WEEKLY AI NEWS: RESEARCH, NEWS, RESOURCES, AND PERSPECTIVES
AI & ML news: Week 2- 8 September
OpenAI new models could cost up to 2000$ per month, X goes off in Brazil, and much more
20 min readSep 9, 2024
The most interesting news, repository, articles, and resources of the week
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Research
- Diffusion Models Are Real-Time Game Engines. a two-phase training process involving an RL agent to learn and a diffusion model to generate frames; it can interactively simulate DOOM over 20 frames per second on a single TPU. A game engine driven by a diffusion model allows real-time interaction with complex environments over long trajectories.
- Agentic Retrieval-Augmented Generation for Time Series Analysis. suggests an agentic RAG framework for time series analysis. It makes use of a multi-agent architecture in which an agent directs specialized sub-agents to carry out time-series tasks. These sub-agents can retrieve pertinent prompts that contain information about past patterns and trends, which helps to improve predictions on new data. The sub-agents use tuned small language models to accomplish these tasks.
- Persuasion Games using Large Language Models. asserts that the persuasive efficacy of LLMs can be increased by using a multi-agent framework, in which the main agent conducts persuasive dialogue while supporting agents handle crucial functions like information retrieval and response analysis. The study finds that LLMs are capable of influencing users’ perspectives and convincing them to make a purchase decision; for example, sales agents can influence user perspectives in a 71% positive way.
- Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling. discovers that synthetic data produced by weaker + less costly (WC) models is superior to data produced by stronger but more expensive models for fine-tuning models; generally, the results imply that WC models might be a compute-optimal method for training sophisticated LLM reasoners.
- Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model. demonstrates that it is possible to scale from 7B parameter models to 2T multi-modal tokens that can compete in performance with similar scale diffusion and language models. It also presents a training recipe to train multi-modal models over discrete and continuous data; it combines next token prediction with diffusion to train transformer models over mixed-modality sequences.
- ReMamba: Equip Mamba with Effective Long-Sequence Modeling. examines the long-context capacities and efficiency of Mamba models; the RNN-like nature of Mamba is the cause of the long-context deficiencies; it does this by compressing data using the following method: achieves a 3.2 improvement over the baseline on LongBench and 1.6 improvement on L-Eval; the strategy appears to also apply to Mamba 2. the top-k hidden states during the first forward pass and uses Mamba’s selective mechanism to incorporate them into the state space during the second forward pass.
- Text2SQL is Not Enough: Unifying AI and Databases with TAG. develops a benchmark and discovers that standard methods only answer 20 percent of natural language queries correctly. It suggests Table-Augmented Generation (TAG), a unified framework for responding to natural language queries over databases. It represents a wider range of unexplored interactions between LLMs and databases.
- Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Experts. Sparsifying the computation is aided by routing tokens to MoE experts. But it can be hard to learn that routing. Usually, there is a complex loss structure. This research presents an innovative solution to this issue, leading to a significant increase in training stability and expert balancing.
- Toward Robust Early Detection of Alzheimer’s Disease via an Integrated Multimodal Learning Approach. A multimodal classification approach intended to enhance the early detection of Alzheimer’s disease is presented in this work.
- Targeted Cause Discovery with Data-Driven Learning. A sophisticated machine learning technique has been created by researchers to determine a target’s direct and indirect causal variables within a system.
- Stochastic Layer-Wise Shuffle: A Good Practice to Improve Vision Mamba Training. To prevent overfitting in Vision Mamba models and enable them to scale up to 300M parameters while still performing competitively with Vision Transformers (ViTs), this research presents a stochastic layer-wise shuffle regularization strategy.
- Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control. Stable Control Representations are a tool that researchers are using to help embodied AI machines interpret scenes more precisely. These representations capture detailed visuospatial information required for challenging tasks by utilizing pre-trained text-to-image diffusion models.
- AI generates covertly racist decisions about people based on their dialect. Language models perpetuate covert racism through dialect prejudice, specifically against African American English (AAE), leading to negative stereotypes and harmful consequences, while overt stereotypes about African Americans are more positive, and current bias mitigation practices may worsen this issue.
