Podcasts about tractable

What was it about political theory that convinced Julie to study political science at university? What was occurring within the technology industry that created an opportunity for Facebook to do things in new ways for consumers which resonated with Julie and drew her to work there? Why was it so fascinating to be leading corporate development for a well-positioned adtech firm during the period of mass consolidation? What about management consulting strategy work led Julie to definitively know she wanted to be a business operator? Why did the autonomy of working at a startup foster a sense of exhilaration that Julie found herself addicted to? How did Tractable allow Julie to work on innovative solutions that brought powerful new technology to the physical world? What did the emergence of generative AI change that reduced the investment companies had to make in bringing solutions to market? Why isn't there a single source of truth for home renovation projects? What are the assorted challenges that contractors face as small business owners themselves while managing a renovation project? How has Block Renovation built technology to enable transparency and trust between homeowners and contractors? Where has Block Renovation integrated generative AI into its solutions? How is localization and deep real estate market knowledge incorporated into each Block Renovation city launch?Julie Kheyfets - CEO of Block Renovation, joins Proptech Espresso to answer these questions and discuss how working with insurance companies revealed to Julie how bad humans are at understanding risk due to our inability to think probabilistically.

Julie Kheyfets is the CEO of Block Renovation, an AI-first marketplace platform revolutionizing the home renovation industry by connecting homeowners with contractors and streamlining project planning. She assumed the CEO role in January 2025, following seven years as the company's COO. ​Before joining Block, Julie led North American growth for Tractable, an AI company specializing in accident and disaster recovery solutions, where her efforts contributed to the company's valuation surpassing $1 billion. ​Beyond her professional achievements, Julie is an accomplished ultramarathon runner, having secured first place in the women's category at the Habanero Hundred in 2021. (01:13) - Challenges in home renovations(02:31) - Julie's journey to Block(03:43) - Building trust with AI & data(05:49) - Contractor vetting process(08:17) - Feature | Market Stadium - Book a demo: Optimize your Multifamily & Single-family market analysis(9:28) - An AI architect in every investor's & homeowner's pocket(12:07) - Growth playbook(16:34) - Industry Trends & Homeowner Mistakes(20:11) - Homeowners & contractors :: Landlords & renters(21:55) - Feature: Blueprint 2025: The Future of Real Estate - Register now(23:45) - Business Model & Marketplace Trust(25:51) - Collaboration Superpower: Courtney Dauwalter (Wiki) & Brian Chesky (Wiki)

In this episode of the Top of Mind podcast, Mike Simonsen sits down with Julie Kheyfets, CEO of Block Renovation, to talk about what's happening with home renovations this year and beyond. About Julie Kheyfets Julie Kheyfets is the CEO of Block Renovation, the AI renovation platform empowering homeowners and contractors to build better together. Block's AI delivers instant cost, scope, and design guidance tailored to each homeowner, powered by proprietary data from thousands of projects. By accessing Block's vetted network of contractors, homeowners can discover the right professionals for their projects, rapidly receive easy-to-compare proposals, and hire with confidence, backed by Block's project protections. Before being appointed CEO, Julie served as Block's COO, steering Block's operations as the company evolved from a one-stop shop for renovations to an AI marketplace platform. Julie brings a strong track record of scaling AI to help consumers in complex industries. Before joining Block, she built and led the North America business for Tractable, the AI platform for accident and disaster recovery. During Julie's tenure, Tractable became the world's first computer vision unicorn for financial services. Here's a glimpse of what you'll learn:  How to match consumers with contractors Why so much of the difficulty in remodeling are “problems of information” The surprising relationship between interest rates and remodeling Block's unique demand renovation demand data and what it's telling us about the market right now How changes to the immigration system will impact the remodeling industry Why productivity in construction has not improved in 50 years and why that is about to change Trends in AI that homeowners should pay attention to Why AI and information have the potential to dramatically improve the consumer relationship with the contractor The most important demographic trends and what they tell us about the home renovation market over the rest of the decade Which local markets are the leaders and laggards in renovation Related to this episode: Julie Kheyfets | LinkedIn Block Renovation Mike Simonsen | LinkedIn Altos Research Featuring Mike Simonsen, President of Altos Research A true data geek, Mike founded Altos Research in 2006 to bring data and insight on the U.S. housing market to those who need it most. The company now serves the largest Wall Street investment firms, banks, and tens of thousands of real estate professionals around the country. Mike's insights on the market have been featured in Forbes, New York Times, Bloomberg, Dallas Morning News, Seattle PI, and many other national media outlets. Follow us on Twitter for more data analysis and insights: Altos on Twitter Mike on Twitter About Altos Research The Top of Mind Podcast is produced by Altos Research. Each week, Altos tracks every home for sale in the country - all the pricing, and all the changes in pricing - and synthesizes those analytics to make them available before becoming visible through traditional channels. Schedule a demo to see Altos in action. You can also get a copy of our free eBook: How To Use Market Data to Build Your Real Estate Business. The Top of Mind podcast features top real estate industry insiders and experts to unpack the most important housing, real estate, mortgage data and trends that are shaping the housing market. Hosted by Altos founder Mike Simonsen and produced by the HousingWire Content Studio.

Matt Clifford is the Co-Founder of Entrepreneur First (EF), the leading global talent investor and incubator. EF has incubated startups worth over $10bn, including Cleo, Tractable and Aztec Protocol. Matt is also Chair of ARIA, the UK's Advanced Research and Invention Agency, and advises the UK government on AI and in 2023 served as the Prime Minister's Representative for the AI Safety Summit at Bletchley Park. In Today's Episode with Matt Clifford We Discuss: 1. The Most Important Questions in AI: Are we seeing diminishing returns where more compute does not lead to a significant increase in performance? What is required to reach a new S curve? What do we need to see in GPT 5? Why does Matt believe that search is one of the biggest opportunities in AI today? 2. The Biggest Opportunities in AI Today: How does Matt see the future for society with a world of autonomous agents? What is the single biggest opportunity around agents that no one has solved? Is society ready for agentic behaviours to replace the core of human labour? How does warfare change in a world of AI? Does AI favour states and good actors or criminals and bad actors more favourably when it comes to offence and defence? 3. China and the Race to Win the AI War: Does Matt believe that China are two years behind the US in terms of AI capability? What are Matt's biggest lessons from spending time with the CPP in China working on AI policy? In what way is the CCP more sophisticated in their thinking on AI than people think? What is the bull and the bear case for China in the race for AI? What is the core impact of US export controls on chips for China's ability to build in AI? Does a Trump vs a Biden election change the playing field with China? 4. What Makes Truly Great Founders: Does Matt agree that the best founders always start an entrepreneurial activity when they are young? What is more important the biggest strength of one of the founders or the combined skills of the founding team? What did EF believe about founders and founder chemistry that they no longer believe? Does Matt believe that everyone can be a founder? What are the two core traits required?  

Merriam-Webster's Word of the Day for June 10, 2024 is: tractable • TRAK-tuh-bul • adjective Tractable is used to describe someone or something that is easily led, managed, taught, or controlled. // This new approach should make the problem more tractable. // The horse's tractable temperament made her especially popular with new riders. See the entry > Examples: “… Kawasaki's popular KLR650 … only makes about 40 horsepower, yet it has launched untold numbers of epic rides due to its reliable, tractable and manageable output.” — William Roberson, Forbes, 30 Sept. 2022 Did you know? A frequentative is a form of a verb that indicates repeated action. The frequentative of the word sniff, for example, is sniffle, meaning “to sniff repeatedly.” Some English words come from a frequentative in another language, and tractable is one. Tractable, meaning “easily led or managed,” comes from the Latin adjective tractabilis, which in turn comes from the verb tractare, which has various meanings including “to drag about,” “to handle,” “to deal with,” and “to treat.” Not to drag on too much about Latin, but tractare is the frequentative of another Latin verb, trahere, meaning “to drag or pull.” Now, one can pull or tug a draft animal on a lead, for example, whether or not that animal is willing or compliant. But if one can pull, handle, or otherwise deal with that animal repeatedly or continuously with ease (by treating it well, we presume)? Well, you can see where this is leading—in English we would call our helpful animal friend tractable. Speaking of farms, despite its resemblance, tractor did not pass through the frequentative tractare but it does come from trahere.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: The US Presidential Election is Tractable, Very Important, and Urgent, published by kuhanj on May 29, 2024 on The Effective Altruism Forum. Disclaimer: To avoid harmful polarization of important topics, this post is written in a non-partisan manner (in accordance with forum guidelines), and I'd encourage comments to be written similarly. US Presidential Elections are surprisingly tractable 1. US presidential elections are often extremely close. 1. Biden won the last election by 42,918 combined votes in three swing states. Trump won the election before that by 77,744 votes. 537 votes in Florida decided the 2000 election. 2. There's a good chance the 2024 election will be very close too. 1. Trump leads national polling by around 1% nationally, and polls are tighter than they were the last two elections. If polls were perfectly accurate (which of course, they aren't), the tipping point state would be Pennsylvania or Michigan, which are currently at +1-2% for Trump. 3. There is still low-hanging fruit. Estimates for how effectively top RCT-tested interventions to generate net swing-state votes this election range from a few hundred to several thousand dollars per vote. Top non-RCT-able interventions are likely even better. Many potentially useful strategies have not been sufficiently explored. Some examples: 1. mobilizing US citizens abroad (who vote at a ~10x lower rate than citizens in the country), or swing-state university students (perhaps through a walk-out-of-classes-to-the-polls demonstration). 2. There is no easily-searchable resource on how to best contribute to the election. (Look up the best ways to contribute to the election online - the answers are not very helpful.) 3. Anecdotally, people with little political background have been able to generate many ideas that haven't been tried and were received positively by experts. 4. Many top organizations in the space are only a few years old, which suggests they have room to grow and that more opportunities haven't been picked. 5. Incentives push talent away from political work: 1. Jobs in political campaigns are cyclical/temporary, very demanding, poorly compensated, and offer uncertain career capital (i.e. low rewards for working on losing campaigns). 2. How many of your most talented friends work in electoral politics? 6. The election is more tractable than a lot of other work: Feedback loops are more measurable and concrete, and the theory of change fairly straightforward. Many other efforts that significant resources have gone into have little positive impact to show for them (though of course ex-ante a lot of these efforts seemed very reasonable to prioritize) - e.g. efforts around OpenAI, longtermist branding, certain AI safety research directions, and more. Much more important than other elections This election seems unusually important for several reasons: There's arguably a decent chance that very critical decisions about transformative AI will be made in 2025-2028. The role of governments might be especially important for AI if other prominent (state and lab) actors cannot be trusted. Biden's administration issued a landmark executive order on AI in October 2023. Trump has vowed to repeal it on Day One. Compared to other governments, the US government is unusually influential. The US government spent over $6 trillion in the 2023 fiscal year, and makes key decisions involving billions of dollars each year for issues like global development, animal welfare, climate change, and international conflicts. Critics argue that Trump and his allies are unique in their response to the 2020 election, plans to fill the government with tens of thousands of vetted loyalists, and in how people who have worked with Trump have described him. On the other side, Biden's critics point to his age (81 years, four years older...

