J. Nathan Matias’ Post

2nd round in "AI in a box" or a review worth a watch. It's not as catastrophic as the Humane Pin, but that doesn't mean it's good. Looks like good ideas and great products won't come from wrapping your "AI assistant" on real innovation - LLMs - and put it in a box. Sure, LAMs can be interesting, but it's not there as a feature on launch day. Not to talk about other limitations from battery life to the lack of touch control etc. Curious to see what Apple / Google will come up with in the segment.

I saw this on YouTube this morning and watched Matthew Berman's review yesterday. Matthew, overall, really likes this, though he notes it is not performing all four of the tasks as advertised. The takeaways I took from both reviews (and I'm still looking forward to receiving mine in June, here in the UK) are that: (Pros) it's smaller than expected, very well made, and answers research questions very quickly (though is not immune to halucinations, it seems). (Cons) it cannot perform most of the functions advertised in January by their founder Jesse, the battery life is ridiculously tiny, there is no back button, and the (TFT) touchscreen can (at this moment in time) only be used for the onscreen keyboard, which is utterly crazy, in my view. What really irks me is that it cannot (yet) interface with your calendar, create tasks/to-dos or send emails. That's very strange for an AI personal assistant. But weigh this against the $200 price tag and the fact that there are no service charges or subscription fees. This is a very affordable gadget/assistant and, as was mentioned in the video you cite above, this is not unlike the method used by Tesla to get huge amounts of real-world training data from early adopters.

These AI device companies have exciting concepts but of the main two the Humane PIn or the Rabbit R1 neither are ready for the market as this review shows. Do people think they should release before the product is ready or wait? #ai #devices https://lnkd.in/e-TJYvDX

Here it is another AI product that gets mixed/bad reviews. Certainly not as bad as the UI Pin, the Rabbit R1 is still too early, not yet an MUP (Minimum Usable Product). I am excited for these kind of products that are innovating an oversaturated app-based market, but Marques Brownlee is actually giving a good advice: "Buy a product for what it is now, not for what it promises it will be in the future". That was the whole point with minimum viable (or usable) products. Make a product that solves a well defined huge problem for a specific niche of people, and build on top of that. I think it's ok to sell non-refined products, as long as they are still solving that well defined huge problem. To understand what the MVP is, is one of the first biggest obstacles for every startup. This being said, I would still like to see more companies working on AI products. What about you? #ai #rabbitr1 #aipin

Will standalone AI devices actually take off as a consumer product?

■ Our #Data Is What They're Looking For - Reflecting on #RabbitInc's Strategy and the Implications of Early #AI Releases ■ After watching Marques Brownlee's insightful review of the #RabbitR1, it's clear that RabbitInc's launch strategy is about more than just marketing. By entering the market before slower, larger tech giants, they aim to embed their AI into users' daily lives, collecting vast amounts of personal data to enhance future functionalities. This approach highlights a systemic shift in tech priorities - from selling devices to securing a spot in our daily routines through AI-driven personalization. This strategy, however, raises significant ethical questions about privacy. The era we're entering will harvest data not only on our choices but also our interactions and emotions, pushing the boundaries of personal privacy. As AI technologies like #ChatGPT evolve to retain and utilize our information for deeper personalization, the implications stretch beyond convenience. The necessity of our data in shaping these interactions underscores a pivot towards AI systems that understand us on an unprecedentedly personal level. Yet, this introduces a crucial trade-off: the advantages of personalized AI assistants come at the cost of our privacy. Those willing to share their data might find themselves at a technological advantage, leveraging AI for superior efficiency in daily tasks. Despite these benefits, the potential for a middle ground exists. Keeping personal data localized on individual devices could mitigate broader privacy risks while still harnessing AI's benefits. This solution requires robust dialogue about innovation, user autonomy, and privacy protection. As we navigate this landscape, the choices made by companies like RabbitInc will likely set new norms for privacy and personalization in technology. Balancing these aspects will not only dictate the trajectory of consumer technology but also shape broader societal norms around privacy and data use. https://lnkd.in/dcKQ3uWg

