Ze'ev Abrams

🧠 AI will be pervasive in manufacturing 🏭 whether we accept it or not. It's time to embrace the technology and drive meaningful value with its usage. 💡 If you are starting this journey or have tried but have yet to quite succeed in generating value, this blog is for you.

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🧩 One thing we've noticed with #VentureIQ technology is how well it can distill and communicate complex technical material 🧩 Whether it's a #deeptech venture, a novel #AI application, or even a patent submission, we can quickly distill the business and strategic implications for investors...and sometimes even the founders/PIs themselves! 🤯 Based on our work so far, the tech can handle a wide array of research areas and technological sectors -- anything from blockchain algorithms to vaccine development. It handles the nuance of the field, the specific terminology, and extracts the key concepts which matter for venture-building. 🤓 To demonstrate, I took a patent I helped write with Eric Laber on reinforcement learning. I ran it through #Ventureiq to build a commercial assessment (key differentiators, target markets). Then for fun I had #NotebookLM make a podcast out of it. I've uploaded the result it here (DM me if you'd like the actual report): https://lnkd.in/eiTejsny This is more than just automation or streamlining operations - it's about unlocking clarity and insight when it was previously unattainable, and the ability to do it over and over again no matter the sector or vertical. Interested in more info? DM me so we can chat.

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My epic co-founder Tim Cheung did an excellent job (as always) in his latest post for Factory AI. The article is about how manufacturers can leverage the goldmine they’re sitting on—their data. First, he clears some of the fog on today’s hot topics: AI, ML, Deep Learning, and Gen AI. Second, he shares six relevant use cases, each accompanied by a case studies for each. Check out the link to the post in the comments. Give it a read and let us know your thoughts as always. #Manufacturing #FoodAndBeverage

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Legislative processes are boring and a lot of people don't pay attention to laws until they get passed, so I'll apologize in advance for talking about a California bill currently in committee. It's called SB 1047. The bill recently passed the California Senate. To become law, it needs to pass through committee next week, get a vote of support in the Assembly this summer, and then be signed by Newsom. The problem is that SB 1047 is a deeply flawed bill that could kill the AI industry in California. Jeremy Howard, a guy I respect a lot in AI, has written about it here: https://lnkd.in/gcQy6T_g Here are his very good points: - The definition of “covered model” is overly broad and could encompass a wide range of open-source models. - The requirements for developers are overly burdensome and could discourage open-source development. - The potential for civil penalties for AI developers could have a chilling effect on research and innovation. - The ability to levy fees could create a barrier to entry for small businesses. There are better ways to support the responsible development of AI. I hope the tech community will rally to oppose this bill and create something more reasonable in its place.

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Three takeaways here: 1. #VentureIQ is insanely good at analyzing dense #RandD and #IP reports and detailing the potential applications of the underlying IP. That said, the reports still skew towards a technical audience... 2. #NotebookLM is insanely good at taking technical writing and producing "podcasts" that a 10 year-old can understand. If you don't believe me, just listen to the first 30 seconds: https://lnkd.in/ep-FNc_z Together #VentureIQ and #NotebookLM can take complex material seemingly written in a foreign language, distill what it does and why it matters, and then explain it to a 10 year-old. And it only takes 5 minutes. Pretty cool, right? This brings me to item #3.... Neal Ghosh, is crazy smart (he ran this on his own #ML patent) and has been working on #AI since before it was cool. Everyone should have a Neal on their team.

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Notion vs Glean reminds me a lot of Tesla vs Waymo... Just like I think Tesla is going to solve the 'software' problem of autonomous vehicles before Waywmo solves the 'hardware' problem... I think that Notion is going to solve 'enterprise knowledge search' before Glean solves the problem of document and knowledge creation... Why? A couple reasons: 1) Just like Tesla, Notion has a huge advantage when it comes to the amount of underlying data and documents to train on... They already have 100M+ users, and likely 10x that in terms of the number of documents, files, etc. in team's workspaces. 2) It's very hard to change people's behaviour re: where/how they create 'work'. It's why a lot of companies have come and gone in that category... As exciting as Glean's AI writing assistants might seem, most people are just fine (or even prefer) a good old fashioned editor/writer. I could be way off here, but this will be an intersting one to follow. Thoughts? Where am I getting it wrong? #ai #enterprise #knowledge #management #notion #glean