Our guest today is Franziska Kirschner, Co-Founder of Intropy AI and ex AI & Product Lead at Tractable: the world's first computer vision unicorn. In our conversation, we dive into Franziska's PhD, her career at Tractable and her experience building deep learning algorithms for computer vision products. She explains how she climbed the ladder from intern to AI Lead and shares how she launched new AI product lines generating £ millions in revenues. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Link to Train in Data courses (use the code AISTORIES to get a 10% discount): https://www.trainindata.com/courses?affcode=1218302_5n7krabaFollow Franziska on LinkedIn: https://www.linkedin.com/in/frankirsch/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ---(00:00) - Introduction(03:08) - Franziska's Journey into AI(05:17) - Franziska's PhD in Condensed Matter Physics(15:12) - Transition from Physics to AI(19:20) - Deep Learning & Impact at Tractable(33:21) - AI Researcher vs AI Product Manager (37:52) - The Impact of AI on Scrapyards(43:14) - Key Steps in Launching New AI Products(53:31) - Founding Intropy AI(01:00:37) - The Potato Travels(01:04:10) - Advice for Career Progression

In this episode, I explore Tractable's recent achievement of securing $65M in funding led by SoftBank, driving the advancement of their AI-enabled appraisals in the insurance sector and its probable impact. Invest in AI Box: https://Republic.com/ai-box Get on the AI Box Waitlist: https://AIBox.ai/ AI Facebook Community

Uncover the breaking news as Tractable secures a significant $65 million funding round, with SoftBank taking the helm. Explore the potential impact of this major investment on the future of AI-driven insurance appraisals in our latest episode. Get on the AI Box Waitlist: AIBox.ai Join our ChatGPT Community: Facebook Group Follow me on Twitter: Jaeden_AI

In this episode, I delve into Tractable's significant funding round of $65M, led by SoftBank, fueling the expansion of their AI-driven platform for insurance appraisals, discussing its implications and potential shifts in the industry. Invest in AI Box: https://Republic.com/ai-box Get on the AI Box Waitlist: https://AIBox.ai/ AI Facebook Community

Join us in celebrating Tractable's major funding win of $65 million, with SoftBank taking the lead. Explore how this financial boost is set to catapult Tractable's AI insurance appraisals into a new era. Get on the AI Box Waitlist: AIBox.ai Join our ChatGPT Community: Facebook Group Follow me on Twitter: Jaeden_AI

Delve into the strategic move as Tractable secures $65 million in funding, led by SoftBank. Join the conversation on how this substantial investment is poised to elevate Tractable's AI-driven insurance appraisals. Get on the AI Box Waitlist: AIBox.ai Join our ChatGPT Community: Facebook Group Follow me on Twitter: Jaeden_AI

Discover the latest development as Tractable secures a significant $65 million in funding, led by SoftBank. Join us as we analyze the potential impact of this strategic investment on the future of AI-driven insurance appraisals. Get on the AI Box Waitlist: AIBox.ai Join our ChatGPT Community: Facebook Group Follow me on Twitter: Jaeden_AI

In this episode, we explore SoftBank's pivotal role in Tractable's funding success, a substantial $65 million injection into their AI-driven insurance appraisal technology, and discuss the implications for the insurtech landscape. Invest in AI Box: ⁠https://Republic.com/ai-box⁠ Get on the AI Box Waitlist: ⁠https://AIBox.ai/⁠ ⁠AI Facebook Community Learn About ChatGPT Learn About AI at Tesla

In this episode, we unravel the details behind Tractable's successful $65 million funding round, with SoftBank at the forefront, and analyze the implications for the evolution of AI in insurance appraisals. Invest in AI Box: ⁠https://Republic.com/ai-box⁠ Get on the AI Box Waitlist: ⁠https://AIBox.ai/⁠ ⁠AI Facebook Community Learn About ChatGPT Learn About AI at Tesla

In this episode, we delve into SoftBank's significant investment in Tractable, funding their AI technology for insurance appraisals. We discuss how this injection of capital could change the insurance landscape and customer experiences. Invest in AI Box: https://Republic.com/ai-box Get on the AI Box Waitlist: ⁠⁠https://AIBox.ai/⁠⁠ AI Facebook Community Learn more about AI in Video Learn more about Open AI

Explore the implications of Tractable securing $65M in funding, spearheaded by SoftBank, for their AI-centric insurance appraisal solutions, as I dissect its potential to reshape the insurance industry's evaluation methods in this episode. Invest in AI Box: https://Republic.com/ai-box Get on the AI Box Waitlist: ⁠⁠https://AIBox.ai/⁠⁠ AI Facebook Community Learn more about LLM's Learn more about AI

In this episode, we explore Tractable's impressive feat as they secure $65 million in funding for their AI-driven insurance appraisal application, with support from the tech giant SoftBank. Join us to delve into the transformative potential of Tractable's innovations and how they are revolutionizing the insurance industry. Discover how AI is reshaping the appraisal process and the future of insurance claims in this insightful discussion! Get on the AI Box Waitlist: https://AIBox.ai/Join our ChatGPT Community: ⁠https://www.facebook.com/groups/739308654562189/⁠Follow me on Twitter: ⁠https://twitter.com/jaeden_ai⁠

In this exciting episode, we uncover Tractable's monumental achievement, securing $65 million in funding for their AI-powered insurance appraisal solutions, with SoftBank leading the way. Join us to explore the transformative impact of Tractable's innovations and how they are reshaping the insurance industry. Dive into the future of insurance appraisals and the dynamic role AI technology plays in streamlining the claims process in this enlightening discussion! Get on the AI Box Waitlist: https://AIBox.ai/Join our ChatGPT Community: ⁠https://www.facebook.com/groups/739308654562189/⁠Follow me on Twitter: ⁠https://twitter.com/jaeden_ai⁠

In this episode, discover how Tractable, the AI-powered insurance appraisal company, has secured an impressive $65 million in funding, with tech giant SoftBank leading the way. Dive into the fascinating world of AI in insurance, as we explore how Tractable's innovative technology is transforming the appraisal process and revolutionizing the insurance industry. Join us to unpack the potential implications of this substantial investment and the future of AI-driven insurance solutions. Get on the AI Box Waitlist: https://AIBox.ai/Join our ChatGPT Community: ⁠https://www.facebook.com/groups/739308654562189/⁠Follow me on Twitter: ⁠https://twitter.com/jaeden_ai⁠

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Soaking Beans - a cost-effectiveness analysis, published by NickLaing on August 6, 2023 on The Effective Altruism Forum. TLDR: On early-stage analysis, persuading people to soak their beans before cooking could cost-effectively save Sub-saharan Africans money, and modestly reduce carbon emissions. (great uncertainty) Introduction Across East Africa, hundreds of millions of people cook and eat beans multiple times every week. In Uganda where I live, beans make up an estimated 25% of the average Ugandan's calorie intake and 40% of their daily protein intake. Unfortunately cooking beans takes an absurd amount of time - usually two to three hours using charcoal or wood. The great news is that just soaking beans in water for 6-12 hours reduces cooking time by between 20% and 50% and has no negative effect on bean taste or nutrients . When we tested soaking vs. not soaking, cooking time reduced by half. Despite the obvious benefits of massively reduced cooking time using less fuel., very few people in Uganda soak their beans - nobody I know at least. I estimate under 0.5% of Ugandan families soak beans, but likely far less. I couldn't find any data on bean soaking habits in Uganda or Sub-Saharan Africa in general but I have heard anecdotally that it is common in some countries, perhaps Zimbabwe? (insider knowledge appreciated). Considering Uganda alone, Ugandans eat an estimated 10-20kg of beans per capita every year . Changing the behaviour of even a small percentage of Ugandans by convincing them to soak their beans, has potential benefits of reduced fuel burned, bringing about a range of environmental, economic and health impacts. Soaking beans could be IMPORTANT due to the potential environmental, economic and health benefits gained through reduced cooking time. It is NEGLECTED as no organizations we know of are working on mass media or other interventions to persuade people to soak beans. It may be TRACTABLE as people can immediately experience financial benefit from soaking beans through reduced expenditure on charcoal and time gathering firewood. Potential impact calculations Assumptions Uptake: For simplicity, we assume that it may be possible to persuade 1% of Ugandans to change their behavior and soak beans. This is just a guess at what could be the outcome of a moderately successful campaign. Fuel/time saving: We estimate a 25% time and fuel saving from soaking beans (ref) Time horizons: If someone starts soaking their beans, once benefits are clear and the change is locked in, it seems likely that they and their family will continue to soak for a long time, possibly even indefinitely. On the other hand, Uganda could electrify faster than expected making much of this analysis obsolete (unlikely), or Ugandans could start eating something other than beans (also unlikely). To be conservative, we have capped our analysis at 5 years of benefit from the campaign.Counterfactual: For the purposes of this analysis we assume that all of the 1% of Ugandans who will change behaviour to soak beans is due to our intervention. This is somewhat reasonable as there are no current efforts promoting bean soaking, and it is very unlikely people will change their behaviour without a specific promotion campaign CO2 emissions prevented through soaking Environmental impact could come through two avenues - CO2 equivalent emissions prevented, and deforestation prevented. Although benefits of preventing deforestation could potentially be large, it is difficult to calculate so here we only calculate the potential CO2 emissions prevented, first through reducing charcoal use, then through reducing woodfire user. Charcoal: CO2 equivalent saved by bean soaking About 1 in 3 Ugandans use charcoal for cooking. We estimate the Uganda-wide amount of charcoal use for cooking beans through 2 diffrent...

