Eric Bonabeau

This analysis, at least anecdotally makes sense to me. In many situations, infringe and holdout is the least painful option for large infringers. The system enables it. - The plaintiff must allocate the time and $$ to assert, taking time and $$ away from selling and developing products. Even if they engage litigation financiers they are limiting their upside potential. - The infringer continues to infringe while employing stall tactics or simply relying on the painfully slow, overburdened legal system. - The cost benefit analysis for infringers - "infringe and pay lawyers" typically outweighs the cost of design around or license. - Enhanced damages and injunctions are rare. - Litigation insurance products reduce risk all around. "The Marcum report shows that there is indeed something wrong with the patent system in the United States but it’s not excessive litigation or patent trolls. The real problem is the inability of patent owners to enforce their rights against infringers. Given the difficulty of obtaining injunctive relief and the limited enhancement of damages, it is unsurprising that predatory infringement has become a standard business practice when, at worst, all an infringer can be expected to pay is a reasonable royalty and, at best, nothing at all."

5

During yesterday's LibreQoS APNIC webinar, I posed the question: won't more bandwidth solve these problems? As Herbert Wolverson said, yes, more bandwidth is good, but, still these problems remain if queueing is done incorrectly. Here's my take on why bandwidth alone is not as good as bandwidth + good queueing policies: Generally it was believed that 'data is important, so don't throw it away; hang on to it and send it later'. In practice, this has proven to be suboptimal as two issues may emerge: 1) latency increases for some flows due to heavy demand from other flows using the same bottleneck link, 2) even a single flow can have excessive latency due to aspects of typical TCP behavior (referred to bufferbloat) when the buffers grew large enough that the data being buffered was retransmitted anyway. It turns out that not all data is equally important. Active Queue Management is the art of deciding priorities, both in deciding what data to throw away, but also in allowing some later arriving data to be transmitted ahead of data in another connection that arrived before it. These problems have been studied, and good solutions have been found by using certain queueing policies in routers and switches, referred to as “fq_codel’ and “cake”. These track the different flows not by classifying the data, but by watching the behavior. Flows that send relatively little data (DNS lookups), or at a measured pace (video chat) have priority over flows that send a lot of data as quickly as possible (App and System updates, Video and ISO downloads).

10

5 Comments

AI memory is fundamental for conversational AI and AI agents. It is critical for building personalized experiences and improving reasoning. Check out Letta’s course to understand how to use AI memory to enhance the customization and quality of your AI app.

15

Science-fiction is inherently speculative. Author George #Orwell of ‘#1984’ predicted concepts such as doublethink, little earbuds and society’s obsession with the black mirror screens that dominate our lives — and according to the BBC, novelist and short story author JG Ballard, of “High-Rise” and “Crash” fame, has predicted the current tool and cultural fixation of #GenerativeAI in the modern society — only he didn’t just speculate them within a story, he is actually one of the first use-cases of a generative AI used in a creative context — Ballard was so fascinated with the growing technological advancement of computers that he used one to compose poems, essentially the precursor to what we have now with ChatGPT. Today, the debate about the creative use of generative AI and its value on creative industries is an extremely fraught and controversial topic, but if controlled in a space that doesn’t replace human labor or try to equate itself to traditional art forms, it is interesting to see what computers can do and how they are creatively limited in comparison. Ballard’s two poems, The Yellow Back Novels and Machine Gun City are generated, and both include notes on how Ballard achieved producing them. Regardless of quality (Ballard himself probably wouldn’t describe these two poems as his best work), it is interesting to see primitive versions of the technology that we utilize on the daily today, and how these moral conversations that concern them would continuously evolve.