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Excerpt from an Angel Squad memo that we invested in recently: “[Company] is led by a team of scientists and engineers with PhD-level expertise in Machine Learning, Engineering Simulations, and Computational Materials Science. The team is headed by CEO, [Name], who previously led AI development at two early-stage startups. [Name] is joined by CTO, [Name], who has deep experience in machine learning and materials science, and Chief Science Officer, [Name], a doctoral researcher at the [School]. “ Investing at the earliest stages of value creation often means taking big bets on a founding team. At Angel Squad and Hustle Fund, we love founders with deep expertise and super domain knowledge, particularly in deep tech verticals.

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Stability AI now has a problem. FLUX is everything the AI community expected SD3 to be. It initially appeared inflexible and instead it turns out to be trainable with consumer HW. Multiple groups are developing auxiliary models for it (e.g., ControlNet) at a furious pace. More importantly, Black Forest Labs managed to release it way, way, way faster than the time it took Stability AI to release the mediocre SD3 Medium model. And, as we speak, they are certainly polishing an updated version (FLUX 1.5?) able to support video generation. So, now, Stability AI has to show something that is equally or more compelling. There's plenty of room for one or two Midjourney competitors in the market. But they must both offer state-of-the-art image/video generation AND be able to specialize in certain types of content. For now, I wouldn't be surprised if, after X, also Adobe would sign a deal with Black Forest Labs to use FLUX as foundation model for their Firefly service.

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In the last day, many people are claiming that “the #AI scaling laws are dead” because Ilya said in a speech that we are running out of training data…as most of the public internet data is already used. This is just NOT reality. First there’s about 10-100x more public text data on the internet than what’s being used to train frontier models. Than there’s 1 billion X the amount of public text data on private networks and personal devices. Then there’s 10-100 billion x the amount of public text data in multimodal data (audio, video, sensors, satellites, EV etc). Organic data is doubling every ~18 months. And there’s virtually infinite synthetic data that could be created. Ilya doesn’t have the funding of the biggest players so it’s to his advantage to spread this belief to give his company a greater position of strength in the market and maybe slow down other players from out scaling him. He’s a very smart guy, but you can’t take everything he saids at face value. 🤔 He knows he can’t compete on who’s got the most compute, but he believes he can compete on who’s smart enough to make new algorithmic breakthroughs… See data in comments

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The challenge of retaining AI companies and talent in Massachusetts is a real one. As Scott Kirsner 's article today highlights, it's a battle we must face head-on. Let's start with the challenges our industries face every day. The machine shop struggling with job quotes, the nurses in our hospitals grappling with patient falls, the financial advisors striving to offer new services, or the emergency services trying to maintain aging equipment. These are real-world problems that need real-world AI solutions. Solving problems for the Commonwealth's industries starts with understanding. We must understand and curate these problems and then use AI to solve them. It's a process that requires dedication and a deep understanding of customer needs. But it's a process that can lead to transformational solutions. Massachusetts is a hub for AI innovation. Let's turn our challenges into opportunities.  C10 Labs, Shahid Azim, Patricia Geli, Ramesh Raskar, Paul Baier, AI Blueprint for MA, Beth Porter So, what's the problem your Massachusetts company needs to solve? #AI #Innovation #MassachusettsTech, #ai-blueprint-for-ma

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There are still VC scale hardware opportunities in the age of GPU and cloud, you just have to have an open mind and know where to look. 👇🏻

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This is one of my favorite episodes yet! Scott Cronick, Michai Mathieu Morin, and I dive into the mysteries of the New Jersey drones, UFOs, and AI. We might just be one of the few groups getting close to the truth—judge for yourself! Perfect for your weekend listening! https://lnkd.in/ewsXjut3 #Drones #UFOs #AI #Podcast

