moore's Law and the growing number of factories and standardization that would lead to microcomputers, off guard. Control Data had bet big on the mainframe market - and PLATO. CDC would sell mainframes to other schools to host their own PLATO instance. This is where it went from a timesharing system to a network of computers that did timesharing. Like a star topology. Control Data looked to PLATO as one form of what the future of the company would be. Here, he saw this mainframe with thousands of connections as a way to lease time on the computers. CDC took PLATO to market as CDC Plato. Here, schools and companies alike could benefit from distance education. And for awhile it seemed to be working. Financial companies and airlines bought systems and the commercialization was on the rise, with over a hundred PLATO systems in use as we made our way to the middle of the 1980s. Even government agencies like the Depart of Defense used them for training. But this just happened to coincide with the advent of the microcomputer. CDC made their own terminals that were often built with the same components that would be found in microcomputers but failed to capitalize on that market. Corporations didn't embrace the collaboration features and often had these turned off. Social computing would move to bulletin boards And CDC would release versions of PLATO as micro-PLATO for the TRS-80, Texas Instruments TI-99, and even Atari computers. But the bureaucracy at CDC had slowed things down to the point that they couldn't capitalize on the rapidly evolving PC industry. And prices were too high in a time when home computers were just moving from a hobbyist market to the mainstream. The University of Illinois spun PLATO out into its own organization called University Communications, Inc (or UCI for short) and closed CERL in 1994. That was the same year Marc Andreessen co-founded Mosaic Communications Corporation, makers of Netscape -successor to NCSA Mosaic. Because NCSA, or The National Center for Supercomputing Applications, had also benefited from National Science Foundation grants when it was started in 1982. And all those students who flocked to the University of Illinois because of programs like PLATO had brought with them more expertise. UCI continued PLATO as NovaNet, which was acquired by National Computer Systems and then Pearson corporation, finally getting shut down in 2015 - 55 years after those original days on ILLIAC. It evolved from the vacuum tube-driven mainframe in a research institute with one terminal to two terminals, to a transistorized mainframe with hundreds and then over a thousand terminals connected from research and educational institutions around the world. It represented new ideas in programming and programming languages and inspired generations of innovations. That aftermath includes: The ideas. PLATO developers met with people from Xerox PARC starting in the 70s and inspired some of the work done at Xerox. Yes, they seemed isolated at times but they were far from it. They also cross-pollinated ideas to Control Data. One way they did this was by trading some commercialization rights for more mainframe hardware. One of the easiest connections to draw from PLATO to the modern era is how the notes files evolved. Ray Ozzie graduated from Illinois in 1979 and went to work for Data General and then Software Arts, makers of VisiCalc. The corporate world had nothing like the culture that had evolved out of the notes files in PLATO Notes. Today we take collaboration tools for granted but when Ozzie was recruited by Lotus, the makers of 1-2-3, he joined only if they agreed to him funding a project to take that collaborative spirit that still seemed stuck in the splintered PLATO network. The Internet and networked computing in companies was growing, and he knew he could improve on the notes files in a way that companies could take use of it. He started Iris Associates in 1984 and shipped a tool in 1989. That would evolve into what is would be called Lotus Notes when the company was acquired by Lotus in 1994 and then when Lotus was acquired by IBM, would evolve into Domino - surviving to today as HCL Domino. Ozzie would go on to become a CTO and then the Chief Software Architect at Microsoft, helping spearhead the Microsoft Azure project. Collaboration. Those notes files were also some of the earliest newsgroups. But they went further. Talkomatic introduced real time text chats. The very concept of a digital community and its norms and boundaries were being tested and challenges we still face like discrimination even manifesting themselves then. But it was inspiring and between stints at Microsoft, Ray Ozzie founded Talko in 2012 based on what he learned in the 70s, working with Talkomatic. That company was acquired by Microsoft and some of the features ported into Skype. Another way Microsoft benefited from the work done on PLATO was with Microsoft Flight Simulator. That was originally written by Bruce Artwick after leaving the university based on the flight games he'd played on PLATO. Mordor: The Depths of Dejenol was cloned from Avatar Silas Warner was connected to PLATO from terminals at the University of Indiana. During and after school, he wrote software for companies but wrote Robot War for PLATO and then co-founded Muse Software where he wrote Escape!, a precursor for lots of other maze runners, and then Castle Wolfenstein. The name would get bought for $5,000 after his company went bankrupt and one of the early block-buster first-person shooters when released as Wolfenstein 3D. Then John Carmack and John Romero created Doom. But Warner would go on to work with some of the best in gaming, including Sid Meier. Paul Alfille built the game Freecell for PLATO and Control Data released it for all PLATO systems. Jim Horne played it from the PLATO terminals at the University of Alberta and eventually released it for DOS in 1988. Horn went to work for Microsoft who included it in the Microsoft Entertainment Pack, making it one of the most popular software titles played on early versions of Windows. He got 10 shares of Microsoft stock in return and it's still part of Windows 10 using the Microsoft Solitaire Collection.. Robert wood head and Andrew Greenberg got onto PLATO from their terminals at Cornell University where they were able to play games like Oubliette and Emprie. They would write a game called Wizardry that took some of the best that the dungeon crawl multi-players had to offer and bring them into a single player computer then console game. I spent countless hours playing Wizardry on the Nintendo NES and have played many of the spin-offs, which came as late as 2014. Not only did the game inspire generations of developers to write dungeon games, but some of the mechanics inspired features in the Ultima series, Dragon Quest, Might and Magic, The Bard's Tale, Dragon Warrior and countless Manga. Greenberg would go on to help with Q-Bert and other games before going on to work with the IEEE. Woodhead would go on to work on other games like Star Maze. I met Woodhead shortly after he wrote Virex, an early anti-virus program for the Mac that would later become McAfee VirusScan for the Mac. Paul Tenczar was in charge of the software developers for PLATO. After that he founded Computer Teaching Corporation and introduced EnCORE, which was changed to Tencore. They grew to 56 employees by 1990 and ran until 2000. He returned to the University of Illinois to put RFID tags on bees, contributing to computing for nearly 5 decades and counting. Michael Allen used PLATO at Ohio State University before looking to create a new language. He was hired at CDC where he became a director in charge of Research and Development for education systems There, he developed the ideas for a new computer language authoring system, which became Authorware, one of the most popular authoring packages for the Mac. That would merge with Macro-Mind to become Macromedia, where bits and pieces got put into Dreamweaver and Shockwave as they released those. After Adobe acquired Macromedia, he would write a number of books and create even more e-learning software authoring tools. So PLATO gave us multi-player games, new programming languages, instant messaging, online and multiple choice testing, collaboration forums, message boards, multiple person chat rooms, early rudimentary remote screen sharing, their own brand of plasma display and all the research behind printing circuits on glass for that, and early research into touch sensitive displays. And as we've shown in just a few of the many people that contributed to computing after, they helped inspire an early generation of programmers and innovators. If you like this episode I strongly suggest checking out The Friendly Orange Glow from Brian Dear. It's a lovely work with just the right mix of dry history and flourishes of prose. A short history like this can't hold a candle to a detailed anthology like Dear's book. Another well researched telling of the story can be found in a couple of chapters of A People's History Of Computing In The United States, from Joy Rankin. She does a great job drawing a parallel (and sometimes direct line from) the Dartmouth Time Sharing System and others as early networks. And yes, terminals dialing into a mainframe and using resources over telephone and leased lines was certainly a form of bridging infrastructures and seemed like a network at the time. But no mainframe could have scaled to the ability to become a utility in the sense that all of humanity could access what was hosted on it. Instead, the ARPANET was put online and growing from 1969 to 1990 and working out the hard scientific and engineering principals behind networking protocols gave us TCP/IP. In her book, Rankin makes great points about the BASIC and TUTOR applications helping shape more of our modern world in how they inspired the future of how we used personal devices once connected to a network. The scientists behind ARPANET, then NSFnet and the Internet, did the work to connect us. You see, those dial-up connections were expensive over long distances. By 1974 there were 47 computers connected to the ARPANET and by 1983 we had TCP/IPv4.And much like Bitzer allowing games, they didn't seem to care too much how people would use the technology but wanted to build the foundation - a playground for whatever people wanted to build on top of it. So the administrative and programming team at CERL deserve a lot of credit. The people who wrote the system, the generations who built features and code only to see it become obsolete came and went - but the compounding impact of their contributions can be felt across the technology landscape today. Some of that is people rediscovering work done at CERL, some is directly inspired, and some has been lost only to probably be rediscovered in the future. One thing is for certain, their contributions to e-learning are unparalleled with any other system out there. And their technical contributions, both in the form of those patented and those that were either unpatentable or where they didn't think of patenting, are immense. Bitzer and the first high schoolers and then graduate students across the world helped to shape the digital world we live in today. More from an almost sociological aspect than technical. And the deep thought applied to the system lives on today in so many aspects of our modern world. Sometimes that's a straight line and others it's dotted or curved. Looking around, most universities have licensing offices now, to capitalize on the research done. Check out a university near you and see what they have available for license. You might be surprised. As I'm sure many in Champagne were after all those years. Just because CDC couldn't capitalize on some great research doesn't mean we can't.