The U.S. construction industry faces record job openings, luxury leaders report mixed sales figures, HS2 rail link deemed 'unachievable', Nigerian banks increase salaries amid inflation, Tractable secures $65 million in funding, NTPC sees 9.4% increase in net income, Moscow skyscrapers targeted by drones, high-growth companies approach HR strategically, Worldcoin Protocol launch gains attention, Apple users experience issues with parental controls.

This is the AI News Briefing of July 21, 2023.(00:32) Apple unveils AI chatbot 'Apple GPT'(00:56) Debates over ChatGPT's performance degradation(01:31) OpenAI's ChatGPT introduces 'custom instructions'(01:51) AI Deals of the Week: Preply, Tractable, and moreFollow our newsletter at www.adepto.ai for a deeper dive into these fascinating developments and for the latest AI news and insights.The AI News Briefing has been produced by Adepto in cooperation with Wondercraft AI.Music: Inspire by Kevin MacLeod (incompetech.com), Licensed under Creative Commons: By Attribution 3.0 http://creativecommons.org/licenses/by/3.0/

In this episode, we delve into the recent $65M fundraising round of Tractable, an firm leveraging AI for insurance appraisals, led by investment giant SoftBank. We discuss the implications of this funding, the company's growth trajectory, and how its AI solutions are transforming the insurance industry. Get on the AI Box Waitlist: https://AIBox.ai/ Investor Contact Email: jaeden@aibox.ai Sponsor the Podcast: jaeden@fiund.com Facebook Community: ⁠https://www.facebook.com/groups/739308654562189/⁠ Discord Community: https://aibox.ai/discord Download Selfpause: https://selfpause.com/Podcast ⁠Inflection AI Report⁠

Earlier-than-expected first close of Isomer Capital's third flagship fund continues proven strategy of investing in Europe's best venture capital funds and their breakout companiesThe TL:DRIsomer Capital's track record and reputation as a ‘partnership' investor has enabled the firm's third flagship fund to reach a substantial first close ahead of schedule, despite headwinds in the macro-environment.Since inception, Isomer Capital has invested in more than 70 early-stage venture capital (VC) firms across Europe, gaining exposure to 29 unicorns to date, including the likes of UiPath in Romania, Tractable, Deliveroo, Oyster in the UK, ManoMano, Sorare in France, Wefox in Germany, and Dune Analytics in Norway.Isomer's proven hybrid fund of funds strategy is one of the best ways for institutional investors to access the high potential of European technology venture capital, by supporting European entrepreneurs creating the technology products of tomorrow. As a testament to this, IC II saw its net asset value (NAV) continue to increase in Q4 2022 against macro headwinds, maintaining its ranking in the top 5% of its kind globally.Isomer Capital has attracted investment from British Business Investments, the European Commission, and a range of endowments, pensions, corporates, and family offices from across Europe, Asia, and the United States.

Read the full transcript here. What are "variance-amplifying" and "variance-dampening" institutions? Has the world been getting weirder recently? Should entrepreneurs aim for variance amplification or variance dampening? What percentage of people should be entrepreneurs? What traits and skills are necessary for successful entrepreneurship? How has ambition changed over the course of history? How can entrepreneurs know if they're really changing the world, or just doing something slightly before someone else did it, or just doing something that would have happened anyway? How can entrepreneurs avoid getting mired in "tar pit" ideas?Matt Clifford MBE is cofounder and CEO of Entrepreneur First, the leading technology company builder that invests in top technical individuals to help them build world-class deep technology startups from scratch in six locations across Europe, Asia, and Canada. Since 2011, Entrepreneur First has created over 500 startups worth over $10bn including Magic Pony Technology, Tractable, and CloudNC. Matt is also Chairman of the UK's new Advanced Research and Invention Agency (ARIA), which aims to enable exceptional scientists and researchers to identify and fund transformational research that leads to new technologies, discoveries, products, and services. Matt sits on the board of Code First Girls, which he co-founded in 2013 to teach young women how to code, and is a member of the Innovate UK Council. Matt started his career at McKinsey & Co. and holds degrees from Cambridge and MIT, where he was a Kennedy Scholar. He was awarded an MBE for services to business in the 2016 Queen's Birthday Honours. Follow him on Twitter, interact with him on LinkedIn, or learn more about his work at Entrepreneur First.[Read more]

The special guest on the latest edition of The PR Week Podcast is Tractable's PR lead Norval Scott.The former Dow Jones and Globe & Mail journalist discusses the tech media sector, transitioning into PR and the lack of insight his newsroom colleagues have about the comms profession, plus contemporaneous news topics including:- Omnicom PR agency Q2 financial results, Ketchum hires Jim Joseph;- Dxtra firms move under IPG CEO Philippe Krakowsky following Andy Polansky retirement;- Minions reminds consumers Tupperware is still around;- Saudi Arabia chooses Edelman to help transform its global perception;- PRWeek Awards 2023 launches for entries; Corey duBrowa is chair of jury; - Nancy Elder moves to the New York Mets from Dazn;- And much more.Follow us on Twitter: @PRWeekUSReceive the latest industry news, insights, and special reports. Start Your Free 1-Month Trial Subscription To PRWeekhttps://forms.haymarketsubscribe.com/loading.do?omedasite=PRWeek_Land_Trial&pk=PODCAST22D

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Reducing aquatic noise as a wild animal welfare intervention, published by saulius on July 18, 2022 on The Effective Altruism Forum. Summary Aquatic noise comes from ships, seismic surveys to find oil and gas, sonar, and other sources. It causes marine animals stress and masks their communications, among other effects on marine ecosystems. Aquatic noise can be reduced by lobbying for international agreements to slow down ships, adopting various technologies, and protesting against seismic surveys. Usually, aquatic noise is considered an environmental or conservationist issue, and these seem to be the dominant concerns of the few organizations working on reducing aquatic noise. In this text, I analyzed whether animal advocates should work on decreasing aquatic noise as it potentially stresses wild fish. My tentative conclusion is that most likely, ocean noise interventions wouldn't be cost-effective compared to current farmed animal interventions, although there is some small chance that it's more cost-effective than them. I attempted to analyzed the cost-effectiveness of a campaign prevent seismic surveys to find oil and gas, but conclusions seem too uncertain to inform decisions. Pursuing wild animal welfare (WAW) interventions that are also good for the environment and conserving species could help the WAW movement find allies in these important fields. Also, it might be good for the reputation of the WAW movement to have a concrete intervention that they could to point to. Context and conclusions I've spent some months trying to find a wild animal welfare (WAW) intervention that is: Tractable (can in principle be funded >$100K/yr starting in 2023 even if we choose not to do so), Non-controversial (>40% support and

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Perform Tractable Research While Avoiding Capabilities Externalities [Pragmatic AI Safety #4], published by Dan Hendrycks on May 30, 2022 on The AI Alignment Forum. This is the fourth post in a sequence of posts that describe our models for Pragmatic AI Safety. We argued in our last post that the overall AI safety community ought to pursue multiple well-reasoned research directions at once. In this post, we will describe two essential properties of the kinds of research that we believe are most important. First, we want research to be able to tractably produce tail impact. We will discuss how tail impact is created in general, as well as the fact that certain kinds of asymptotic reasoning exclude valuable lines of research and bias towards many forms of less tractable research. Second, we want research to avoid creating capabilities externalities: the danger that some safety approaches produce by way of the fact that they may speed up AGI timelines. It may at first appear that capabilities are the price we must pay for more tractable research, but we argue here and in the next post that these are easily avoidable in over a dozen lines of research. Strategies for Tail Impact It's not immediately obvious how to have an impact. In the second post in this sequence, we argued that research ability and impact is tail distributed, so most of the value will come from the small amount of research in the tails. In addition, trends such as scaling laws may make it appear that there isn't a way to “make a dent” in AI's development. It is natural to fear that the research collective will wash out individual impact. In this section, we will discuss high-level strategies for producing large or decisive changes and describe how they can be applied to AI safety. Processes that generate long tails and step changes Any researcher attempting to make serious progress will try to maximize their probability of being in the tail of research ability. It's therefore useful to understand some general mechanisms that tend to lead to tail impacts. The mechanisms below are not the only ones: others include thresholds (e.g. tipping points and critical mass). We will describe three processes for generating tail impacts: multiplicative processes, preferential attachment, and the edge of chaos. Multiplicative processes Sometimes forces are additive, where additional resources, effort, or expenditure in any one variable can be expected to drive the overall system forward in a linear way. In cases like this, the Central Limit Theorem often holds, and we should expect that outcomes will be normally distributed–in these cases one variable tends not to dominate. However, sometimes variables are multiplicative or interact nonlinearly: if one variable is close to zero, increasing other factors will not make much of a difference. In multiplicative scenarios, outcomes will be dominated by the combinations of variables where each of the variables is relatively high. For example, adding three normally distributed variables together will produce another normal distribution with a higher variance; multiplying them together will produce a long-tailed distribution. As a concrete example, consider the impact of an individual researcher with respect to the variables that impact their work: time, drive, GPUs, collaborators, collaborator efficiency, taste/instincts/tendencies, cognitive ability, and creativity/the number of plausible concrete ideas to explore. In many cases, these variables can interact nonlinearly. For example, it doesn't matter if a researcher has fantastic research taste and cognitive ability if they have no time to pursue their ideas. This kind of process will produce long tails, since it is hard for people to get all of the many different factors right (this is also the case in startups). The impl...