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I am seeking a CEO for an exciting and dynamic AI company - email me if you have a lead - [email protected] MIT affiliation Lives in the Boston' ecosystem CS/AI background Former startup founder Some GTM experience More on the company - This company aims to tackle some of the most critical safety concerns facing the AI industry: hallucinations and unreliable outputs. Driven by a team of machine learning experts, This company allows software developers across the globe to address these issues and de-risk AI model outputs. It is able to surgically update the architecture of any AI model within seconds so that it can quantify its own uncertainty. With this any AI model can flag its own mistakes and hallucinations, unlocking AI’s potential across new industries while enabling quality assurance. It makes developing safe AI models significantly faster and more efficient, and facilitates a meaningful reduction in compute costs in a world where AI chips and computing resources are scarce and expensive.  

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📢 Exploring the Intersection of Robotics, AI, and Human Collaboration in the US Market As robotics and automation gain traction in industries like retail and catering, there’s an increasing spotlight on their potential impact on jobs and workers’ rights. Recent discussions in the US highlight unions advocating for contracts that include protections and clauses around automation—a critical step in navigating this rapidly changing landscape. At The Digital Leadership Forum, we’ve always focused on the theme of human-AI collaboration, exploring how technology can empower rather than replace the workforce. I’ve been diving into this topic recently and am curious: Are there any forward-thinking legal experts in my network working on frameworks or strategies to address the legal and ethical implications of automation? Let’s connect! I’d love to explore the possibility of developing shared frameworks or facilitating discussions around this important theme. Together, we can help shape the future of human-AI collaboration while ensuring fairness and innovation. 💡 Drop me a message ([email protected]) or comment below if you’re working in this space, or tag someone you think might be interested! Thanks! #Robotics #Automation #HumanAICollaboration #AILeadership #FutureOfWork #LegalTech #Innovation #RetailAutomation #CateringIndustry #EthicsInAI #LaborRights #thedigitalleadershipforum

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This past week I had the pleasure of attending a Connector Series event Presented by IntePros, where Wil Reynolds, VP of Innovation at Seer Interactive discussed an “AI Survival Guide." A huge thank you to Kristin Zeigler for the invite. It was a smaller, more intimate forum which allowed for really effective networking and deeper conversations.   The conversation centered around #GenAI, most notably #ChatGPT, and what it means for our workforce, how to use it most effectively and the learning & development opportunities & challenges it presents for our youth.   Some of my key takeaways and insights that I wanted to share: ➡ Authenticity will become a competitive advantage - For both companies and individuals, authenticity will become a competitive advantage in an increasingly AI-assisted world. Employers, customers, coworkers, and the alike are quickly becoming very good at distinguishing what messages are authentic vs. AI-generated, or as Wil would call it "Lazy-GPT'd." People and companies who are over-reliant on GPTs for original thought, marketing messaging, texts, social media posts, emails and other forms of communication will quickly lose trust and credibility. However, those that use GPTs to poke holes in their ideas, as a benchmark for framing a message or learning quickly about something new to communicate authentically will develop better relationships overtime, and therefore, standout amongst a crowded market (I am happy to report I did not "GPT" this post 😁) ➡ Current GPTs "spit out" the average, not the exceptional - Current GPTs are trained to predict the "average" response to a prompt based on the data they are trained on. Most of the GPTs that are commonly used by the public (such as ChatGPT) are trained on such a large and broad corpus of text data from the internet. What this means for marketers is that GPTs are not going to be very good at providing differentiated messaging. However, what it can help marketers do is accomplish specific, siloed tasks such as summarizing large amounts of market research, poking holes in a human-generated message, or conducting a keyword analysis. ➡ Leadership honesty will attract talent - Candidly, the way most executives at fortune 500 companies are thinking about AI is "how can I get the same amount of work done (or more) with 10% less of our current workforce?" Basic business principles will tell you cutting costs is often a more streamlined way of boosting profits as opposed to growing revenue. Wil makes the argument that top talent will be attracted to leaders who "keep it real" with their employees. In an environment where employees understand their leadership's vision for AI, they are more effectively able to plan for their career/talent development and carve a path within the company (or outside of it).