- Latent Distillation for Continual Object Detection at the Edge. A unique Continual Learning technique for object detection that overcomes memory and computational limitations on edge devices is called latent distillation.
- Masked Mixers for Language Generation and Retrieval. Masked mixers are a unique architecture designed to enhance input representation in language models by substituting masked convolutions for self-attention.
- Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology. Using masked autoencoders and self-supervised learning, researchers have created a novel technique that greatly enhances the processing of large-scale microscope pictures.
- Pooling And Attention: What Are Effective Designs For LLM-Based Embedding Models? This work compares alternative pooling and attention strategies while examining multiple designs for LLM-based embedding models.
- AlphaProteo generates novel proteins for biology and health research. New AI system designs proteins that successfully bind to target molecules, with potential for advancing drug design, disease understanding and more.
News
- X goes offline in Brazil after Elon Musk’s refusal to comply with local laws. Millions of users shut out and 500,000 switch to rival platform Bluesky as providers enact supreme court ban
- ’A tech firm stole our voices — then cloned and sold them’. Paul Skye Lehrman and Linnea Sage, voice-over performers, discovered that an AI-powered text-to-speech platform had cloned their voices without permission after they were tricked into providing audio recordings through Fiverr. The couple has filed a lawsuit against the platform, Lovo, for allegedly using their voices illegally.
- Did your car witness a crime? Bay Area police may be coming for your Tesla — and they might tow it. Tesla’s Sentry Mode, a feature that uses the car’s cameras to monitor its surroundings, is increasingly being used by law enforcement as evidence in criminal investigations. The footage captured by the system has been instrumental in solving various crimes, such as car break-ins and hit-and-run incidents.
- Updates to the Command R Series. Updates were made to Command R and Command R+ for almost every task. Their recall, speed, arithmetic, and reasoning have all improved.
- Workers at Google DeepMind Push Company to Drop Military Contracts. In a letter, almost 200 workers at Google DeepMind demanded that the firm revoke its military contracts, citing a breach of its own AI ethics policy. Armed forces have purchased DeepMind technology from Google Cloud, which has caused internal strife among AI personnel who respect moral principles. Although Google’s response showed that the company was following the AI Principles, employees are still not pleased and want further regulation to prevent the military from using their AI.
- TRL release. This could be among the Transformer Reinforcement Learning library’s more significant updates. WinRate Callbacks, Liger Kernels, onlineDPO, and other features are included.
- xAI Starts Colossus Training Cluster. With intentions to double its size in a few months, xAI has initiated the 100,000 Colossus H100 training cluster, which is now the largest in the world.
- First MLPerf benchmarks for Nvidia Blackwell, AMD, Google, Untether AI. In MLPerf’s LLM Q&A benchmark, Nvidia’s new Blackwell chip showed the best per GPU performance, demonstrating notable improvements with its 4-bit floating-point accuracy. Rivals like AMD and Untether AI, however, have displayed encouraging outcomes, especially in terms of energy efficiency. For example, Untether AI’s speedAI240 chip performed exceptionally well in the edge-closed category, demonstrating a range of strengths in emerging AI inference technology.
- Two Oxford PhDs are building an app to let you remix photos into memes. A new social network by a duo of Oxford PhDs is working on an app to let you add friends to a photo in a more memeable and fun way.
- Apple and Nvidia may invest in OpenAI. The two tech giants might join OpenAI’s potentially huge funding round.
- Boston Dynamics’ new electric Atlas can do push-ups. In a recent video, Boston Dynamics demonstrated Atlas, its electric biped robot, completing push-ups to highlight the strength of its actuators during its early commercialization phase for factory floor applications.
- Meet Boardwalk Robotics’ Addition to the Humanoid Workforce. The humanoid upper torso robot Alex, by Boardwalk Robotics, is intended for use in manufacturing, logistics, and maintenance. Alex is a legless robot that was developed separately while utilizing the heritage of IHMC’s bipedal robot experience. Its designers prioritized manipulation over mobility in order to guarantee efficiency and safety. Pilots are now choosing commercial partners, but researchers can buy Alex right now.