This week on MoneyBall Medicine, Harry takes a field trip (literally!) into farming and agriculture. His guests are Al Eisaian co-founder and CEO of crop intelligence IntelinAir, and the company’s director of machine learning, Jennifer Hobbs. Intelinair’s AGMRI platform uses customized computer vision and deep learning algorithms to sift through terabytes of aerial image data, to help farmers identify problems like weeds or pests that can go undetected from the ground. The parallels to the digital transformation in healthcare aren't hard to spot.Harry has talked with scores of guests about advanced computer science techniques like neural networks, computer vision, and machine learning and how they’re changing the way healthcare providers can find patterns in genomic data or radiology images. But the fact is, these same techniques are being used to generate new kinds of actionable insights in many other areas, including agriculture. In fact, today’s farmers are almost overwhelmed by the volume of imaging available to them from drones, airborne cameras, and satellites. IntelinAir uses AI techniques to spot patterns and trends in these images, in a bid to help farmers address problems before they get out of hand, while making smarter use of fertilizers and pesticides.Which sounds a lot like using digital health data to keep patients healthier while making smarter use of pharmaceuticals. So don’t be surprised if ag tech companies end up having a thing or two to teach the digital health industry.You can find more details about this episode, as well as the entire run of MoneyBall Medicine's 50+ episodes, at https://glorikian.com/moneyball-medicine-podcast/Please rate and review MoneyBall Medicine on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:• Launch the “Podcasts” app on your device. If you can’t find this app, swipe all the way to the left on your home screen until you’re on the Search page. Tap the search field at the top and type in “Podcasts.” Apple’s Podcasts app should show up in the search results.• Tap the Podcasts app icon, and after it opens, tap the Search field at the top, or the little magnifying glass icon in the lower right corner.• Type MoneyBall Medicine into the search field and press the Search button.• In the search results, click on the MoneyBall Medicine logo.• On the next page, scroll down until you see the Ratings & Reviews section. Below that, you’ll see five purple stars.• Tap the stars to rate the show.• Scroll down a little farther. You’ll see a purple link saying “Write a Review.”• On the next screen, you’ll see the stars again. You can tap them to leave a rating if you haven’t already.• In the Title field, type a summary for your review.• In the Review field, type your review.• When you’re finished, click Send.• That’s it, you’re done. Thanks!TRANSCRIPTHarry Glorikian: We talk a lot on the show about advanced computer science techniques like neural networks, computer vision, and machine learning and how they’re changing the way healthcare providers can find patterns in genomic data or radiology images. But the fact is, these same techniques are being used to generate new kinds of actionable insights in many other areas. And today I thought it would be a fun exercise to take a field trip…literally!... into farming and agriculture. Just like doctors, today’s farmers are almost overwhelmed by the volume of imaging that’s now available to them. In the clinic, these images come from MRI machines and other types of scanners. On the farm, they come from drones, airborne cameras, and satellites. And in both cases, if you can use AI techniques to spot patterns and trends in the images, you’re then in a position to address problems before they get out of hand.We’re about to meet two executives from IntelinAir, an ag-tech startup that offers a so-called “crop intelligence platform” called AGMRI. It consists of customized computer vision and deep learning algorithms that sift through terabytes of aerial imaging to help farmers identify problems that can be hard to spot from the ground. We’re talking about things like weed infestations, nutrient or water deficiencies, weather damage, insect damage, fungal damage, and poor tillage or drainage patterns. The company flies over client’s fields up to 13 times per season, which means they can provide a picture of the evolving health of the crops in those fields. Ultimately the goal is to help farmers increase yields while making smarter use of fertilizers and pesticides. Which sounds a lot like keeping patients healthier while making smarter use of pharmaceuticals. But as we’ll hear, the flood of new data that’s available to farmers is even bigger in some ways than that available to doctors. So it won’t be surprising to me if ag tech companies end up having a thing or two to teach the digital health industry. So without further ado, let’s meet IntelinAir’s co-founder and CEO Al Eisaian, and its director of machine learning, Jennifer Hobbs.Harry Glorikian: Al, Jennifer, welcome to the show.Al Eisaian: Hey, thanks for having us.Harry Glorikian: No, it's great to have you guys on the show. And I know that I'm sort of slightly stepping out of the bounds of what is, what would be looked at is as traditional health care. But I thought this episode would be really interesting to go into from from two sides. One is, obviously, you guys are in the agricultural space and agriculture is, as far as I'm concerned, paramount to health. As a matter of fact, it's probably a better way to keep people healthy, if they just eat better. And the other side of it is the image analytics I've always looked at, the technology doesn't necessarily care what it ingests. It has the ability to see all sorts of features, whether that's a crop or an insect or an image on a radiology scan or, you know, a pathology slide. There's all the... I think the technology can blur where it is and how it's applied. But before we get started with that is, Al, tell us the origin story. How did this how did IntelinAir get started. How did you end up doing this?Al Eisaian: Sure. It's an interesting story because I was invited to talk to a lot of PhDs and graduate students about entrepreneurship back in 2014. So I was invited to go to UIUC and give a talk. And so I did. And as you know... I landed in Chicago and then you drive through three hours of corn and soybean fields. And it was interesting. And I was like, I didn't think much of it. But during the few days that I was at UIUC, they took me through all the very impressive buildings and very impressive labs that they had. So I had no idea that, you know, Ray Ozzie went there. I had no idea that Marc Andreessen, graduated from there. And so there were all these buildings that I was looking at. And then they took me to the to the Ag Department. So I found out very quickly that there's, that UIUC was one of the epicenters of data science. Fei-fei Li went there. So that this whole stuff with deep, deep learning and imagenet and all that stuff actually had its origin there. And then it went to Princeton and then Stanford.Al Eisaian: So I knew I knew nothing about agriculture. But I had just recently sold my company and I was thinking like, where do I spend the next decade of my life or more? And I wanted to do something that had global impact. And I've been a little bit of a sustainability nerd for a long time. And and I sort of put two and two together. After about a year of doing research, I said, yeah, this is an area that I can bring my passion for big data and data analytics and agriculture and try to make something that would be more than just about making money. And then so that's how IntelinAir was born. My co-founder is a professor at UIUC, is a very storied professor, Professor Hovakimyan, and and so we kind of put our heads together and we said this is what we can do. And so when you look at even the name, IntelinAir, stands for intelligence in air. So it's really around observation, it's really about, if you want to improve anything you have to measure it, you have to measure it frequently, and you have to validate that measurement, and then actually put science to work. So that's the origin story of of IntelinAir. Harry Glorikian: So at the highest level, what's the value proposition here? Are we trying to make farming more efficient, more sustainable, more productive? All of the above? I mean, how are you guys thinking about this?Al Eisaian: Yeah, so and I'm big on names that kind of actually describe what we do. So the name of the product and the service is called AGMRI. So a lot of analogs from health care. So AG stands for agriculture and MRI, the way we describe it is Measurable, Reliable Intelligence. So if you can agree to the thesis that if you want to improve something, you have to frequently measure and see what the behavior of that thing that you're trying to improve is observed properly, accurately, and then you put the types of, you make the types of decisions that allows you to introduce and make those improvements. So at the highest level, it's a comprehensive crop performance intelligence platform. And when I say comprehensive, I mean full scope and I mean full season. So when I say full scope, it's everything, it's not just imagery, it's soil information, it's weather information, it's everything that is absolutely essential for growing crops. It's the farmer practices. It's getting the IoT information off the equipment. It's all of those things combined. It's sort of what we call there is our gigantic data ingestion challenge. Right? We're talking about petabytes of data. The value proposition really is around timely, actionable insights