1

1 Comment

On the latest Vital Signs, Nikhil Krishnan and I had a ton of fun chatting with Dr. Atul Butte. Dr. Butte is a professor at UCSF, Chief Data Scientist of the UC Health System, and a co-founder of numerous healthcare startups. In our conversation, Dr. Butte shared his takes on healthcare AI opportunities, challenges, hype, and more. A few highlights: ⚡LLM use cases, data, and growth LLMs are already helping to solve the “drudgery” aspects of medicine, such as automatically drafting SOAP notes and patient messages. Dr. Butte is excited about future applications of LLMs that can help with the “cognitive” aspects: with difficult medical problems an LLM might be able to stitch together disparate pieces of information to generate a potentially novel solution. He also highlights that prompt engineering will become the next “coding language” as so much of leveraging LLMs is knowing how to ask them the right questions. Additionally, Dr. Butte discusses how since 2016, UCSF has been working to deidentify its patient notes so that it can query them to answer clinical questions and ultimately enhance patient care. ⚡The need for patient-facing AI As patients are increasingly able to access their own personal health data in nicely structured formats (e.g., EHRs, wearables), they desperately need more tools to help them interpret and make decisions using that data. Dr. Butte understands that these patient-facing solutions are challenging to get funded but he emphasizes that patients need to receive the benefits of AI sooner. ⚡Evaluating healthcare AI solutions In order to evaluate AI solutions, we often put human specialists – such as medical professionals – on the same tasks. Understanding that specialists often disagree with each other, a significant challenge arises in figuring out how we can evaluate AI considering we can’t just “average” all the specialist opinions. Dr. Butte points out that there still remains a massive gap in AI evaluation tools. Additionally, there are so many organizations that want to have a say in how healthcare AI is regulated that the landscape is uncertain. An awesome conversation with an expert on all things AI meets healthcare. Listen to the full episode below: Spotify: https://spoti.fi/3Wr3643 Apple: https://apple.co/4acwgan

82

8 Comments

We are delighted to present the concluding installment based on our paper, "Vision 2035: The Emergent Singularity" (link in the comments). In this final section, we illuminate the three critical components that will prove instrumental for companies as they navigate the transformative landscape of machine intelligence: 1. The Role of Data: In the age of machine intelligence, data reigns supreme. As we navigate this data-rich landscape, challenges emerge in harnessing its full potential. The concept of "Big Data" evolves rapidly, demanding advancements in edge computing, natural language processing (NLP), and even quantum computing. Despite this growth, there's a growing reluctance to share data, hindering accessibility. Startups can serve as pivotal players, breaking down barriers and showcasing the value of innovative solutions. From technical to regulatory hurdles, the path to data accessibility is fraught with challenges, but also ripe with opportunities for venture-backed initiatives to lead the way. 2. Open-stack technologies: Rooted in openness across software, data, storage, and computation, open-stack technologies are analogous to open-source software in many ways. By embracing open-stack, companies transcend closed system limitations, fostering innovation and enabling agile, cost-effective development through collaborative ecosystems. Leveraging existing communities potentially reduces customer acquisition costs while cultivating dedicated user bases for lasting relationships. 3. Collective intelligence: Ecosystems built on shared, community, and aggregate knowledge offer prime opportunities for open-stack-centric machine intelligence startups. The emergent benefits from group knowledge can speed up product development and broaden customer reach. Collective intelligence approaches like federated learning can also streamline data processes, minimize data sharing while maintaining control and adaptability, enhance model accuracy, and mitigate technical and product risks across an ecosystem. We hope you have found our series of summary analysis on the realm of machine intelligence to be insightful. As highlighted in our longer vision paper, we are convinced that the value creation potential of winners in this space will amount to a remarkable two orders of magnitude (100x) compared to today's companies. Lastly, we may follow up with a little encore in two weeks. Keep your eyes peeled! Patrick & Sri Machine Ventures #singularity #machineintelligence #ai

12

3 Comments

It is great to see DARPA engaging broadly with startups and the private sector (as it always has) to discuss what technologies could be most critical to future national security. In such a conversation today ppl mentioned quantum computing, encryption and sensing, semiconductor design and manufacturing, new materials and chemistries, AI Safety and risk of AI aided bioterrorism and biological warfare, zero knowledge proofs, fully homomorphic encryption, US weapons and broader defense manufacturing capacity, hydrogen production technologies, and autonomous swarms.

23

3 Comments

💡 Are vertical AI startups the future of industry-specific SaaS? SignalFire's latest blog outlines a compelling case for how AI-enabled vertical SaaS solutions could transform industries, tapping into a $6 trillion market opportunity. By applying seven innovative frameworks, startups can address unique, industry-specific challenges like handling unstructured data, achieving data defensibility, and automating workflows. One standout approach is the “networked SaaS” model, where diverse stakeholders—from end-users to insurers—can connect on a single platform, driving efficiency and deep integration. Curious about these frameworks and examples of companies pioneering this space? Dive into the full article! 📈👇

8

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.