- Americans Are Uncomfortable with Automated Decision-Making. Consumer Reports recently released a national survey finding that Americans are uncomfortable with the use of artificial intelligence (AI) and algorithmic decision-making in their day to day lives. Nearly three-quarters of respondents (72%) said they would be “uncomfortable”
- Canva says its AI features are worth the 300 percent price increase. The design software company is massively jacking up subscription prices for some users.
- AI worse than humans in every way at summarising information, government trial finds. A test of AI for Australia’s corporate regulator found that the technology might actually make more work for people, not less.
- Reliant’s paper-scouring AI takes on science’s data drudgery. Karl Moritz Hermann co-founded Reliant AI, which has raised $11.3 million in a seed round to automate academic literature reviews. Tabular, the company’s AI solution, promises zero-error data extraction from scientific papers. Reliant offers researchers an intuitive user interface (UI) while utilizing LLMs and patented methodologies to increase efficiency compared to conventional methods. Its usage of in-house hardware highlights its dedication to providing the research sector with premium, domain-specific AI solutions.
- Leveraging AI for efficient incident response. With the help of heuristic retrieval and LLM-based ranking, Meta has developed an AI-assisted root cause analysis system that has successfully identified 42% of the causes in its web monorepo investigations. Improving system accuracy has mostly been achieved by fine-tuning the Llama 2 model using previous data. The organization intends to increase the integration of AI tools with the goal of achieving autonomous processes and proactive risk mitigation.
- Artificial Intelligence Predicts Earthquakes With Unprecedented Accuracy. After testing their AI in China, researchers at the University of Texas were able to predict 70% of earthquakes.
- Recall 2.0? Microsoft plans another AI feature that scans everything. Another AI-driven feature that searches PC content surfaces in Windows 11, raising questions about data privacy.
- You.com raises $50M Series B. The search engine, agent platform, and knowledge base startup You.com has raised more money as it expands.
- Sakana raises $100m Series A. With the increase, Sakana will be able to hire more researchers, expand its computational capacity, and generally establish itself as one of Japan’s top AI labs.
- Google AI Overviews rollout hits news publisher search visibility. Some news items now have AI-written summaries available in Google’s US and UK search results. According to research, publisher visibility is being impacted by these AI Overviews, which is causing original articles to fall in the search results. To sustain traffic, this move may require major adjustments to SEO tactics.
- US, UK, EU and others sign landmark AI safety treaty. More than a dozen countries have signed a treaty designed to ensure that artificial intelligence models are used in a safe manner.
- OpenAI’s Next-Generation Models Could Reportedly Cost $2,000. The Sam Altman-led company’s new artificial intelligence models, such as Strawberry and Orion, likely won’t be cheap (prices as high as $2,000 per month).
- Alleged fraudster got $10 million in royalties using robots to stream AI-made music. A North Carolina man is facing fraud charges after allegedly uploading hundreds of thousands of AI-generated songs to streaming services and using bots to play them billions of times. Michael Smith is said to have received over $10 million in royalties since 2017 via the scheme.
- Advertisers plan to withdraw from X in record numbers. A record number of firms plan to cut advertising spending on X next year because of concerns that extreme content on the platform could damage their brands, dealing another blow to the financial fortunes of Elon Musk’s social media company.
- Dutch Regulator Slams Clearview AI with €30.5 Million Penalty for “Massive” Rights Breach. The Dutch Data Protection Authority (DPA) announced on Tuesday that it has imposed a €30.5 million ($33.7 million) fine on US facial recognition company Clearview AI for illegally creating a database of billions of facial images.
- M&S using AI as personal style guru in effort to boost online sales. Shoppers can use technology to advise them on outfit choices based on their body shape and style preferences
- Google’s AI-powered Ask Photos feature begins US rollout. More sophisticated natural language queries may now be used to search through photographs with Google photographs’ new AI-powered search function, “Ask Photos,” which is now available to a limited number of American users.