that allows not only the farmer, but the whole ecosystem of farming to benefit and make better decisions. So it's something that provides value to the entire value chain.Harry Glorikian: So what is the special sauce of this? What can you do with high resolution field images that that no one else can do, or what is the computer vision...and I feel like Jennifer's about to jump in here any second now and tell me like...but what is that special sauce that you guys have brought to the table? Because I have a feeling here there's multiple layers of information that are getting stacked on top of each other to sort of, I want to say, tell a story of what's happening. Tell me how you guys would describe this. And remember, there are probably no farmers that are listening to this podcast. So if you could sort of put it into context, because at some point I can almost see that these, this approach has a superimposition onto different parts of health care when we look at it.Al Eisaian: Sure. Jennifer, do you want to take the lead and I'll chime in as necessary?Jennifer Hobbs: Absolutely. So much like health care, our data is truly huge. A lot of people talk about big data, but our data really is big and it's big in a lot of different ways. So, as Al mentioned, we have lots of different channels, right? We have RGB, we have near-infrared. We also have things like thermal. We have the soil information. We have the topo maps. We have all of this information that we can incorporate into the models. Then additionally, we have it at high res. So there's a lot of things and a lot of work, and computer vision in agriculture in the past has been limited to publicly available, low-resolution satellite data. And it's great that it's out there and it's free and it covers lots of different areas. But there's only so much you can see at that resolution. Where at the resolution we're at, we're able to see the crops emerge weeks before you can see it in satellite, you can see the different stressors within the field. You can see individual weeds and weed clusters. And that really, that level of size makes the data richer, allows us to do earlier and better prediction across all of the different tasks that we're interested in. And then because we fly around 13 times a season, we have a continually evolving view of the field. So from a single snapshot, at any point in time, you can do prediction decently well. It's pretty hard to do prediction, but with this temporal element, now all of a sudden you have that story, you have that evolving health of the field. And we can do, by using multiple flights, we can do both better detection as well as better prediction. And that's very exciting. So it's big data on a lot of different fronts. And because we have so much of it, we can then turn around and use a lot of the deep learning methods out there that help us deliver these models across a variety of tasks, a variety of different lighting conditions, domains, and really scale up quickly and and address the issues that are most pressing to the farmers.Al Eisaian: So, and just to give you an indication on scale, when we talk about resolution, the free satellite is like 10-meter by 10-meter squares. We're talking about 8- to 10-centimeter squares.Harry Glorikian: Yeah, like you can actually see the bug on the leaf.Al Eisaian: Not quite, not quite. We need to get down to like maybe a couple of centimeters to see the bug on the leaf. But we can see a lot at 8 to 10 centimeters. And that's not far away, right? I mean, a couple of centimeters at scale. You can do it with drones today. The big question is, again, the volume of data. Because every time you come down, you know, it just explodes.Harry Glorikian: So every one of these companies out there is obviously trying to convince... It was funny because I was reading, I haven't gotten through it, but a Technology Review piece that was just written about ag tech. But everybody's trying to convince, "You need our technology because it improves yield," or some other aspect. And so how do you... What's the pitch? And how do you win a farmer's trust, right, to be part of this process that they're doingAl Eisaian: You know, I think, again, back to how the company was built. I mean, way before we decided to really focus on just the ag sector, I personally had, I visited like a hundred farmers. And then my team has probably visited hundreds of farmers. A lot of those visits were actually in their farms. And then a lot more was done at these shows, Farm Progress and other shows. We would just engage people in conversation and ask them, What are the issues that they're having? How do they do their days work? And we had a lot of a ride along. Literally we lived with farmers, just to try to understand what are the...it's not just technology for technology's sake. In our case, was the question was, is it going to be used? Is it going to be used? And farmers basically were not interested in just getting bunch of images. They were like, "Just tell me what my problems are. Tell me Soon enough that I can go address it. And if you do that, then we'll engage." So initially, the first couple of years we were just iterating with the farmers, directly with the farmers. The last couple of years, what we've done is, obviously we think that we're kind of getting closer and closer, and I think we are there now, where this technology can be distributed by through our partners. So large companies that have tens of thousands of farmers that they can serve with our technology. So the go-to-market with farming is quite a challenge. And that's thing that I completely, completely underestimated. I thought farming was simple. Farming is really, really complex. And I was like, this is my fifth company I've started in two decades. And I can say by probably an order of magnitude, this has been the hardest because there's so many elements, especially outdoor farming. I think indoor farming, vertical farming, there's a lot of the elements that you can control. So indoor farming is a lot simpler. And if I were to... Maybe I shouldn't say this, but if I were to start it all over again, I would go after indoor farming. I wouldn't do outdoor farming. But my love of sustainability and the planet and stuff like that would still pull me to the outdoor broad acre.Harry Glorikian: So I'll be honest with you, the first time I, and this was a long time ago, the first time I went to EPCOT Center, and I went through their hydroponic area and sustainable farming and then the aquaponics area, I was like, "I really want to start a business like that." And I swear to you, every time I see an article, I get sucked into it because I think this is going to be the next big opportunity, although making money there is really hard.Al Eisaian: Exactly. That's because you don't know all the details. That's the curse of entrepreneurship. It looks really good. We're like, oh, my God, you see dollar signs. And you see your name in the headlines. But then you get engaged and oh, my god, it's like a can of worms after can of worms after can of worms.Harry Glorikian: I know it, Al. I mean, it's funny because every time I get involved in something I don't know every detail and then, but once you're in it you've got to get out of it. And so you've got to dig your way out of the hole, no matter what. Right? Otherwise it fails and that's not acceptable.Harry Glorikian: So, Jennifer, when you guys are doing this stuff, how much of this are you having to, you know, I keep thinking about my world where we have images, we have classification of those images or diagnosis of those, and then we train the system over and over and over again. And the bigger the data sets, the better. You guys are working not just with one image, but multispectral levels of imagery. And so how are you approaching this from, I guess, the machine learning perspective. I don't even know all the techniques that you guys are using, but are you taking stuff that's off the shelf? Are you having to design it from scratch? Is there some combination? But what walk us through how you look at that area and where you see that technology going next.Jennifer Hobbs: Sure. Well, we do a little bit of both and, interestingly, the medical imaging space is probably the space that is most similar to what we're doing as far as like techniques used, because of the size of each individual image, the number of images. So we do steal a lot from the cutting edge work that's being done in the medical imaging space. But one of the things I've always, when doing R&D in an academic setting, in an industrial setting, so I did my PhD in physics. So I have an academic background, but when we're doing R&D in an industrial setting, I still believe, I sort of believe you can do the research in sort of an iterative, agile-like fashion. So a lot of times we will take essentially a baseline model or whatever is sort of standard in the field. What's the what's the simplest thing that we think can work that's going to get us some initial results? And we'll try it and we'll see how it works and then we can decide how to go from there. So if we're talking about a detection or segmentation task, if I take one image and I do the simplest thing possible, which is just maybe stack all of the channels together, how well do I do? And then when I start to look at the failure cases, I can sort of start to see, well, are the mistakes that it's missing...would it do better if if I could give it more historical information? Ok, well, if I want to fuse the temporal element, how do I, how might I construct the network so that I can bring in this additional temporal time element? Sometimes