Secret Cyborgs, Recovering Ancient Antibiotics with AI, Tool Mastery LLMs and Nvidia's Pocket Sized Image Generator
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The AI News Now newsletter is back from its summer break and packed with news stories from the last few weeks so let's get to it.
AI is already quietly reshaping the workplace. But instead of coming from the top down it’s coming from the bottom up.?
Employees are discovering that AI is often a secret weapon to boost their productivity. Despite some company bans on AI usage, these savvy workers are turning themselves into "secret cyborgs" because the upside is less work, done faster and for those who do it well, better on the job performance and reviews.
It's a clandestine revolution, one that's making employees faster at coding and completing writing tasks, all while flying under the corporate radar.
Now, you might be wondering why these employees are keeping their AI usage under wraps. It turns out that companies are somewhat hesitant to greenlight AI usage. Concerns about potential risks and a lack of finalized AI guidance are the main culprits. This hesitation, however, is creating a secret society of AI users who are gaining an edge over their colleagues, unbeknownst to their coworkers and bosses.
The rise of external devices and personal laptops and phones makes it easier for these covert users to employ AI without the fear of getting caught. This stealthy use of AI is creating a rather interesting dynamic in the workplace. On one hand, it's leading to a productivity boom. On the other hand, it's creating a divide, an inequality where AI users are gaining an advantage over non-users.
But here's the kicker - this doesn't have to be the case. If companies would only embrace AI and provide clear guidance on its use, instead of falling into the hysterical media cover and doomsday thinking, they could transform that productivity boost to their own advantage. Not only would this prevent covert users from benefiting at the expense of others, but it could also lead to a significant increase in overall productivity. In the near future AI could will become a necessity in the workplace so these folks are already ahead of the curve.?Smart businesses will embrace the trend before their conservative competitors.?
Embracing AI can create a level playing field, increase productivity, and eventually become a necessity in the workplace. It's a fascinating development that could reshape how we view work and productivity. It's not a race against machines, but rather a race with machines and who joins forces with them first.
Here’s to the secret cyborgs.
The economic landscape of AI is shifting. While traditional AI has been profitable for big players, it's been a tough road for startups due to high costs and the need for human intervention. Enter Generative AI and foundation models, which are turning the tables and disrupting the industry. These models, which don't always require absolute correctness, are seeing rapid adoption and growth, with companies like ChatGPT and Janitor AI boasting millions of users within months of launch. The potential of Generative AI is vast, with applications in various fields such as therapy, education, and programming. The cost and time savings offered by these models are transformative, with the potential to bring the marginal cost of creation to zero - much like the microchip and the internet did for computing and distribution. Despite concerns, there are opportunities to build companies around traditional defensive moats such as scale, network, and brand. The future of AI looks generative, and now might be the time to dive in.
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ToolLLM delivers a novel framework designed to supercharge open-source large language models (LLMs) with tool-use capabilities. With its unique three-pronged approach of data construction, model training, and evaluation, ToolLLM is leaving no stone unturned. ToolBench uses an instruction-tuning dataset created using ChatGPT boasts knowledge of over 16,000 real-world RESTful APIs. In a bid to enhance the planning and reasoning abilities of LLMs, a depth-first search-based decision tree (DFSDT) helps the model find the best next steps.?Finally, ToolEval, an automatic evaluator, is on hand to assess the tool-use capabilities of LLMs, while ToolLLaMA, a fine-tuned LLaMA model, is showing off its impressive execution of complex instructions and adaptability to unseen APIs.
IBM has just pulled back the curtain on a revolutionary analog AI chip that operates strikingly like our own grey matter. It's designed for the heavy lifting in deep neural networks, and it's not only more efficient but also much less of a power hog. This innovative chip, boasting 64 AIMC cores connected via an on-chip communication network, promises to outperform current digital chips by being more energy-efficient and less taxing on the battery. If this tech works and scales, we could see cloud providers slashing their energy costs and shrinking their carbon footprint with this chip.
Nvidia has once again made its mark in the AI landscape with the development of a new AI image personalization method known as Perfusion. This little genius not only fits on a floppy disk, taking up a mere 100KB of space, but also trains itself in a brisk 4 minutes! The model introduces a novel concept called "Key-Locking" to avoid overfitting and allows for the merging of multiple personalized concepts into one image. Plus, users can tweak the balance between visual fidelity and textual alignment, all with a single 100KB model.
In a fascinating twist of science, AI scientists are using technology to resurrect antibiotic properties from extinct organisms, a concept dubbed "molecular de-extinction". This breakthrough not only holds promise in speeding up antibiotic discovery but also paves the way to tackle the looming threat of antibiotic resistance, which the CDC warns could claim 10 million lives by 2050.
Ever wondered if machine learning models are more like parrots or philosophers? The latest research reveals that with extended training, these models can graduate from simply memorizing data to actually generalizing from unseen inputs. The study delves into the workings of a small one-layer MLP model, revealing that as the model's test data accuracy improves, its weights display less noise and more consistency, signaling a shift towards generalization. It's a bit like watching a student move from rote learning to critical thinking! The secret sauce? The model's knack for outputting high probabilities for correct labels, maintaining low-magnitude weights, and a few other factors like model size, weight decay, and data size. This fascinating study also suggests that training simpler models with more inductive biases could help us better understand larger models and the patterns they learn. So, it turns out, even AI has to hit the books to get smarter!
Remember when HustleGPT and other projects were going to build fully autonomous AI businesses??Turns out, not so much.?Jackson Greathouse Fall, who charmed the internet with his ambitious plan of kickstarting a business using OpenAI's ChatGPT with a mere $100, faced an unfortunate nosedive. The AI’s dream project, a sustainable living blog dubbed 'Green Gadget Guru', was far from a success story. The blog, built on ChatGPT's advice, was riddled with issues and ultimately flopped, with Fall's claims of revenue generation remaining an illusion. This scenario offers a sobering reminder of the potential pitfalls of over-reliance on AI in business, amidst the current gold-rush mentality towards AI technologies. For others, it’s just obvious that we’re not at fully autonomous Agents just yet and we should make sure you keep a human in the loop to do the creative thinking.
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