Richad Nieves-Becker is a self-taught data scientist with an eclectic background. He currently leads the data science function at Revantage, a real estate shared service organization in the Blackstone family. He got a BA in Neuroscience and Anthropology. And was on the PhD path until he realized it was not for him. He pivoted and earned a Masters in Commerce from the University of Virginia. He started at CoreLogic focusing on text mining, then moved to Greystone leading all things data in an innovation lab. He credits his career progress to focusing on impact and deeply understanding the business case. In this episode, we cover a range of topics including:- His entry into data science- Academic vs business work- How he cold emailed his way to getting job interviews- Why data scientists need sales skills- How data scientists should think about building a portfolio- Defining tractable problems in machine learning and data science- Moving from mathematics to data science- Framework for creating educational content- Creating a course for data scientists- How he interviews people- The report he's writing for new data science leaders- Building good culture by aligning an individual's desires to the company's goals- The advantage of modular products- Rise of MLOps and data versioning- Monetization of models

3月16日、第277回の放送。 ゲスト

Alex Burkan est un inventeur, mais aussi un Youtubeur. C'est en travaillant sur des équipements de production d'hydrogène que l'idée lui est venue. Pendant dix ans, l'inventeur a réalisé des centaines d'expériences avant d'arriver à cet objet final. Le sabre laser est loin d'être un simple jouet, il peut chauffer jusqu'à 2 800 degrés.En fin de podcast, Imane Bounar annonce le retour du jeu Wii Sports, il s'appellera désormais Nintendo Switch Sports et sortira le 29 avril prochain. Notre politique de confidentialité GDPR a été mise à jour le 8 août 2022. Visitez acast.com/privacy pour plus d'informations.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: What can we learn from a short preview of a super-eruption and what are some tractable ways of mitigating it, published by Mike Cassidy on February 3, 2022 on The Effective Altruism Forum. On the 15 January 2022, the partially submerged Hunga Tonga-Hunga Ha'apai volcano (~

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Wikipedia editing is important, tractable, and neglected, published by Darius_M on the Effective Altruism Forum. 1. Key Takeaways The case for Wikipedia editing in a nutshell: Wikipedia articles are widely read and trusted, there is much low hanging fruit for improvement, and editing Wikipedia has low barriers to entry and is relatively low effort. Consequently, improving a Wikipedia article may benefit the reasoning and actions of its thousands, and often millions, of readers. Moreover, since Wikipedia is a global public good, improvements to Wikipedia are likely undersupplied relative to the socially optimal level. Careful prioritisation is crucial. Improving or creating some Wikipedia articles could easily be 100x to 1,000x as valuable as others. The key factors to consider for prioritisation are (i) pageviews, (ii) audience, (iii) topic, (iv) room for improvement, and (v) language. Respecting Wikipedia community rules and norms is key. The Wikipedia community is wary of people making edits to promote a particular idea, person, or organisation, especially when there are relevant conflicts of interest. Consequently, edits that violate Wikipedia rules and norms may be actively harmful and are likely to be deleted. However, there are currently still very many genuine gaps in the quality and coverage of Wikipedia articles, and filling these gaps tends to work well and is regarded highly. Contributing to or starting a WikiProject on an important topic may be valuable. A WikiProject is a group of contributors who want to work together as a team to improve Wikipedia. A WikiProject allows for more efficient collaboration, by providing a centralised place where interested editors can make plans and discuss proposals. There are self-interested reasons to edit Wikipedia. In particular, Wikipedia editing can be really fun, it is a great opportunity to learn more about a topic, it may help you improve your writing, and it may be a useful signal in some communities or for some professional opportunities. Some EA-relevant content is better suited to a specialised EA Wiki than to Wikipedia. For instance, content that is too niche to meet Wikipedia's notability requirements. Please note that much of this post is not original, drawing on existing writing (see the “Relevant Resources” section). However, I felt it was important to add to, synthesise and popularise these ideas here on the forum. Any mistakes are my own. 2. Respecting Wikipedia Rules Before giving the positive argument for Wikipedia editing, I want to stress the importance of becoming familiar with and respecting the rules and norms governing Wikipedia editing. Lack of familiarity with the relevant rules and norms is one of the main reasons editors have their contributions reverted. The most important ones include: Neutral point of view: “All Wikipedia articles (...) must be written from a neutral point of view, representing significant views fairly, proportionately and without bias.” Verifiability: “Material challenged or likely to be challenged, and all quotations, must be attributed to a reliable, published source.” No original research: “Wikipedia does not publish original thought (...) Articles may not contain any new analysis or synthesis of published material that serves to advance a position not clearly advanced by the sources.” Notability: “Article and list topics must be notable, or “worthy of notice”. (...) if no reliable, independent sources can be found on a topic, then it should not have a separate article.” Conflict of interest (COI): “COI editing involves contributing to Wikipedia about yourself, family, friends, clients, employers, or your financial and other relationships. (...) COI editing is strongly discouraged on Wikipedia.” Paid-contribution disclosure: “If you are paid in any way for contributin...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Space governance is important, tractable and neglected , published by Tobias_Baumann on the Effective Altruism Forum. Summary I argue that space governance has been overlooked as a potentially promising cause area for longtermist effective altruists. While many uncertainties remain, there is a reasonably strong case that such work is important, time-sensitive, tractable and neglected, and should therefore be part of the longtermist EA portfolio. I also suggest criteria for what good space governance should look like, and outline possible directions for further work on the topic. What is space governance? It's plausible that humans, or their successors, will eventually be able to colonise space. There are already various Mars missions, and future technological advances might make large-scale colonisation economically feasible. Space governance encompasses the laws, rules, norms and institutions that structure interactions in space, as well as mechanisms that are used to establish and enforce those. For the purposes of this post, we're interested in a subset of space governance that I will call long-term space governance. Long-term space governance refers to the processes of interaction and decision-making among the actors involved in the large-scale settlement of space. Space colonization is currently not well covered by existing governance mechanisms. The most significant treaty in internal space law is the Outer Space Treaty, signed in 1967, which establishes that space shall be free for exploration and use by all nations, but that no nation may claim sovereignty of outer space or any celestial body.[1] Subsequent efforts to establish more comprehensive rules, such as the Moon Treaty (which grants jurisdiction over celestial bodies to the international community), have largely failed to achieve widespread assent. Therefore, we currently lack a coherent global framework for space governance. As of now, space is a free-for-all.[2] This is particularly true for challenges that arise in the context of humanity expanding beyond Earth: large-scale settlements in space are currently infeasible, so much of the existing debate centers on more immediate concerns (e.g. related to satellites or exploration of space). The work I have in mind aims to replace the current state of ambiguity with a coherent framework of (long-term) space governance that ensures good outcomes if and when large-scale space colonisation becomes feasible. In the following, I will argue that such work is important, tractable, and neglected. Importance The case for the importance of space governance is straightforward: it directly affects astronomical stakes. On a cosmic scale, Earth is a tiny point in a vast universe containing hundreds of billions of galaxies. Our own galaxy, the Milky Way, already contains at least 100 billion planets. So, while space governance is not fundamentally different from existing governance problems, it takes place on a scale never before seen in human history. Also, the range of possible outcomes is huge. The right space governance regime could enable an outcome that is very good from (almost) every perspective - through positive-sum cooperation and compromise between the relevant actors, combined with the vast amount of resources that an intergalactic civilisation can access. (Cf. Eric Drexler's Paretotopia.) On the other side of the spectrum, escalating conflicts and warfare on a cosmic level could cause actors to inflict unimaginable horrors on each other, resulting in suffering on an astronomical scale. That said, one could object that anything we can do now will be overturned in the future, rendering our efforts irrelevant. In particular, one might expect transformative AI to happen relatively soon (which may be the trigger for large-scale space colonisation), and power...

I'd like to introduce you to The Entrepreneur First Podcast, my new show with EF co-founder Alice Bentinck.Together, we've been talking to some of the brightest entrepreneurs who have ever set foot in an EF office about what it takes to build a business from the ground up.In this episode I speak to Alex Dalyac, co-founder and CEO of Tractable, EF's first unicorn. We're joined by LinkedIn's co-founder and Chairman, Reid Hoffman.Reid and Alex share what they believe are the most important tenets of founding a successful business - particularly in the earliest stages - and how aspiring founders can launch a startup heading for unicorn status.This is the only episode of The Entrepreneur First Podcast that is going to appear on this feed.  To hear more, search for The Entrepreneur First Podcast wherever you listen.Thanks to Cofruition for consulting on and producing the show. You can learn more about Entrepreneur First at www.joinef.com.

Our guest today is Alice Bentinck, co-founder of Entrepreneur First.EF (as it's more commonly known) celebrate their tenth year this week and co-founders Alice & Matt have gone from strength to strength - even fostering their first billion-dollar company in Tractable … which also happens to be the UK's 100th unicorn.But it has not been plain sailing - as you'll hear today.There are many myths around entrepreneurship that I try to challenge on this podcast, one of which is that entrepreneurs are born not created. I believe it has never been easier to be an entrepreneur and is now a much more credible career choice than even 10 years ago … However,  the number of tools and options available can also make it more difficult to know where to turn.EF looks to solve this by matching you up with a co-founder and learning the basics of entrepreneurship from some of the very best …and we are probably just at the beginning of their journey.A big thank you to all you listeners for powering us into the top 15 of Apple business podcasts ….If you are enjoying the series, it makes a massive difference if you could rate us on iTunes.It is wonderful to read reviews such as the one by Charles Fletcher of Navigate Politics or hearing from people like Eben Owen who has recently got a job having been inspired by the guests on the podcast.You can find out more on our website or get in touch via hello@jobsofthefuture.coYou can follow us on social media:Instagram: @JimmysjobsTwitter: @JimmysjobsAnd most importantly on LinkedInIf you'd like to see more information about the job roles being offered please look at my Twitter @jimmym