you can do it as simply as just stacking more images. Sometimes you need something temporal in nature, RNN, LSTM type approach. In this work that we did that was just accepted at AAAI, we used a convolutional, we use a U-net to sort of get the features and then a convolutional LSTM to incorporate the temporal elements. Other times, maybe it's not so much the temporal element, it's I need to get more context. I need to see more of the field with a single glance so we can use some of the dilated convolution techniques out there. So a lot of it is sort of starting simple, seeing what works, seeing where things are still lacking, and then identifying the different routes, different ways where we can fuse more information into the system, more and more and more, until we kind of get to a level that we're happy with.Harry Glorikian: So I'm trying to get again to the secret sauce. Is the image-gathering part of the process becoming sort of commoditized over time, by the drone technology or different methodologies of capturing that? Or is there uniqueness in the capture part? Or is the uniqueness in the data analytics side of it?Jennifer Hobbs: I think it's both. I mean, the imagery itself, I think we're currently, like one of our strengths is the temporal element. But assuming you have the data a lot of times with data science and machine learning, a lot of the times the secret sauce is actually asking the right question, is knowing what it is you're looking for or what problem you're actually trying to solve. Sometimes we can get, it's easy to get kind of caught up and say, well, "I want to do everything all at once" or "I want to detect this." And maybe you actually don't care about detecting this. What you really want is to solve a downstream process. And so a lot of times it's still understanding what the farmer needs, what they want, what his end acceptance criteria might be. And then and then going after that. Because in truth, for somebody, let's say in an academic lab, you never want to say, "Well, I actually don't care if my model is not 100 percent, I want the best outcome possible." And certainly we do, but we also have to look at it in terms of what's performance versus cost, performance versus time. If I can make a model that runs three times faster and is only two percent lower in performance, well, now the cost is is a lot less. And so there's that business criteria kind of on top of the actual machine learning. And so I think a lot of that understanding how this is going to be used, how this is going to deliver value to the customer, is also one of the things that we do really well.Al Eisaian: So as far as far as the capture side, yeah, we've been the main culprit of commoditizing it over the last five years. I was on a panel, I think, five years ago, and I said, "You know what, I should be paying a penny per acre per capture." And this is when people were charging $4 an acre for one capture, and that was at 28-centimeter resolution and I wanted it at 5-centimeter resolution. We didn't quite get to 5-centimeter resolution. And we didn't quite get to a penny per acre per capture. But we're pretty damn close. And I think we're going to get closer over the next three, four or five years. So the more we can automate the capture... So right now, the vast majority of our capture is through manned airplane with very, very high powered, expensive sensors in the belly of the airplane. These are not your Canon cameras hanging out the window. These are these are like a quarter-million-dollar sensors that you can fly 120 knots, cover 150,000 acres a day and capture that at 8- to 10-centimeter resolution, pretty accurately. It's almost like, I think it was probably five years ago it was military technology. Now it's commercial. And we're hoping that more military technology will become more commercial. So I think that's commoditizing. And then I think two years ago, October, the US government relaxed satellite imagery for commercial applications from 50 centimeters per pixel to 25 centimeters per pixel. So you can see that from a standpoint of purely ground spatial resolution, that is happening. Right. I mean, our government probably has technology at 5 centimeters, 10 centimeter resolution today, but not open for commercial. That's going to change over the next three to five years. I'm willing to bet good money on that, that it will. So now, you still have the thermal problem, especially for the agriculture sector. But imagine that you have satellite imagery at 5-centimeter ground resolution. That becomes pretty powerful. Right. And then as far as commoditization, that data should be, I hope, should continue to come down in pricing so that it's available and it's ubiquitous.Al Eisaian: And then so then back to your question of what is the real differentiator and secret sauce? It's the analysis. It's the A.I. That's one area that is going to continue to be a bottleneck and continue to be more of a bottleneck in agriculture, because the vast majority of data scientists and machine learning PhDs are not smart enough yet, as Jennifer is, to actually go to agriculture. Everybody is doing this. We have an overabundance of people that are doing self-driving cars, overabundance of people that want to go into the health care field. But we have the really smart people that come to agriculture, like Jennifer.Harry Glorikian: So well, I could tell you, like, we definitely don't have enough people that go to health care. I can I can attest to that. I mean, I keep trying to lure people and say, forget this whole Facebook junk. What are you going to do there? Come to health care so that you can change people's livesJennifer Hobbs: The one thing I'll say, the difference with, there are a lot of things that we have in common with health care, but one of the differences is just the scope of the data. So the data itself is large, but we collect all of this raw data. But what really gives it value is when we can extract information out of it through these different models. And certainly to get started, at least you need annotations and you need good ground truthing and annotations. And that's another thing where we have people skilled in that area who can generate these annotations for us. But I think one of the exciting areas in this field, and really an area that's sort of hamstringing the CV and ag community out there, is if we have petabytes of unlabeled data and only gigabytes of annotations, how do we narrow that gap? How do we use all of the unannotated data out there? Because in truth, we're never going to get all of it. You can't annotate the entire world every single day. So we need to use what we have to also further maximize the unlabeled data that's out there. And I think that's a really exciting area that that we're excited to go after. And I think will be a real game changer on this front as well.Harry Glorikian: I'm obviously thinking on my feet here, but I'm trying to figure out like, OK, but in our world, like I can for the most part, my predictive power, I mean, it's getting better and better over time, but I don't have as many elements per se affecting, like the weather, the water, the tractor that came, there's a lot of things that you guys are trying to adapt for, so it's sort of exciting, like if you guys actually figure out how to take all these inputs and really predict better, I almost want to say, like I want that prediction model and start to think about superimposing it into my world, because I don't think we have as many variables. I know somebody somebody is going to make a comment that listens to this, "Harry, you don't know what you're talking about." But I do believe that you guys are dealing with things with many more unknowns than maybe we are in the health care world. So how well is the predictive nature of what you're doing to let someone know something before it happens. To say "You may want to go and look over here" or "By the way, historically, we've noticed that if you do this, you got a better outcome." Are you guys at that level of being able to make those recommendations to farmers?Jennifer Hobbs: That was the really exciting kind of result that came out of Safa, she was a PhD intern with us last summer, this work that that was accepted at AAAI that she did. So we were doing nutrient deficiency detection from the air. Can we find areas that are under stress? And this is really important because once stress sets in, you can't fix that. You can just sort of stop it. So you want to know as soon as possible that this area is lacking nutrients, you can go out and spray. At the same time, it has an environmental element to it because the more targeted and precise you can apply the chemicals, the less excess chemicals ends up in the water table, for example. So if we can, One, we want to detect it. But let's say detection for this task with our data, you can try a bunch of different things. And it hovers around an IOU score of, let's say, 0.4, depending on kind of where and what time of the season. And we did a lot of things from a single image and it was hard to kind of get it above that. When we started, including the temporal element -- what if we include the previous two flights? All of a sudden that IOU for detection shot up to, I believe, close to 0.6. And so then our next immediate question was, well, if I can now detect really well, can I anticipate this one, two flights out? And we saw that again, using this flight over flight information, we were able to predict these regions of stress two flights into the future better than we were able to detect from a single image initially. So sort of seeing how the field