6

I recently sat down with Dennis Crowley (@dens) to discuss his new startup, Hopscotch Labs. We discussed the convergence of new technologies such as LLMs, and augmented reality, and where we are going into our future. We got talking about AI, our children, and entrepreneurship. He has some interesting ideas about how AI and AR will come together, especially in our ears. He also talked about staying true to his original mission: building things that help people better experience the world around them. My big interview with @dens is published in my newsletter CrazyStupidTech. Here is a link to the website. If you like what you read, sign up for the newsletter. https://buff.ly/3O5qvmd

23

2 Comments

For those of you still doubting Artificial Intelligence....wake the f$%& up!!! Go walk around downtown San Francisco and maybe all of Google's Waymo driverless cars on the streets may be a wake up call for you. If you need more proof take a look at Y Combinator's current cohort of companies as well as all the AI and AI-Enabled companies in the MedTech Innovator current cohort of companies or the ones pitching at LSI and LSI Europe!

14

We're launching the inaugural edition of Beyond Benchmarks, a deep dive into the metrics and trends observed across the early-stage enterprise cloud market. This is the first look at some of the business data around GenAI in SaaS. Some initial observations: 1) 60% of SaaS companies have already integrated GenAI into their offerings (60% of these as part of their current product and 40% as a new product), and an additional 20% are planning to do so this year. Almost half aren't monetizing these features. Those that are are primarily experimenting with both usage-based and flat fee pricing models. 2) Most companies are utilizing OpenAI as their primary LLM, but many are experimenting with multiple models - and we are noticing an early trend towards intelligently routing GenAI inference requests to different models based on cost, performance, and security. 3) Though we are in the initial innings of GenAI, we are seeing some positive signals - companies that implemented GenAI had 7% higher NDR than those that did not. Still early days, but we'll be doing this benchmarking report regularly to track and share how things progress in this new era. Link to report in comments.

104

6 Comments

Are LLMs better at Deductive (start with a general formula/relationship and compute outputs for different inputs) or Inductive (start with examples of inputs and outputs and figure out the generalized relationship) reasoning.... The answer is they are better at Inductive reasoning... Based on this what kind of applications are they better suited for???? More details in this amazing study....https://lnkd.in/gjKYUraW

13

2 Comments

https://lu.ma/gedw1srz event this upcoming Wed on embodied AI: come see robodog demos and hear Keith Adams , Jan Liphardt , Casey K. Caruso and more discuss where AI, robotics, and crypto intersect!

16

Some VC investors believe that brick and mortar - referring to a physical presence of some kind - should be avoided at all costs. Yet, the track record of clinic and hybrid care businesses in health care indicates otherwise. ==> One Medical (sold for $3.9 billion to Amazon), Oak Street (bought by CVS for $10.6 billion), Lifestance (IPO at $7.5 billion market cap), and PE-backed Premise Health (acquired by OMERS Private Equity for $1.1 billion) If a clinic is indicated as the optimal way to deliver care for that patient population, there are creative ways to build and operate them. Full article below with advice and strategies on how to build and operate hybrid clinics from my teammate Anarghya Vardhana from Maveron, Amir Dan Rubin from OneMedical Group, Steven Eidelman from Modern Animal and more!

13

1 Comment

I couldn't agree less with this article. In my view, AI will change the world as much as, if not more than the internet did...which is a lot. '60s computer scientist Roy Amara said it best: “we overestimate the impact of technology in the short-term and underestimate the effect in the long run.” Both Altman and the author are falling victim to this blind spot, but to me, it seems far more dangerous to underestimate the impact and get blindsided by the change than it is to be slightly overzealous on estimating the rate of change. #AI #ArtificialIntelligence #Technology #Innovation #cdntech [Read more](https://lnkd.in/gJWwyFZR)

8

One conversation we should be having more, especially in healthcare: is this an AI problem or a policy problem? A big challenge in American healthcare stems from the reality that our healthcare strategy changes with every administration. Throwing AI at problems that are well-addressed by policy innovation (e.g., AI for payer-provider RCM war) feels like a gross misallocation of resources. Solution sets are rarely zero sum – in truth, most problems will require policy, tech, and human interventions, but it's worth asking ourselves whether AI is the best solve when we have a multitude of options.

86

22 Comments

This is just incredible. Neal Ghosh's demonstration of how #VentureIQ takes deeply technical and complicated content across a range of industrial use cases and ages/stages through an AI-generated conversational podcast (a la #notebookLM) is really mind-blowing. Check out his post, below, and if you haven't seen VentureIQ, yet, let's get you set up to learn more. You can visit VentureIQ.ai or DM any of us over here to book a demo!

2