Ash Fontana became one of the most recognized startup investors in the world after launching online investing at AngelList. He then became a Managing Director of Zetta, the first investment fund that focused on AI. The firm was the lead investor in category-defining AI companies such as Kaggle, Domino, Tractable, Lilt and Invenia. He has appeared in Fast Company, Bloomberg, Forbes, CNBC and at the UN. Ash previously co-founded Topguest, a Founders Fund-backed company that built customer analytics technology for companies like United, Virgin, and InterContinental. Topguest sold in an eight-figure transaction 18 months after the company was founded. From his experiences, he's written his first book, The AI-First Company, the definitive playbook to putting AI first in every business conversation. The playbook is an executable guide for applying AI to business problems, made for real companies, with real budgets, that need strategies and tactics to effectively implement AI. In this podcast, we'll dive into the topics from his book and really understand how you can apply these concepts to infuse AI in your organization. Ash will share with us why the concept we often hold about AI—a big brain in the sky—isn't accurate, and how we should be thinking of AI. He'll also define what it means to be an “AI-First company” and lead us through practical steps you can take now to start moving your organization on the path being an AI leader.__________________________________________________________________________________________"[AI] is very good at discrete things like making the same decision over and over again, very reliably with a predictable output or making very rational decisions or whatnot. So, I think it's important to just remember it's different from our form of intelligence. And that's why it's important to develop, because if it was the same why would we be bothering with all this."-Ash Fontana__________________________________________________________________________________________Episode Timeline:01:06—Introducing Ash Fontana + The topic of today's episode3:06—What is an AI-first company?4:49—How do you describe AI?5:50—Is AI less adaptable than humans at making decisions when the parameters or underlying ideas suddenly shift?7:16—Could you explain how flywheel concept as it relates to AI systems?9:08—How would a legacy or incumbent company approach where to start with AI?10:59—How can a company shift from a lean approach to being "all in"?13:14— What is some of the languages or some of the words that we need to start learning to grasp AI?14:49—What do companies most often get wrong when seeking to prioritize AI? 17:10—What are some resources or a place to start for companies looking to make the first steps?19:20—Where can we find you?__________________________________________________________________________________________Additional Resources:https://www.linkedin.com/in/ashfontana/The AI-First Company(Book)

Ash Fontana became one of the most recognized startup investors in the world after launching online investing at AngelList. He then became a Managing Director of Zetta, the first investment fund that focused on AI. The firm was the lead investor in category-defining AI companies such as Kaggle, Domino, Tractable, Lilt and Invenia. He has appeared in Fast Company, Bloomberg, Forbes, CNBC and at the UN. Ash previously co-founded Topguest, a Founders Fund-backed company that built customer analytics technology for companies like United, Virgin, and InterContinental. Topguest sold in an eight-figure transaction 18 months after the company was founded. From his experiences, he's written his first book, The AI-First Company, the definitive playbook to putting AI first in every business conversation. The playbook is an executable guide for applying AI to business problems, made for real companies, with real budgets, that need strategies and tactics to effectively implement AI. In this podcast, we'll dive into the topics from his book and really understand how you can apply these concepts to infuse AI in your organization. Ash will share with us why the concept we often hold about AI—a big brain in the sky—isn't accurate, and how we should be thinking of AI. He'll also define what it means to be an “AI-First company” and lead us through practical steps you can take now to start moving your organization on the path being an AI leader.__________________________________________________________________________________________"[AI] is very good at discrete things like making the same decision over and over again, very reliably with a predictable output or making very rational decisions or whatnot. So, I think it's important to just remember it's different from our form of intelligence. And that's why it's important to develop, because if it was the same why would we be bothering with all this."-Ash Fontana__________________________________________________________________________________________Episode Timeline:01:06—Introducing Ash Fontana + The topic of today's episode3:06—What is an AI-first company?4:49—How do you describe AI?5:50—Is AI less adaptable than humans at making decisions when the parameters or underlying ideas suddenly shift?7:16—Could you explain how flywheel concept as it relates to AI systems?9:08—How would a legacy or incumbent company approach where to start with AI?10:59—How can a company shift from a lean approach to being "all in"?13:14— What is some of the languages or some of the words that we need to start learning to grasp AI?14:49—What do companies most often get wrong when seeking to prioritize AI? 17:10—What are some resources or a place to start for companies looking to make the first steps?19:20—Where can we find you?__________________________________________________________________________________________Additional Resources:https://www.linkedin.com/in/ashfontana/The AI-First Company(Book)

Welcome to. Strategy Skills episode 159, an episode with Ash Fontana on the future of AI. Ash just published a great book on AI, THE AI-FIRST COMPANY, please see a link below. THE AI-FIRST COMPANY: https://amzn.to/3zRYW7o Among other insights, Fontana shows readers how to: Make AI your company's first priority. Identify the most valuable data. Build AI teams. Create AI budgets. Integrate AI with existing processes. Measure its effectiveness. Reinvest the profits from automation to build a competitive advantage. About the Author Ash Fontana became one of the most recognized startup investors in the world after launching online investing at AngelList. He then became a Managing Director of Zetta Venture Partners, the first investment fund that focused on AI. The firm was the lead investor in category-defining AI companies such as Kaggle, Domino, Tractable, Lilt and Invenia.  Enjoying our podcast? Get access to sample advanced training episodes here: www.firmsconsulting.com/promo We use affiliate links whenever possible (if you purchase items listed above using our affiliate links, we will get a bonus).

Discussing AI-First Company and AI First mentality with Ash Fontana. He sheds light on how organizations could embrace analytics, data, and AI to retain a competitive edge. Bio: Ash Fontana became one of the most recognized startup investors in the world after launching online investing at AngelList. He then became a Managing Director of Zetta, the first investment fund that focused on AI. The firm was the lead investor in category-defining AI companies such as Kaggle, Domino, Tractable, Lilt, and Invenia. He has appeared in Fast Company, Bloomberg, Forbes, CNBC, and at the UN. This is his first book. Ash's Book: The AI-First Company: How to Compete and Win with Artificial Intelligence by Ash Fontana https://amzn.to/33C2OL5 Ash's Recommendations: On Intelligence: How a New Understanding of the Brain Will Lead to the Creation of Truly Intelligent Machines by Jeff Hawkins, Sandra Blakeslee https://amzn.to/3vZhksk Neurophilosophy: Toward a Unified Science of the Mind-Brain by Patricia S. Churchland https://amzn.to/2RiKrYO Discussion Timeline: TIMELINE Some questions we covered: 1. Starter: Give your starter pitch 1 point that this book points to: 2. Vishal briefly introduce guest Stage 2: Subject Matter Expertise 3. What is the state of startups today? 4. State of AI in mature organization? 5. AI and Enterprise outlook? Cautionary tale or hopeful story 6. Who will win the AI race? 7. Challenges in AI adoption? Stage 3: Introduction as an author 8. What is an AI-First company? 9. Why write AI First? 10. Why does every company need to prioritize AI over the next decade? 11. What are the most common mistakes companies make when trying to become AI First? 12. What's the difference between “lean-startup” and “lean AI”? 14. How do AI-First companies retain more of the “first mover” advantage than others? 15. AI + Business, will make it more science or art? 16. Can AI be a competitive edge Stage 4: Rapid Fire with Ben Pring [Say what comes to your mind] 17 a. #MachineLearning 17 b. #Technology 17 c. #Leadership 17 d. #FutureOfWork 17 e. #Culture 17 f. #DigitalTransformation 17 g. #Disruption 17 h. #JobsOfFuture 17 i. #FutureofStartup 17 j. #FutureofOrganization 17 k. #AIFirst Stage 5: Closing 18. What are 1-3 best practices that you think are the key to success in your journey? 19. Do you have any favorite read? 20. As a closing remark, what would you like to tell our audience? About TAO.ai[Sponsor]: TAO is building the World's largest and AI-powered Skills Universe and Community powering career development platform empowering some of the World's largest communities/organizations. Learn more at https://TAO.ai About WorkPod: Work Pod takes you on the journey with leaders, experts, academics, authors, and change-makers designing the future of work, workers, and the workplace. About Work2.org WorkPod is managed by Work2.org, a #FutureOfWork community for HR and Organization architects and leaders. Sponsorship / Guest Request should be directed to info@tao.ai Keywords: #FutureofWork #Work2.0 #Work2dot0 #Leadership #Growth #Org2dot0 #Work2 #Org2

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Discussing AI First Company and AI First mentality with Ash Fontana. He sheds light on how organizations could embrace analytics, data and AI to retain competitive edge. Bio: Ash Fontana became one of the most recognized startup investors in the world after launching online investing at AngelList. He then became a Managing Director of Zetta, the first investment fund that focused on AI. The firm was the lead investor in category-defining AI companies such as Kaggle, Domino, Tractable, Lilt and Invenia. He has appeared in Fast Company, Bloomberg, Forbes, CNBC and at the UN. This is his first book. Ash's Book: The AI-First Company: How to Compete and Win with Artificial Intelligence by Ash Fontana https://amzn.to/33C2OL5 Ash's Recommendations: On Intelligence: How a New Understanding of the Brain Will Lead to the Creation of Truly Intelligent Machines by Jeff Hawkins, Sandra Blakeslee https://amzn.to/3vZhksk Neurophilosophy: Toward a Unified Science of the Mind-Brain by Patricia S. Churchland https://amzn.to/2RiKrYO Discussion Timeline: TIMELINE Some questions we covered: 1. Starter: Give your starter pitch 1 point that this book points to: 2. Vishal briefly introduce guest Stage 2: Subject Matter Expertise 3. What is the state of startups today? 4. State of AI in mature organization? 5. AI and Enterprise outlook? Cautionary tale or hopeful story 6. Who will win the AI race? 7. Challenges in AI adoption? Stage 3: Introduction as an author 8. What is an AI-First company? 9. Why write AI-First? 10. Why does every company need to prioritize AI over the next decade? 11. What are the most common mistakes companies make when trying to become AI-First? 12. What's the difference between “lean-startup” and “lean AI”? 14. How do AI-First companies retain more of the “first mover” advantage than others? 15. AI + Business, will make it more science or art? 16. Can AI be a competitive edge Stage 4: Rapid Fire with Ben Pring [Say what comes to your mind] 17 a. #MachineLearning 17 b. #Technology 17 c. #Leadership 17 d. #FutureOfWork 17 e. #Culture 17 f. #DigitalTransformation 17 g. #Disruption 17 h. #JobsOfFuture 17 i. #FutureofStartup 17 j. #FutureofOrganization 17 k. #AIFirst Stage 5: Closing 18. What are 1-3 best practices that you think are the key to success in your journey? 19. Do you have any favorite read? 20. As a closing remark, what would you like to tell our audience? About TAO.ai[Sponsor]: TAO is building the World's largest and AI-powered Skills Universe and Community powering career development platform empowering some of the World's largest communities/organizations. Learn more at https://TAO.ai About FutureOfData: FutureOfData takes you on the journey with leaders, experts, academics, authors, and change-makers designing the future of data, analytics, and insights. About AnalyticsWeek.com FutureOfData is managed by AnalyticsWeek.com, a #FutureOfData Leadership community of Organization architects and leaders. Sponsorship / Guest Request should be directed to info@tao.ai Keywords: #FutureofData #Work2.0 #Work2dot0 #Leadership #Growth #Org2dot0 #Work2 #Org2