is changing week over week gives the model enough information to say not only is it here, but this is where it's going. And that's extremely powerful and has a lot of value to to the farmers.Harry Glorikian: So it's similar to, now I forgot her name, but she's over here at MIT where she's taken historical MR images in and been able to find features that predict a tumor advancing into the future before a human being can actually really see those features. And so that I guess that's my next question, is, what does the system see that a human can't see? I'm sure it's a lot, but work with me here.Jennifer Hobbs: The answer right now, today, is we don't know. Right. The sort of the trust of these deep learning models, unlike the past machine learning models, where they were based on handcrafted features and you could say, oh, it made this decision because of these features. There's a lot of things we can do to try to understand what the model is looking at. But it's not it's not as straightforward in the past. So interpretability is obviously a huge area of the machine learning community right now and one I think will continue to to grow, because people want to know, what is it, what is it looking at, what is it seeing? And there are some additional things we can do in our field, kind like medical as well, where you say, well, in addition to knowing what the model is looking at, I want to know I actually want to know causal effects. And then that's a whole 'nother area as well that's, I think, really kind of catching catching steam. So, yeah, the answer is we don't know. We can hypothesize and say, well, you know, it's doing things like, by the way it's constructing its features it's a little bit more robust to lighting changes. So it's able to control for this and that and actually see this sort of evolution. But we don't know that. That's sort of our best hunch at this point. But that that's really sort of all it is, is a hunch.Harry Glorikian: I can see how over time like this is, you know, it's going to provide more accurate, actionable information about crops. But let's say you sign somebody up and they start their first passes. When do they start seeing the benefit of the service?Al Eisaian: It's almost immediate, right? Because, so, A), they don't have to go through a bunch of different point solutions to kind of try to keep an eye on things, I mean, we're talking about vast areas, right? I mean, these are like multi-thousand-acre farms. And, you know, in the US, it's not really contiguous farms. You might have a couple of plots over here, a couple of fields over here and then several fields 10 miles away because of how inheritance has worked out and because of subsidies and whatever. And so the fact that you can, in the winter or if you have inclement weather outside, you can actually sit in front of your computer or on your iPhone and keep an eye on your domain, if you will, and just sort of like flipping through the stuff, that's immediate value and you don't necessarily need to have every flight to happen.Al Eisaian: I mean, again, those flights are again... It's a continuous system. And then you've got 13 high resolution captures. Because there's stuff in the in the system already. So there's a bunch of stuff like, you can look at from your last season, that allows you to make decisions for this season that you're in. And so the value is almost immediate.Al Eisaian: And then I also want to emphasize a couple more things. One, it's a decision support system for the farmer as far as which fields do I go to? So we do the prioritization. We say here's the severe areas by field, by percentage, so that you know exactly. And then also we pinpoint where the problem is. So they don't just go to the field, they actually go to the, they're staring at the problem. Harry Glorikian: That's interesting. It's exactly like what I was thinking about, guys, because, you know, they've developed a system that can show a cranial bleed and it'll move it up on what a radiologist should look at. So there's so many similarities of these technologies. It's just looking at different spaces.Al Eisaian: Wee flipped 80-20 or maybe 90-10, which is, instead of 80 percent of time guessing or trying to figure out where your problems are and 20 percent of time you're addressing your problems, we flip it, which is we take care of that. So you spend, I mean we actually alert you, you don't even, I would say 5-95 right now. We tell you where the exact problems are. So 95 percent of th time you are addressing issues. And then the second thing with regards to the collaboration that happens between farmer and all of the people that are around the farmer, the retailer, the sprayer company, the irrigation company, the seed company, if they give access to their fields, then they can actually do it remotely. So we're talking now tele-agronomy.Harry Glorikian: That was going to be one of my next things is how do you, how does this dovetail with all this what, what is it, precision ag technology that's out there? And how do you, are you working with those companies to integrate this information?Al Eisaian: Yeah, the way that we have built the product and the insights it can we can populate, we have like API systems with John Deere and FieldView Climate and a bunch, a whole host of others. We believe that that insights and data should be democratized and free. Not free necessarily that we don't want to make money, but from a standpoint of where you need to consume it. So it could be mobile and you can consume it on our app. On AGMRI. It could be a widget inside of a John Deere operations center. It could be a widget inside of Climate FieldView. The main issue is what is the preference of the farmer? Wherever that they are consuming their stuff and they want to get these insights, we're happy to kind of pipe it over there. So these collaborations, as I sort of think about the future, it's better data. I mean, I think Jennifer hit it right on the on the nail, which is you got you got to increase the trust in that, that trust translates to lower costs, higher yield, less headache, better lifestyle. Because farmers in planting phase all the way to harvest, planning for next year, it's a pretty anxious time, right? So imagine that actually this is also a lifestyle improvement, because now you feel a lot more in control, versus guessing, versus somebody else coming and telling you stuff, versus, there's always some sort of disease that's a runaway versus it's surprising you. Wouldn't you want to know, like, if it's in the next county and if you can take some preventive measures, you can be in a better situation. So the old saying is an ounce of prevention is worth a pound of cure. Unfortunately, people don't pay for prevention. They pay for a cure. And I think that's where that's where I think that whole mindset is shifting.Harry Glorikian: It's interesting because we are trying to shift health care away from only treating somebody when they're sick and actually managing them when to keep them healthy is more valuable. So. I mean, I have two sorts of questions. How do you look at yourselves versus other people in the field that are making these, making a lot of claims, because I have seen things around carbon sequestration and so forth. And then sort of a dovetailing question is, I feel like there's so much more that you could do with this rather than, I know the application that you're looking at, but the possibilities around commodities and all those sorts of. I'm a capitalist, I can't help myself. I'm thinking about, you know, but there are so many other areas. What could or those other areas be that this is applicable to? And again, how how do you compare to other people in the field. Not trying to pull anybody down or raise anybody up, but just as a sort of a thought process.Al Eisaian: We're the best and everybody else is just so-so. Harry Glorikian: [Laughs] I should have asked Jennifer that question, Al.Al Eisaian: Not from not from the boastful entrepreneur. Very fair question. So I think so. I mean, it's really a question of approach. From day one, we've invested in data science and and cutting edge science. And literally we're starting to come to market this year, five years after starting the company. This is the year that we're going to actually spend money on marketing and sales. Why? Because it's damn hard, I mean, Jennifer, just explained. It's really, really hard to get to a level that you can with a straight face tell people that this is not vaporware, that this actually works.Al Eisaian: In comparison to others. You know, look, carbon sequestration, at the core of it, what does it entail? You have to measure so you have to trust the measurements that you're making pretty certain practices. You have to verify. And you have to certify. And then you have to pay people. The certification process, the verification process is the hardest and who has the most granular information in the world? Nobody has invested as much money as we have in really, really granular, really, really high cadence, like 13 times a season. But then there's a bunch of other things that is like every five minutes. Weather. Precipitation. And so when you look at it that way, you say, OK, if you're thinking about carbon sequestration, if you're thinking about actually helping the climate situation. Agriculture and forestry, agriculture is 25 percent of problem and also 25 percent of the solution. And forestry is 17 percent, 17, 18 percent, depending on whose numbers you're talking about. If you take those two together, then everybody should be talking to IntelinAir about our technology. Everybody is