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7 RECOMMENDATIONS FOR HARDWARE STARTUPS It’s hard to understate the difficulty of the v1 launch of a consumer hardware company. I see a ton of new HW startups coming out. Here are some counterintuitive recommendations… 1) Target a small market for v1, not a large market. You need a small number of people to love your product and this will earn you the right to build a v2. You have to understand your v1 will not make you a successful company. Your v1 needs to prove that you deserve to exist in the first place. In the case of WHOOP, we started with pro athletes who were willing to put up with our v1’s short battery life and flaky Bluetooth connections because they so desperately needed Sleep and Recovery data. A broad consumer base would have rejected it. 2) Be great at no more than 1-3 things. If you try to be great at more than that, you will build a hardware that is mediocre at a wide range of things. You won’t think it’s going to be mediocre, but it will be. 3) You want a small launch. Not because you want small sales but because you want to have a steady ramp. It’s much better to do 100k of sales steadily over 12 months than in 2 months with a huge drop off. Keep in mind you need to manage your manufacturing partner and that there’s nothing better or more efficient than steady and predictable. 4) CAC is your enemy. Referrals are gold. The magic of referrals is that they signal high NPS and they help you grow steadily. Again everything that helps you have a steady HW manufacturing plan is good. Building a referral program for v1 launch is a good use of your time. 5) Be excellent at Software. Turns out being good at both Hardware and Software is a hard thing and something that your BIG COMPANY competitor is actually not great at (excluding Apple here). For some reason, there’s such a magnifying glass on the Hardware development, that Software is then overlooked by consumer startups. Don’t make that mistake. You can be great at both. 6) You should spend an unreasonable amount of time and money on ONE aspect of your Hardware. Could be the design or a technical breakthrough. But something needs to be SPECIAL. In our case, we designed a modular battery pack that allowed Whoop to be charged without taking it off your body. This made our development timeline much longer, but it allowed us to achieve something that no other wearable has achieved since: 24/7 health data. 7) Finally, have an unrealistic belief in yourself and your team. You are not supposed to succeed and the odds are stacked against you. Building hardware takes confidence and you’re up against big players who will rip you off. We went so far as to leave a message for our competitors on our circuit board (pictured below). In the case of Amazon and others, the prophecy came true. If you’re building consumer hardware, I wish you nothing but success. It will be super hard, but super rewarding. Good luck 👊🏼 #consumer #hardware #wearable #startup #whoop

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Some great observations and clarifications here by Francesco Perticarari dispelling myths from facts about #DeepTech #Investments using data.

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Question for VCs and executives in agile manufacturing: Desktop Metal, the darling of 3D printing, is about to be sold at its early-days valuation. What did we learn from this? Here are a few lessons I picked up: 1. Focus on verticals: It's incredibly tough, if not impossible, to build a general-purpose machine. Manufacturing is full of exceptions, and each application has its unique requirements. Don't spread yourself too thin. Dive deep into the most promising vertical first, then expand to the next. 2. Have technically savy managers: Manufacturing requires both technical know-how and operational experience. Ensure your executives in agile manufacturing companies understand all the technical tradeoffs and can decide what not to do and what to focus on technically. What not do is probably more important that all the things we could do. 3. Hybrid processes win: Don't define a solution for agile manufacturing as a single process like 3D printing. Hybrid approaches are key. A single part often requires multiple operations. That's why in 2018, we envisioned a process-agnostic platform capable of utilizing various methods—be it 3D printing, machining, or Roboforming—through AI to achieve the best results. This vision led to the birth of Machina. 4. Have high conviction but be ready to change: Despite the clear limitations of 3D printing, many investors, including those backing Desktop Metal, advised us to sell out to companies like them till recently. Their strategy didn’t seem to work, and yet, some praised it until just a few months ago. What did you learn? I would love to hear from investor in DM. Note: I am still very bullish on 3D Prining but with a different product and GTM strategy.

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