Show Notes(02:06) Azin described her childhood growing up in Iran and going to a girls-only high school in Tehran designed specifically for extraordinary talents.(05:08) Azin went over her undergraduate experience studying Computer Science at the University of Tehran.(10:41) Azin shared her academic experience getting a Computer Science MS degree at the University of Toronto, supervised by Babak Taati and David Fleet.(14:07) Azin talked about her teaching assistant experience for a variety of CS courses at Toronto.(15:54) Azin briefly discussed her 2017 report titled “Barriers to Adoption of Information Technology in Healthcare,” which takes a system thinking perspective to identify barriers to the application of IT in healthcare and outline the solutions.(19:35) Azin unpacked her MS thesis called “Subspace Selection to Suppress Confounding Source Domain Information in AAM Transfer Learning,” which explores transfer learning in the context of facial analysis.(28:48) Azin discussed her work as a research assistant at the Toronto Rehabilitation Institute, working on a research project that addressed algorithmic biases in facial detection technology for older adults with dementia.(33:02) Azin has been an Applied Research Scientist at Georgian since 2018, a venture capital firm in Canada that focuses on investing in companies operating in the IT sectors.(38:20) Azin shared the details of her initial Georgian project to develop a robust and accurate injury prediction model using a hybrid instance-based transfer learning method.(42:12) Azin unpacked her Medium blog post discussing transfer learning in-depth (problems, approaches, and applications).(48:18) Azin explained how transfer learning could address the widespread “cold-start” problem in the industry.(49:50) Azin shared the challenges of working on a fintech platform with a team of engineers at Georgian on various areas such as supervised learning, explainability, and representation learning.(51:46) Azin went over her project with Tractable AI, a UK-based company that develops AI applications for accident and disaster recovery.(55:26) Azin shared her excitement for ML applications using data-efficient methods to enhance life quality.(57:46) Closing segment.Azin’s Contact InfoWebsiteTwitterLinkedInGoogle ScholarGitHubMentioned ContentPublications“Barriers to Adoption of Information Technology in Healthcare” (2017)“Subspace Selection to Suppress Confounding Source Domain Information in AAM TransferLearning” (2017)“A Hybrid Instance-based Transfer Learning Method” (2018)“Prediction of Workplace Injuries” (2019)“Algorithmic Bias in Clinical Populations — Evaluating and Improving Facial Analysis Technology in Older Adults with Dementia” (2019)“Limitations and Biases in Facial Landmark Detection” (2019)Blog Posts“An Introduction to Transfer Learning” (Dec 2018)“Overcoming The Cold-Start Problem: How We Make Intractable Tasks Tractable” (April 2021)PeopleYoshua Bengio (Professor of Computer Science and Operations Research at University of Montreal)Geoffrey Hinton (Professor of Computer Science at University of Toronto)Louis-Philippe Morency (Associate Professor of Computer Science at Carnegie Mellon University)Book“Machine Learning: A Probabilistic Approach” (by Kevin Murphy)Note: Azin and her collaborator are going to give a talk at ODSC Europe 2021 in June about a Georgian’s project with a portfolio company, Tractable. They have written a short blog post about it too which you can find HERE.

Show Notes(02:06) Azin described her childhood growing up in Iran and going to a girls-only high school in Tehran designed specifically for extraordinary talents.(05:08) Azin went over her undergraduate experience studying Computer Science at the University of Tehran.(10:41) Azin shared her academic experience getting a Computer Science MS degree at the University of Toronto, supervised by Babak Taati and David Fleet.(14:07) Azin talked about her teaching assistant experience for a variety of CS courses at Toronto.(15:54) Azin briefly discussed her 2017 report titled “Barriers to Adoption of Information Technology in Healthcare,” which takes a system thinking perspective to identify barriers to the application of IT in healthcare and outline the solutions.(19:35) Azin unpacked her MS thesis called “Subspace Selection to Suppress Confounding Source Domain Information in AAM Transfer Learning,” which explores transfer learning in the context of facial analysis.(28:48) Azin discussed her work as a research assistant at the Toronto Rehabilitation Institute, working on a research project that addressed algorithmic biases in facial detection technology for older adults with dementia.(33:02) Azin has been an Applied Research Scientist at Georgian since 2018, a venture capital firm in Canada that focuses on investing in companies operating in the IT sectors.(38:20) Azin shared the details of her initial Georgian project to develop a robust and accurate injury prediction model using a hybrid instance-based transfer learning method.(42:12) Azin unpacked her Medium blog post discussing transfer learning in-depth (problems, approaches, and applications).(48:18) Azin explained how transfer learning could address the widespread “cold-start” problem in the industry.(49:50) Azin shared the challenges of working on a fintech platform with a team of engineers at Georgian on various areas such as supervised learning, explainability, and representation learning.(51:46) Azin went over her project with Tractable AI, a UK-based company that develops AI applications for accident and disaster recovery.(55:26) Azin shared her excitement for ML applications using data-efficient methods to enhance life quality.(57:46) Closing segment.Azin’s Contact InfoWebsiteTwitterLinkedInGoogle ScholarGitHubMentioned ContentPublications“Barriers to Adoption of Information Technology in Healthcare” (2017)“Subspace Selection to Suppress Confounding Source Domain Information in AAM TransferLearning” (2017)“A Hybrid Instance-based Transfer Learning Method” (2018)“Prediction of Workplace Injuries” (2019)“Algorithmic Bias in Clinical Populations — Evaluating and Improving Facial Analysis Technology in Older Adults with Dementia” (2019)“Limitations and Biases in Facial Landmark Detection” (2019)Blog Posts“An Introduction to Transfer Learning” (Dec 2018)“Overcoming The Cold-Start Problem: How We Make Intractable Tasks Tractable” (April 2021)PeopleYoshua Bengio (Professor of Computer Science and Operations Research at University of Montreal)Geoffrey Hinton (Professor of Computer Science at University of Toronto)Louis-Philippe Morency (Associate Professor of Computer Science at Carnegie Mellon University)Book“Machine Learning: A Probabilistic Approach” (by Kevin Murphy)Note: Azin and her collaborator are going to give a talk at ODSC Europe 2021 in June about a Georgian’s project with a portfolio company, Tractable. They have written a short blog post about it too which you can find HERE.

Artificial intelligence has opened up a wealth of opportunities for both insurers and insurtechs, but turning AI’s vast potential into scalable, successful technology solutions is a major challenge. Tractable is a great example of a company that has managed to do it, with its AI solution for assessing vehicle damage now being used worldwide to process thousands of claims every day. Co-founder and President Adrien Cohen joins Matthew to discuss Tractable’s global growth, the challenges of working with AI, and what the company has planned for property insurance. Talking points include: Why insurance is a great use case for AI Data challenges and training AI systems Why insurers struggle to develop solutions in-house Spotting opportunities and expanding into different markets Convincing customers about new technology Sign up to the InsTech London newsletter for a fresh view on the world every Wednesday morning. If you like what you're hearing, please leave us a review on whichever platform you use, or contact Matthew Grant on LinkedIn. Continuing Professional Development - Learning Objectives InsTech London is accredited by The Chartered Insurance Institute (CII). By listening to any InsTech London podcast or reading the accompanying transcript, you can claim up to 0.5 hours towards the CII member CPD scheme. To claim 0.5 hours for this podcast, go to the Episode 134 page of the InsTech London website, or email cpd@instech.london.

MLOps community meetup #43! Last Wednesday, we talked to Nathan Benaich, General Partner at Air Street Capital and Timothy Chen, Managing Partner at Essence VC about The MLOps Landscape. // Abstract: In this session, we explored the MLOps landscape through the eyes of two accomplished investors. Tim And Nathan shared with us their experience in looking at hundreds of ML and MLOps companies each year to highlight major insights they have gained. What do the ML infrastructure and tooling landscape look like at the moment? Where have they been seeing patterns emerge? What do they expect to see happen within the market in the next couple of years? What current tools out there are the most interesting to them? And last but not least how do they go about selecting which companies to invest in. // Bio: Nathan Benaich is the Founder and General Partner of Air Street Capital, a venture capital firm investing in early-stage AI-first technology and life science companies. The team’s investments include Mapillary (Acq. Facebook), Graphcore, Thought Machine, Tractable, and LabGenius. Nathan is Managing Trustee of The RAAIS Foundation, a non-profit with a mission to advance education and open-source research in common good AI. This includes running the annual RAAIS summit and funding fellowships at OpenMined. Nathan is also co-author of the annual State of AI Report. He holds a PhD in cancer biology from the University of Cambridge and a BA from Williams College. Timothy Chen is the Managing Partner at Essence VC, with a decade of experience leading engineering in enterprise infra and open source communities/companies. Prior to Essence, Tim was the SVP of Engineering at Cosmos, a popular open-source blockchain SDK. Prior to Cosmos, Tim cofounded Hyperpilot with Stanford Professor Christos Kozyrakis which later exited to Cloudera. Prior to Hyperpilot, Tim was an early employee at Mesosphere and CloudFoundry. Tim is also active in the open-source space as an Apache member. // Final thoughts Please feel free to drop some questions you may have beforehand into our slack channel (https://go.mlops.community/slack) Watch some old meetups on our youtube channel: https://www.youtube.com/channel/UCG6qpjVnBTTT8wLGBygANOQ ----------- Connect With Us ✌️------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Nathan on LinkedIn: https://www.linkedin.com/in/nathanbenaich/ Connect with Tim on LinkedIn: https://www.linkedin.com/in/timchen