interested. And then, as I said, we're just starting to kind of talk about and start boasting about our stuff. But do you think about FedEx spending $200 million buying carbon offsets in the future? And then who's going to measure it? Who is going to verify it? Who is going to certify it? Who is going to make sure that that farmer gets paid? These are challenging things that have to be solved. But at the core of it, we've got a solution. Now, somebody else can take that solution, or maybe we will do it, and then monetize it, but ultimately it's not through handwaving and PowerPoint presentations, it's really about science. You have to measure it, right. You have to say "I actually sequestered x many gigatons of carbon. And here's the measurement before. Here's the measurement after." Right. And here's what the farmer did. And he deserves this check, OK? And and so I think on that front, we like our chances.Al Eisaian: With regards to some other people. I mean, look, some people look at this thing primarily as imagery business. We've never looked at it as an imagery business. We've always looked at it as a crop intelligence business, what you're trying to do is you're trying to use science and whatever and the highest fidelity data that you can get your hands on to provide real solutions, to provide real, take it to the bank ROIs to the farmer, but not only to the farmer, but also everybody else that's involved. You mentioned commodity trading. Would it behoove the people that provide working capital to farmers to say, hey, you know, it would be good -- it's sort of like the Progressive Insurance thing. If you say yes to this gadget inside of your car where I can measure how you're driving, I'm willing to give you a 20 percent discount. We're going towards that. So the most advanced, we are talking to Wells Fargo and other companies. They're starting to think that, because, that a big asset. I mean, if you're giving working capital to people that are not data driven, that might cost them more. Al Eisaian: Insurance. You know, one of the one of the things that I learned in year two was there was a massive weather problem in Iowa and I went to this farmer's shop and there was like five to five drones, different types of drones. And I said, what are these drones for? He goes, oh, yeah, when when weather hits, my brother takes that one, I take that one. My cousin takes that one or two field hands take these two. And we all jump into our trucks and we we drive out to the fields. And for the whole day we survey, we fly the drone, take imagery, bring it back, take it out, put it into the system. And think about that level of detail that they have to go through just to negotiate with the insurance adjuster what they need to get paid on the crop insurance front. That's one way of doing it. Now imagine the way that we can do it, which is both the insurance provider and the farmer are subscribers to our system, we actually have algorithms that tell you exactly by percentage what the damage was. So there is no pissing contest between, oh, look at my thing, look at my video, look at my this.Harry Glorikian: So what I find is interesting is I actually I was talking to somebody at another venture fund earlier today, and I was I was saying to them, I'm like, know, once you deify something, the potential business model shifts are phenomenal. You just have to imagine them. And now you've got to bring other people along with you, which is half the problem.Al Eisaian: I want to do it for the farmers, right. I mean, some farmers say, what are you gonna do with my data? I go, you know what? I want to pay you for your data. And they're like, what? I go, Yeah, you know, if you and I get into business where your data now matters because you're running your farm better, you should get a better rate. You should get a better insurance rate. You should get better yield. You should get better. Everything, right. That data has value and I want to pay you.Jennifer Hobbs: You can turn it around, you can use it to create better seeds, better products, because you could do a lot of, there's obviously a ton of research that's done in the labs, around the farms, that are being used to develop these other products. But then they have to go out and live in the real world. And the question is, well, how well is this product going to work on my field? Given all of things? You know, what if they didn't have my type of soil or my type of weather. What if it rains more or less the season? And now you have, you know, acres and acres and acres. You have entire states of data that you can actually look to see how well did these different combinations perform. More than just you know, "Here is a really confined experiment that was run," how did it actually fare out in the real world? Because maybe it's also very effective, but it has to be used a certain way. You find that people aren't using in a certain way. Well, if I make these changes, can I get better yield? And I think that's where having the data coming in just opens up so many different possibilities.Al Eisaian: There's one more thing to add just relevant to this thing. Imagine that USDA has thousands of people that call and get survey data. They call a farmer that has let's say... This is a case in point, like a real, real live thing. The farmer has 43 fields. He reports on one field and extrapolates. And that's how USDA, for the most part, gets their estimations. They use some satellite stuff as well, but you can imagine? It's $8 billion a year of of guarantees. And I don't know how much, but there's I'm sure there's hundreds of millions of dollars of fraud that happens where the farmer reports something that didn't really happen. And then now they have to get the federal farm insurance. So what I'm saying is that, you know, the US government should scan and get all the data, and just give it to people like us to do the data crunching. Right. It would save tens of billions of dollars of taxpayer money, literally. Because right now we're doing the, paying for the capture. We're doing all the analysis. We're doing the productization. Can you imagine? That's, I think, where we need to get to.Harry Glorikian: So let's jump back to the to the technology for a second. Where do you see this going? Because I just you know, every time I try to keep up with this, I'm barely able to. It's moving almost too fast in a certain sense. Right. So where do you see this going from a technological perspective? Is it resolution? Is it analytics? Is it predictive power? Or is it all of the above? I mean, I'm trying to if you were giving a visionary talk about where this is going in the future, where how would you frame it?Al Eisaian: I'll start, and then Jennifer can probably be much more articulate about this. Look, we've made our bets. 80 cents on the dollar for us in R&D and engineering goes to AI. We're making huge, huge bets on that. We keep hiring more people. And then maybe as an entrepreneur, I should stop that, but maybe not. But that's the bet we're making. On the capture side, I think there's two very promising developments that we're betting on. One is the ultra high resolution imagery below the atmosphere will continue going to these high flying drones that don't need bathroom breaks, that can fly 24 hours or maybe 48 hours a day and they can capture a 10, maybe 12 times more of the data that we need. And so obviously the cost will come down. I think the sensor tech, there's many, many great companies, both defense-related and nondefense-related companies that are working on sensor technologies that will blow your mind. And we can go to hyperspectral imaging, which now for disease detection and stuff like that becomes really valuable. So that's on the sort of like the physics side of things. Like flying sensors, hyperspectral. But I think the most exciting part is post data capture. That's everything that Jennifer and Jennifer's team does. And I'll pass it to Jennifer.Jennifer Hobbs: Whatever I try to give academic talks, I try to capture the minds of the other, the people in the computer vision and machine learning fields who might be doing stuff like self-driving cars or what have you, because there's so many opportunities to both make computer vision for agriculture better in the future. But I think, to benefit both the agriculture and the computer vision side, there are challenges because we're getting so much data, more data, more sensors, just more types of data. Right now, you're going to run into this point where, what if what if the information on a single field is a terabyte? What do I do with that? How do I how do I process it? How do I extract all of the information? What kind of methods do I use? If I have hyperspectral imagery coming in all the time and then I have all this equipment data and all this weather data, how do I make sense of all of that? And there are so many different avenues there to to explore. I think, I hope people in in the machine learning community get really excited about this and say.... It has huge implications for the agricultural industry, but it's a great domain for us to understand, to improve our understanding of computer vision. So I think as more and more data comes in, it just puts the burden on us to come up with methods that can handle this amount of data. How can I handle an image that's maybe 100,000 by 100,000 pixels fifty times during the season, where I have hyperspectral data, with all of this weather coming in. And I think that's a really exciting, exciting piece. And then I think that also prompts, on the hardware side, you see a lot of a lot of interest around the different