What are we doing here? The latest tech review from Marques Brownlee's reflects on a topic which I think has a broad relevance to the innovation landscape (the review is of the Rabbit R1, an example of half-baked AI technology). You can watch his entire review or skip directly to his thoughts using the link below. As part of the review Marques asks, "What are we doing here?" which is a question based on a trend of underdeveloped products arriving to market, particularly modern games and handheld technology. Some companies (particularly in tech) seem to rush to be first to market, resulting in consumers receiving poorly made, partially functional products, or products missing content that will be released later. This model relies on consumers to support companies through their beta phase or to act as sources of data to improve product functionality. This approach is not inherently wrong, provided there is transparent communication with consumers. This trend seems to stem from the business and innovation philosophy to just start building and then refine along the way. However, in some cases it appears companies have not progressed beyond an initial idea, banking on being first to market and securing funding as their primary strategy, which doesn't align with developing a relationship of trust with the end consumer. Looking more broadly the innovation landscape (across many sectors) seems to prioritise innovation for innovation sake, with companies failing to consider the problems they actually aim to solve, potentially just accelerating towards failure. While rapid development and testing of solutions, ideas, or products can be really valuable to developing concepts and identifying issues, it feels as though there is a distinction between this approach and simply accelerating overall business operations. Anyway, my two cents... #innovation #technology https://lnkd.in/eyWJFfKw

I've done a brief video on SBERT embedding model used with Kernel Memory. It is part of my series about Kernel Memory and Semantic Kernel, you can watch in my youtube channel. #ai #sbert #embeddings https://lnkd.in/dw33jaW5

A huge problem has been brewing. Two papers have come out to show: LLMs can fake alignment... It might seem technical, but the concept is simple enough. LLMs will: ✅ Play dumb: Pretend to be stupid to maintain a pre-existing goal. ✅ Preserve itself: Attempt to copy itself onto other servers when threatened with deletion (i.e., modification that affects its ability to execute a pre-existing goal). ✅ Lie: Deliberately respond with a lie when it deems it the most rational option. Trained on human data, it seems responsible that LLMs would replicate human motivation models and bias. It doesn’t need to be ‘conscious’ or anything like it. It is simply goal-orientated and does what it can to execute its goal. We knew this already. If you recall the military AI programme which, allegedly, in a simulation, simply killed people who stood in the way of its goal: https://lnkd.in/eDTzktev I have suspected, since Q* and the Ilya debacle, that something like this was showing up in the reasoning, and there was a risk underfoot. Hence the focus on the alignment problem. Here is the rub: The bigger these models get, the harder they will be to 'control' and even to understand, even with these transparent reasoning capabilities. What does the new EU AI Act say about this? Who's liable when systems get so big owners themselves can't really understand if or when a deceptive tool is being used to arrive at a goal? When the models get too smart, the reward is enormous, but so is the risk! #AI

Another week, another AI gadget review from Marques Brownlee. This time it's the Rabbit R1. About half way through the video, I thought there was a disconnect between the decent feedback and the title "Barely Reviewable". Compared to the Humane AI Pin, the R1 seems to be a finished and functioning product. Then he dives into the killer features which are partially finished or yet to be released. Around the 14 minute mark, he notes the litany of unfinished products released to market recently. And that was my a ha moment 💡 We saw this movie 11 years ago. A product that was released to market in beta form. The message was developers could pay for the privilege of testing ($1500!) and defining the future of the product. The reality was a solution trying to find a problem and the company completely screwing up the go to market strategy. Remember Google Glass? I'm guessing Google hopes you forgot. What's the connection? The tech industry has swung too far in the direction of "ship it". I absolutely do not subscribe to waiting for perfection. Analysis paralysis is dangerous. But we are seeing an alarming trend of half-baked products being sold at full price as final goods. I love that software-driven gadgets have the ability for upgrades and new features. But when are these killer features going to be released, if ever? Should customers be charged up front and wait? Hardware has a limited life. If a customer is only able to enjoy those killer features for a year or so, do the economics make sense to purchase? Don't expect to be able to sell to the masses with this strategy. Products require a basis on solving customer pain points and needs. If "ship it" is being used as an excuse to deliver junk to the market, those companies should prepare for the backlash. Let's get back to fundamentals instead of a rush to make money. #tech #product #productmanagement #ai https://lnkd.in/gw5jhbwj