The Trends that Keep the Collision Repair Industry Up at Night  So many of the gadgets we use in our home , like GOOGLE NEST products that learn our behaviors -so does it go for the automobile . MS&AD, the fifth largest property and casualty insurer globally, will use artificial intelligence (AI) technology from Tractable to analyze photos of vehicle damage on auto insurance claims in Japan, speeding up recovery for its policyholders -how does this help both the consumer and body shop technician ?  The shift toward increased electrification of vehicles means those working at automotive repair facilities will need thorough training on how to avoid safety concerns, especially with high voltage batteries and related systems -how is the collision industry preparing for this & are student technicians receiving the basics here ?  Education & The Shortage is Real  Collision repair training programs graduate only 10 to 15% of their students, which means that only 10,000 of those currently enrolled will achieve a certificate, associate degree, or other formal designation--what is the key to improving the percentage rate of graduates ? School Grant Opportunities https://bit.ly/2UwI1UI Student Scholarship & Grant Opportunities https://bit.ly/3pEDb6f The Hire Our Heroes program supports military veterans and family members of veterans who are studying collision repair in a local high school or technical college. Brandon Eckonrode , Collision Repair Education Foundation Director of Development stops by this edition of Wrench Nation Car Talk  http://bit.ly/WrenchNation to help navigate the future success of the collision technician .  Your help is needed for the many students who are looking for an opportunity in the collision industry -take a moment and sign up for the Collision Education Virtual Golf Fundraiser https://bit.ly/35wQJZe  Please support our great sponsors who help us keep our show innovative & informative & the lights on ! Direct Mail ~ MAIL SHARK ➡️ http://bit.ly/2TBfWw6 BG Products ➡️ http://bit.ly/3dbgnVi Bolton Technology ~ Digital Inspections ➡️ http://bit.ly/2IE6zHD The Parts Authority ▶️ http://bit.ly/PartsAuthorityLocations Vision Collision ▶️ https://bit.ly/2L8rwJy Anytime Auto Glass & Tint ▶️ https://bit.ly/3jwfI3o Pronto Auto Parts https://bit.ly/35yQ48B  

The Hunt for the Wilderpeople! A secret gem of movies. Directed by the same guy that did JoJo Rabbit, so you know it's gonna be good. It takes a serious situation, sprinkles it with some satirical humor that makes you fall in love with the characters. Need a weekend movie recommendation? I got you.  --- Send in a voice message: https://anchor.fm/grevocab/message Support this podcast: https://anchor.fm/grevocab/support

This is the 33rd episode of FintechFlow where we dive deep into artificial intelligence. AI is one of the most used buzzwords these days, and now we will look deeper into it to see how an insurance company could really utilize it. I have two guests representing two very different AI driven insurtech startups today. Both are trying to tackle a concrete business case on the insurace value chain and to prove that their AI solution is a complete game changer. One of the startups, Tractable develops artificial intelligence for accident & disaster recovery. Their AI looks at photos of damage and predicts repair costs.12 of the top 40 motor insurance companies worldwide are amongst their clients and they have raised $55m in venture capital from world-leading VC funds. The other company is Zelros. Their mission is to enable insurance players to revolutionize and re-enchant their relationship with their customers by letting them take ownership of AI. This episode is brought to you by the Digital Insurance Agenda (DIA), the best run insurance related conference I have ever experienced.

Welcome to the 46th Episode of The Two Tankers and A Cat Podcast!  The M18 Hellcat was an American tank destroyer of World War II, also used in the Korean War.  It was the fastest U.S. armored fighting vehicle on the road.  Listen to this episode to hear us talk about the concept of shoot and scoot!  The second point we talk about in this episode is Operation Tractable!  Oh yeah, Charlie learns there are no dingoes in New Zealand :)  Tune in to this episode to learn about some great tank history.  If you have any questions or comments make sure you shoot us a message on our Facebook page or at our email address:  twotankersandcat@gmail.com  If you would like to support our podcast monetarily, check out our Patreon Page, https://www.patreon.com/twotankersandcat   As Always, Happy Tanking and Have A Great Week! Russel & Charlie

Recovering after a car accident is slow and cumbersome. It can take weeks for the process to run its course. That painstaking process includes data collection though – and it turns out that dataset is the perfect training ground for AI. Alex Dalyac is our guest on this episode of the Georgian Impact Podcast. He's the Co-founder and CEO of Tractable AI. Until recently humans had the edge over AI when it comes to image classification tasks – but the scales have now tipped in the computer's favor. Tractable is leveraging that fact to help people recover from automotive accidents and natural disasters. You'll hear about: How accident and disaster recovery could be 10x faster using AI. Why Tractable chose to pivot their AI's strengths from plastic pipes to the accident recovery. How Alex and his team convinced competing insurance companies to pool their data – and how they keep that data safe. The challenges of selling in such a consolidated industry. Tractable's approach to improving trust and transparency. Who is Alex Dalyac? Alex Dalyac is the Co-founder and CEO of Tractable AI , an artificial intelligence company specialized in visual tasks for traditional industries. The company's current focus is insurance and automotive, where its AI predicts the cost to repair a vehicle based on photos of the damage. Its products are used by leading insurers in Europe and North America. Tractable was spun off from Alex's research at Imperial College London, where he led the Computing department's first industrial application of deep learning. Prior to research, Alex was as a hedge fund quant.