chips, the different edge devices that are used to process these. I think it just encourages more and more of that in the future. And so it's, I hope I am optimistic that I think a lot of these challenges, ag will start to be a preeminent domain in computer vision that people, it's an area just like autonomous vehicles that people are really interested in because it improves our understanding of these methodologies in addition to changing the world.Al Eisaian: And you can't eat an electric car. You can eat an ear of corn.Harry Glorikian: No. Yeah, but I was always thinking about there are techniques and approaches that you're learning and taking that we can learn from. I just don't know if anybody's cross referencing the work or the papers that are being written. I'm sort of the geekoid, who's trying to read, you know, obviously the title captures my attention, but, you know, reading all sorts of stuff because I know that it's a tool. It doesn't matter what you're throwing it at, the tool will with a few tweaks might work well. So I'm trying to keep absorb all this stuff and hence the the conversation. Besides the fact that I think editing of crops or making changes in crops and then applying all the stuff that you guys are talking about, I mean, it is a combination. We're going to change the way the world is fed, over time.Al Eisaian: Absolutely.Harry Glorikian: Well, this was great. I look forward to staying in touch and hearing how the company evolves and again, how the technology evolves, I though I, I will probably always be struggling to keep up with everything that you're saying. But that's OK. That's that's part of my job, trying to understand what's happening and where it's going. So thanks very much for the time and look forward to hearing how this thing evolves in the future.Al Eisaian: Thank you so much for the opportunity, Harry.Jennifer Hobbs: Thank you so much.Harry Glorikian: That’s it for this week’s show. We’ve made more than 50 episodes of MoneyBall Medicine, and you can find all of them at glorikian.com/podcast. You can follow me on Twitter at @hglorikian. If you like the show, please do us a favor and leave a rating and review at Apple Podcasts. Thanks, and we’ll be back soon with our next interview.
Wienke Giezeman, CEO & Founder of The Things Network, invites Ray Ozzie, former Chief Software Architect of Microsoft to take the stage with him as the two tech pioneers discuss their experience and share their most valuable lessons learnt.
There’s no need to ask them who they are, what they do, businesses and products they built because listeners know them through their stories. After a lot of random, but worthwhile conversations, they’re getting good at being podcast co-hosts. How long does it take to listen to all episodes of Ben and Derrick’s Art of Product (AoP) podcast? Who knew it would be binge-worthy? One more down, one to go before they reach Episode 100! Today’s Topics Include: Tuple 1.0: App is available, despite instant self-serve checkout setback Tuple customers are expanding their use, but there’s still room for more users One-time lump sum: Tuple pricing model that makes sense StaticKit Beta: Start small with kernel of a product to keep funnel constrained Marketing Research and Product Risk: StaticKit to stand on its own or grow Drip and Deleted Emails: Save copies to maintain confidence in critical path Negative Churn: Influence users to upgrade pricing for expansion revenue Links and resources: Art of Product on Twitter (https://twitter.com/artofproductpod) Derrick Reimer (http://www.derrickreimer.com) Website Derrick Reimer on Twitter (https://twitter.com/derrickreimer) Ben Orenstein (http://www.benorenstein.com/) Website Ben Orenstein on Twitter (https://twitter.com/r00k?lang=en) Tuple (https://tuple.app/) Tuple’s Pair Programming Guide (https://tuple.app/pair-programming-guide) StaticKit (https://www.statickit.com/) Level (https://level.app/) Level Retrospective (https://www.derrickreimer.com/essays/2019/05/17/im-walking-away-from-the-product-i-spent-a-year-building.html) Level Manifesto (https://level.app/manifesto) Giant Robots Podcast (https://giantrobots.fm/) Steve Schoger (https://www.steveschoger.com/) Userlist (https://userlist.io/) Adam Wathan on Twitter (https://twitter.com/adamwathan) Tailwind CSS (https://tailwindcss.com/) Product Hunt (https://www.producthunt.com/) Ray Ozzie (https://www.linkedin.com/in/rayozzie) WordPress (https://wordpress.com/) Drip (https://www.drip.com/) Refactoring Rails (https://www.refactoringrails.io/)
Our guests this week are Paul Scharre from the Center for a New American Security and Greg Allen from the Defense Department's newly formed Joint Artificial Intelligence Center. Paul and Greg have a lot to say about AI policy, especially with an eye toward national security and strategic competition. Greg sheds some light on the Defense Department's activity, and Paul helps us understand how the military and policymakers are grappling with this emerging technology. But at the end of the day, I want to know: Are we at risk of losing the AI race with China? Paul and Greg tell me not all hope's lost—and how we can retain technological leadership. In what initially seemed like a dog-bites-man story, Attorney General Barr revived the “warrant-proof” encryption debate. He brings some thoughtful arguments to the table, including references to proposals by GCHQ, Ray Ozzie and Matt Tait. Nick Weaver is skeptical toward GCHQ's proposal. But what really flew under the radar this week was Facebook's apparent plan to drastically undermine end-to-end encryption by introducing content moderation to its messaging services. I argue that Silicon Valley is so intent on censoring its users that it is willing to sacrifice confidentiality and security (at least for anyone to the right of George W. Bush). News Roundup newcomer Dave Aitel thinks I'm wrong, at least in my attribution of Facebook's motivations. Mieke Eoyang, another News Roundup newcomer, brings us up to date on all the happenings in election security. Bob Mueller's testimony brought Russian election meddling to the fore. His mistake, I argue, was testifying first to the hopelessly ideological House Judiciary Committee. Speaking of Congress, Mieke notes that the Senate Intel Committee released a redacted report finding that every state was targeted by Russian hackers in the 2016 election—and argues that we're still not prepared to handle their ongoing efforts. Congress is attempting to create a federal election security mandate through several different election security bills, but they likely will continue to languish in the Senate, despite what Mieke sees as a bipartisan consensus. Not all hope is lost, though. Director of National Intelligence Dan Coats, now on his way out, has established a new office to oversee and coordinate election security intelligence. Nick adds an extra reason to double down on election security: How else will we be able to convince the loser that he is indeed the loser? In other news, NSA is going back to the future by establishing a new Cybersecurity Directorate. Dave tries to shed some light on the NSA's history of reorganizations and what this new effort means for the Agency. Dave and I think there's hope that this move will help NSA better reach the private sector—and even give the Department of Homeland Security a run for its money. I also offer Dave the opportunity to respond to critics who argued that his firm, Immunity Inc., was wrong to include a version of the BlueKeep exploit in its commercial pentesting software. The long and the short of it: If a vulnerability has been patched, then that patch gives an adversary everything they need to know to exploit that vulnerability. It only makes sense, then, to make sure your clients are able to protect themselves by testing exploits against that vulnerability. Mieke brings us up to speed on the cybercrime blotter. Marcus Hutchins, one of Dave's critics, pleaded guilty to distributing the Kronos malware but was sentenced to time served thanks in part to his work to stop the spread of the WannaCry ransomware. Mieke says that Hutchins's case is a good example that not all black hat hackers are irredeemable. I note that it was good for him that he made his transition before he was arrested. Dave and Nick support the verdict while lamenting how badly hackers are treated by U.S. law. We round out the News Roundup with quick hits: Facebook had a very bad week, not least because of the multibillion dollar fine imposed by the FTC; the Department of Justice is going to launch a sweeping antitrust investigation into Big Tech; there was a wild hacking conspiracy in Brazil involving cell phones and carwashes; Equifax reached a settlement with the FTC regarding its epic data breach. Speaking of which, we make a special offer to loyal listeners who can learn whether they are eligible to claim a $125 check (or free credit monitoring, if you really prefer). Just go here, and be sure to tell them the Cyberlaw Podcast sent you. Oh, and an anti-robocall bill finally made it through both houses of Congress. Download the 274th Episode (mp3). You can subscribe to The Cyberlaw Podcast using iTunes, Google Play, Spotify, Pocket Casts, or our RSS feed! As always, The Cyberlaw Podcast is open to feedback. Be sure to engage with @stewartbaker on Twitter. Send your questions, comments, and suggestions for topics or interviewees to CyberlawPodcast@steptoe.com. Remember: If your suggested guest appears on the show, we will send you a highly coveted Cyberlaw Podcast mug! The views expressed in this podcast are those of the speakers and do not reflect the opinions of the firm.