פרק מספר 62 של באמפרס (380! למניין רברס עם פלטפורמה) - רן, אלון, ודותן בבוקר (חורפי ולא חם סוף-סוף) של סוף אוקטובר עם סקירה של טכנולוגיות ודברים מעניינים מהזמן האחרון.רן - סטנדרט חדש הולך ומתהווה - GQLסטנדרט שאילתות ל- Databases ראשון מאז SQL שנקבע אי שם בשנות ה- 70-80 . . .המטרה היא להסדיר את נושא השאילתות ב Graph Databases (דוגמת Neo4j שמניעים אותו, אבל יש גם אחרים) - וזה כרגע בתהליך של קבלה לועדת הסטנדטים ANSIאפשר לעקב אחרי התהליך והשלביםיש כל מיני הצעות ועדיין לא הוחלט באופן סופי - בעולם ה - Databases יש לא מעט שפות שבהן ניתן לתשאל Graph databases, ובסופו של דבר המטרה היא להתקבע על אחת, שתיהיה סטנדרטית בדומה ל-SQL.אזהרה (!) - חשוב לשים לב ולא להתבלבל בין GQL לבין GraphQL  . . . . אלו שני דברים שונים:מצד אחד -GraphQL זו שפת שאילתות או בעצם קצת יותר כמו פרוטוקול בסגנון REST - משתמשים מעל HTTP אבל זו לא השפה שבא “מדברים” עם ה - Database.לעומת זאת - GQL, קצת כמו SQL,  הוא הסטנדרט (המיועד) - סטנדרט טקסטואלי שבו ניתן לכתוב שאילתות טקסט ל Graph Databases.הרבה מאוד זמן לא ראינו תנועה באיזור הזה, ומעניין שעכשיו יש.מי מבין מאזינינו שהוא במקרה גם בעלים של טסלה (אפי?!) ודאי מאוד התרגש לשמוע שהגרסא החדשה של התוכנה - 10.0 - יצאה.גם למי שאין לו במקרה (רן, למשל - מסתבר שזה פחות הולך בישראל בינתיים) - מעניין לראות שהגרסא נראית פחות או יותר כמו עדכון של IOS או Android: אם מסתכלים על רשימת הפיצ’רים, קשה לנחש שמדובר ברכב . . .הרבה דברים שקשורים לפנאי ולבידור - חיבורים ל - YouTube ול - Spotify, קריוקי וכאלהכמעט שלא תראו פיצ’רים שקשורים למנוע או לחלקים אחרים של, ובכן - רכב…הרכב נראה כפלטפורמת בידור, לפחות לפי הגרסא הזו. מעניין - הופך למערכת הפעלה לפנאי ופחות מערכת הפעלה לרכב.אלון - מישהו אמר (Twitter …) שלא האמין שיגיע לתקופה שבה עדכון של רכב יותר מרגש מעדכון של טלפון . . . מגניב.מתי העדכון הבא של אאודי? אה.ספריה בשם chart.xkcd - מעיין גרפים ב - JavaScript או HTML וכו’ שרצים בתוך הדפדפן - בסגנון xkcd:סדרת קריקטורות גיקיות פופולארית, בעיקר סביב מחשבים וטכנולוגיה, בעיצוב שדומה לעיפרון או עט גס, בשחור לבן “פשטני”.הספריה הזו מייצרת גרפים ותרשמים בסגנון - “כאילו שורטטו בעיפרון או טוש על נייר”.יש גם צבעים - לא רק שחור-לבן כמו ב”מקורי”.אחד המגניבים . . . יש טרנד כזה של מצגות שנראות כאילו עכשיו שרבטו אותן? אז כזה - נראה טוב וקריא מאוד.הפינה האמנותית - Repo ב - GitHub בשם The art of command lineמעניין סקירה של כלים (Unix, Linux) מאוד שימושיים , החל מאיך משתמשים ב - Bash (ה - Shell עצמו) והלאה.למשל - מה קורה שעושים Alt+B ואז Alt+F ? - מסתבר שזה מביא אתכם לתחילת השורה - במקום ללכת “אחורה בהיסטוריה” בשיטת “חץ למעלה-למעלה-למעלה” ואז לנסות להגיע למשהו באמצע, Alt+B ואז Alt+F מאפשר לעבור מילה אחרי מילה.אפשר גם להשתמש ב “VI Mode” בתוך ה - CLI עצמו - לעבור ולהשתמש בקיצורי הדרך של VI.אפשר גם לערוך את ה - Command Line שלכם בתוך Editor ועוד כל מיני פטנטים שאולי לא הכרתם.למי ש”חי בתוך ה - Command Line” (גרסא מאוד מוזרה של Jumanji?) - מומלץ.לא מאוד ארוך, חלק סביר שאתם מכירים - רן לא הכיר הכל. שווה לנסות.בלוג-פוסט מעניין ומעורר השראה - Logs were our lifeblood. Now they're our liabilityיש הרבה מאוד סוגים של לוגים - החל מלוגים “אופרטיביים” (Operational) בסגנון “נגמר לי המקום בדיסק” או exception כזה או אחר ועד לוגים “אפליקטיביים” (Application)  - שהבלוג מגדיר כ - Events ואליהם הוא מתייחס.דברים כמו Analytics למיניהם ש Google ו - Facebook אוהבים (לכאורה) לאסוף (לכאורה!) על פעולות של משתמשים.אומרים ש”דאטה זה הזהב החדש” וזה כנראה נכון בהרבה מובנים - ככל שתאספו יותר מידע על המשתמשים שלכם כך תוכלו לההפיק יותר תובנות, אבל . . .עם הגידול ברגולציות השונות, מתברר שזה לא כל כך פשוט לתחזק את כל הלוגים האלה - אם זו רגולציה באירופה וארה”ב וסין ועוד - מגלים שמצד אחד הדאטה שווה זהב, ומצד שני - “יכולים לתבוע לכם את התחת” אם לא תשמרו על הזהב הזה כמו שצריך ולא תדעו למחוק אותו ולעשות לו סגרגציה (Segregation) כמו שצריך, אז אם חס וחלילה מתרחשת דליפת מידע . . .הבלוג טוען שאם פעם היינו רק רוצים לאסוף כמה שיותר מידע, היום - ובטח שבעתיד - צריך לעשות את זה במשנה זהירות.הצפי הוא לפיתוח טכניקות שבהן נוכל אולי לשמור את ה  -Essence של המידע - מבלי לשמור את ה - Data עצמו.ציטוט ממישהו שנראה שמגיע מ - Facebook, שאומר ש”את הקהל שלנו אני יכול לייצג באמצעות חמישה משתמשים בלבד” - 5 Archetypes של משתמשים שמהם אפשר ללמוד את כל מה שצריך, ולא צריך את כל המיליארד או 2 מיליארד או כמה שזה לא יהיה.למעשה, זה מצביע על טרנד ב - Data Science שיודע לקחת הרבה מאוד Data, להוציא ממנו רק את הייצוגים המעניינים - “ולזרוק” את כל השאר.ה-MP3 של כל שאר הדאטה?(אלון) מעניין מאוד לחברות בתחילת הדרך - חברות ענק כבר מאוד מתעניינות ב - Long Tail, ואם תבוא ותגיד להם “הנה רק 5 ייצוגים” הם לא יגיבו יפה.יכול להיות - אבל מצד שני הרגולציות הולכות וגדלות, ולא נראה שזה הולך להיעלם - באיזשהו מקום הם יהיו חייבים. לחברות בתחילת הדרך זה אולי יהיה “יותר קל” (לוותר), אבל דווקא לחברות הגדולות יש את ה Liability היותר גדול ואולי לא תיהיה להן ברירה.את מי כבר תבעו - ?Google? Facebook -  שתיהן?אם מסתכלים על GDPR - ההגבלה היא על מה שהוא Tractable למשתמש ספציפי - אם שומרים בצורה אנונימית אז אין עם זה שום בעיה.ברגע שלוקחים רק Samples אז מראש יוצאים מבעיות רגולציה - אבל העניין הוא שחברות כאלו כן רוצות את כל ה - Data ... זה נחמד לדברים מסויימים, אבל לא למשל עבור פרסונליזציה…הבלוג בא להצביע על בעיה - ולא טוען שיש לו פתרון להכל. הפתרונות שכן מוצעים הם אגרגציה ואנונימיזציה (Aggregation, Anonymization), שזה מה שעושים למשל ב - Google.הבעיה קיימת, ואי אפשר להתעלם ממנה - אם פעם (ועדיין) לוגים היו הזהב החדש, היום אנחנו מבינים שלזהב הזה יש מחיר ויש ריבית, וזה בטח לא בא בחינם.צריך לחשוב על איך לא להחזיק מידע מיותר - לא משיקולי Storage אלא משיקולי Liability - ואיפה שאפשר לעשות אגרגציה ואנונימיזציה או דברים אחרים.זה בהחלט מציג אתגרים - גם ליישום יעיל ונכון וגם מבחינת פגיעה בפיצ’רים עתידיים - אם בעוד שנה תרצה לעשות פרסונליזציה - תיהיה לך בעיה.האמירה שלוגים הולכים והופכים ל Liability נראית נכונה, ונראה שתיהיה אפילו יותר נכונה עם הזמן.עד כאן סוגיות חוקיות להיום? ספויילר - כנראה שלא . . .תראו מי חוזר - !The Stack Overflow Podcast is Backלמי שזוכר (רומז שאנחנו זקנים?), הפודקאסט היה קיים משך שנים רבות ולאחרונה נכנס לקצת תרדמתהפודקאסט עצמו יותר ותיק מרברסים (!), בן למעלה מ-12 שניםרן עוד זוכר את עצמו מאזין ל Joel Spolsky וחושב שאולי כדאי שיהיה גם אחד כזה בעברית... יצא בסדר בסך הכל

“Happy to spend 10 minutes on our vision and the journey we're on, but then, really, 15 minutes on what we've got today, what it is we've achieved, what it is our AI does,” says Tractable co-founder and CEO Alexandre Dalyac when I video called him a couple of weeks ago. “You can probably speed up all of that,” I quip back.

In today’s episode I kick off the podcast by speaking to EF Co-Founder Matt Clifford. Matt Co-Founded EF with Alice Bentinck back in 2011 and since then EF has gone from strength to strength, building hundreds of companies worth over $400m including Magic Pony Technology, Tractable and StackHut. After starting London, EF has now set up additional programmes in Singapore and Berlin and recently raising funding from LinkedIn Co-Founder and Greylock Partner Reid Hoffman to continue scaling the EF mission globally. In this episode Matt and I discuss: - Why the risk of starting a company isn’t as big as you think and the two kinds of risk that prospective founders often confuse - How the world has moved from writing cheques to writing code and why technology entrepreneurship is the best career path for ambitious people - How EF has formalised its offering to serve the different parts of the startup path – from helping you find a co founder to getting you funded This was a fascinating a conversation as always with Matt and you’ll definitely get a sense of some of the driving ideas behind EF and insights into the current state of tech entrepreneurship.

How can artificial intelligence leave the lab and get down to business? Kenneth Cukier explores an innovative method with Tractable founder Alexandre Dalyac. Also, a new way to measure ancient oxygen is changing our understanding of evolution, and we crunch the numbers to reveal the long-term risks of air pollution. See acast.com/privacy for privacy and opt-out information.

How can artificial intelligence leave the lab and get down to business? Kenneth Cukier explores an innovative method with Tractable founder Alexandre Dalyac. Also, a new way to measure ancient oxygen is changing our understanding of evolution, and we crunch the numbers to reveal the long-term risks of air pollution. See acast.com/privacy for privacy and opt-out information.

Electricity lost its magic over the course the 18th and 19th centuries. The "invisible fire" steadily transitioned from a mysterious force of wonder to a mundane reality of daily modern life. In this two-part edition of Stuff to Blow Your Mind, Robert and Joe explore the various electrical experiments, stunts, inventions, performances, innovations, occultisms and atrocities that transformed the tractable thunder. Learn more about your ad-choices at https://news.iheart.com/podcast-advertisers

In February 2015 the UK government launched Contracts Finder, a portal that advertises open government-spending contract opportunities, allowing companies to easily and quickly bid for them. The UK spends £250 billion every year on public spending contracts, including vital public services like the NHS and it’s important that this money is spent as efficiently as possible. As part of an ASI fellowship, William Jones worked in collaboration with the UK Cabinet Office to increase the number of companies bidding on these contracts. He did this by automatically recommending them contracts which are available and relevant, using publicly accessible information from UK companies. The web application William built is called Contracts Recommender. His talk will explain the public sources of information he uses as well as how Contracts Recommender works. William Jones was recently awarded the MPhil in Advanced Computer Science from the Cambridge Computer Laboratory with Distinction. During the ASI fellowship he first worked on a deep learning image classification project with Tractable.io, using the convolutional neural network framework Caffe. Following this he joined a project with the Cabinet Office using natural language processing to automatically recommend relevant government procurement contracts to companies in the UK. He has just started a PhD in Mathematical Genomics at Cambridge.

If you experience any technical difficulties with this video or would like to make an accessibility-related request, please send a message to digicomm@uchicago.edu. Arun G. Chandrasekhar defines a general class of network formation models, Statistical Exponential Random Graph Models (SERGMs), that nest standard exponential random graph models (ERGMs) as a special case. Their definition provides the first general results on when these models’ parameters become accurate as the number of nodes grows. While standard techniques of estimating ERGMs have exponentially slow mixing times for many specifications, reformulating network formation as a distribution over the space of sufficient statistics, instead of the space of networks, makes estimation practical and easy. A related, but distinct, class of models is defined as subgraph generation models (SUGMs), which are useful for modeling sparse networks. Choice-based (strategic) network formation models can be written as SERGMs and SUGMs, as demonstrated with network data from rural Indian villages.

If you experience any technical difficulties with this video or would like to make an accessibility-related request, please send a message to digicomm@uchicago.edu. Arun G. Chandrasekhar defines a general class of network formation models, Statistical Exponential Random Graph Models (SERGMs), that nest standard exponential random graph models (ERGMs) as a special case. Their definition provides the first general results on when these models’ parameters become accurate as the number of nodes grows. While standard techniques of estimating ERGMs have exponentially slow mixing times for many specifications, reformulating network formation as a distribution over the space of sufficient statistics, instead of the space of networks, makes estimation practical and easy. A related, but distinct, class of models is defined as subgraph generation models (SUGMs), which are useful for modeling sparse networks. Choice-based (strategic) network formation models can be written as SERGMs and SUGMs, as demonstrated with network data from rural Indian villages.

The Latin root word tract means “drag” or “pull.” This root word gives rise to many English vocabulary words, including attraction, subtract, and contract. Perhaps the easiest way to remember this root word is through the English word tractor, for a tractor’s main function is to “drag” or “pull” heavy equipment.Like this? Build a competent vocabulary with Membean.