In this week's Risk & Repeat podcast, SearchSecurity editors discuss Ray Ozzie's solution for going dark, known as Clear, and what infosec experts are saying about it.
In this week's Risk & Repeat podcast, SearchSecurity editors discuss Ray Ozzie's solution for going dark, known as Clear, and what infosec experts are saying about it.
Des cartes d'accès d'hôtels compromises, le programme AARDVARK et la saisie de AnonIB
The Price of Amazon Prime is going up, Apple's AirPort is going away, Square buys Weebly, A DNA site might have cracked the Golden State Killer case, will T-Mobile and Sprint finally tie the knot, is Apple working on an AR/VR headset, and the weekend longreads. Stories: @matthew_d_green, @reneritchie Tweets: @jrichlive, @mattmcgee Links:RIP AirPort Base Stations: Why Apple is exiting the Wi-Fi router business (iMore)Apple’s working on a powerful, wireless headset for both AR, VR (CNET)GEDmatch, a tiny DNA analysis firm, was key for Golden State Killer case (Ars Technica)A few thoughts on Ray Ozzie’s “Clear” Proposal (Matthew Green) Weekend Longreads:Inside Jeff Bezos’s DC Life (Washingtonian)Hulu Beyond 'Handmaid's Tale': Execs and Stars on a Promising Yet Uncertain Future (The Hollywood Reporter)Can Silicon Valley Get You Pregnant? (Fast Company)You could be flirting on dating apps with paid impersonators (Quartz) Credits: Produced by @brianmcc and the @techmeme editors Music by @jpschwinghamer
In our 210th episode of The Cyberlaw Podcast, Stewart Baker, Maury Shenk, Ben Wittes, and Nick Weaver discuss: the encryption debate heats up; the FBI revives push for solution; “FBI doesn’t understand math” argument hits roadblock: hard to say Ray Ozzie doesn’t; Left/liberals piles on the Inspector General’s (IG) report suggesting maybe FBI didn’t want to use national security tools in a criminal case; good week for attribution and retribution; Carbanak mastermind busted in Spain? Nikulin extradited to US; the US to require social media usernames, email addresses, and phone numbers from visa applicants; Julian Assange loses internet connection, Matt Green displays his cruel streak; update on Keeper libel suit, if we can confirm case was dropped. Our guest interview is with David Sanger, National Security Correspondent for The New York Times. As always The Cyberlaw Podcast is open to feedback. Send your questions, suggestions for interview candidates or topics to CyberlawPodcast@steptoe.com or leave a message at +1 202 862 5785. The Cyberlaw Podcast is hiring a part-time intern for our Washington, DC offices. If you are interested, visit our website at Steptoe.com/careers. The views expressed in this podcast are those of the speakers and do not reflect the opinions of the firm.
TechByter Worldwide (formerly Technology Corner) with Bill Blinn
Windows Explorer is a basic part of Windows, but there's a better choice if you don't mind spending a few dollars. Ray Ozzie is making people crazy again, but he's probably right. If you don't yet have your own domain, should you? In Short Circuits, YouTube dumps some videos, Microsoft beats preditions, and Google sues the feds.
Gerry Gaffney conducts a wide-ranging interview with Jakob Nielsen.Is web usability where it's at? Does usability have a say on climate change? Why is the keyboard so popular?Jakob talks about having data propagate to multiple devices, about why government agencies continue to apply an outdated "waterfall' model, and about how usability can make developing countries rich quickly and thus improve the environment.He talks about the need to make things easier if you want people to do those things, about future directions in user interaction, and about the need to start small if you're budget-constrained.Jakob Nielsen's website is useit.com (www.useit.com)His company is the Nielsen Norman Group (www.nngroup.com).Jakob refers to Microsoft's Ray Ozzie (www.microsoft.com/presspass/exec/ozzie/default.mspx) and Groove (www.openp2p.com/pub/a/p2p/2000/10/24/ozzie_interview.html).He also refers to the anti-mac interface (www.useit.com/papers/anti-mac.html).Jakob's forthcoming event is usability week (www.nngroup.com/events/).Jakob's excellent books include "Usability Engineering" (tinyurl.com/4lsfdv) and "Designing Web Usability" (tinyurl.com/5ymz5t).
Google for Lynda and you'll find the legendary Lynda.com. Lynda has had a popular online presence for over 12 years. She created the original Web-Safe Color palette (remember when that mattered?) and now she sells nearly 20,000 training videos online via subscription. Silverlight is next. I chat with Lynda after we had lunch with Ray Ozzie and some other bloggers.
Microsoft's Ray Ozzie has a long and storied history of technological innovation with accomplishments that include creating Lotus Notes and founding Groove Networks. But Ozzie may now be facing the most daunting challenge of his career: coordinating the work of Microsoft's various product groups to keep the world's largest software company agile enough to address the challenge of the next generation of Internet-enabled software. Knowledge at Wharton recently met with Ozzie to talk about his vision for the future of networked computing. See acast.com/privacy for privacy and opt-out information.
Ray Ozzie is in. Bill Gates is heading out (but not entirely). And Steve Ballmer is staying right where he is (at least for now). What does this game of musical chairs among the members of Microsoft's high command portend for the world's biggest software company? Far from being a source of confusion and uncertainty Gates' recently announced decision that he will relinquish his full-time day-to-day involvement in the company in July 2008 may be just the breath of fresh air needed for a firm facing major challenges to its core business according to Wharton experts. At the same time it's not yet clear just how successful Gates will be in removing himself from a company that has been his life for three decades. See acast.com/privacy for privacy and opt-out information.
This is a few minutes from a dinner with Microsoft's Dan'l Lewin and a few others. We discuss Ray Ozzie a bit. Recorded: 2005-11-14 Length: 4:44, Size: 2.2MB
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