- Alibaba releases new AI model Qwen2-VL that can analyze videos more than 20 minutes long. Qwen2-VL, a new vision-language model with improved visual understanding, multilingual text-image processing, and video comprehension, has been published by Alibaba Cloud. In comparison to models such as Meta’s Llama 3.1 and OpenAI’s GPT-4o, Qwen2-VL performs better and is compatible with a wider range of applications, such as real-time video analysis and technical help. The models are open-source under Apache 2.0 for the smaller versions, and are available in three sizes (7B, 2B, and shortly 72B).
- Broadcom is working to integrate optical connectivity directly into GPUs. Currently, one of the main obstacles to training large models is the bandwidth of GPU interface. The problem would be much reduced if Broadcom could include optical transfer directly into GPUs, as they are now working on doing.
- YouTube is making tools to detect face and voice deepfakes. It plans to launch a pilot program for the voice detection tool by early next year.
- Google is working on AI that can hear signs of sickness. Given everything you’ve already heard about AI, you may not be surprised to learn that Google is among other outfits beginning to use sound signals to predict early signs of disease.
Resources
- AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems. An interface written in minimal code to quickly prototype AI agents. It may be used for multi-agent workflow evaluation and debugging, and it is constructed on top of the AutoGen framework.
- Foundation Models for Music: A Survey. gives a thorough rundown of the most recent pre-trained models and foundation models in the music industry.
- A Practitioner’s Guide to Continual Multimodal Pretraining. a thorough manual on ongoing multimodal related; presents FoMo-In-Flux, a large-scale continuous pretraining benchmark with fine-grained and extended horizons.
- AI feedback loop will spell death for future generative models. When you train LLMs with LLM-generated content, the results tend to be digital poop
- Apple’s robotics work aims to solve user’s first-world problems. Apple might be getting more involved in robotics and releasing moving gadgets, like an iPad supported by a robotic arm. Under the direction of Vice President of Technology Kevin Lynch, Apple is making headway in robotics with the assistance of specialists from companies such as Israel’s Technion, and plans to expand its AI interfaces beyond Siri. Apple is thinking of releasing these new robotic devices around 2026 or 2027, while they are still conceptual.
- Towards Real-world Event-guided Low-light Video Enhancement and Deblurring. Using event cameras, this end-to-end system concurrently solves motion deblurring and low-light enhancement in videos.
- Enhancing Sound Source Localization via False Negative Elimination. To overcome false negatives in conventional methods of sound source localization, researchers have put forth a novel audio-visual learning framework. Two schemes are included in the framework: Semantic-Aware Contrastive Learning (SACL) and Self-Supervised Predictive Learning (SSPL). While SACL improves the contrastive learning process to better align auditory and visual elements, SSPL removes false negatives by emphasizing positive-only learning.
- FastSD CPU. Flux Schnell on the CPU is now supported by a widely used inference library.
- Spiking Diffusion Models. A new class of Spiking Neural Networks (SNNs) called Spiking Diffusion Models (SDMs) is intended for image production and offers significant energy savings along with great biological plausibility.
- Laion 5B safety Release. The biggest publicly available image dataset on the internet was Laion 5B. Because of worries about offensive and hazardous imagery, it was taken down. After a major effort to address these problems, the group is now rereleasing the dataset.
- ml_dtypes. Bfloat16 and fp8 support for native numpy arrays.
- VisionTS. By redefining time series forecasting as an image reconstruction challenge, VisionTS is a novel method that takes advantage of the similarities between time series data and natural images to improve forecasting. To achieve remarkable zero-shot performance, it makes use of a visual masked autoencoder (MAE) that has been pre-trained on ImageNet.
- Codec Does Matter: Exploring the Semantic Shortcoming of Codec for Audio Language Model. A novel method for improving LLMs’ audio-generating performance is called X-Codec.
- The timm (PyTorch Image Models) Leaderboard. This leaderboard is based on the results of the models from Timm. Timm comprises various vision models.
- CogVideoX-5B. CogVideo 5B model will launch next week in Hugging Face Diffusers.
- Anthropic Quickstarts. Anthropic has made available a helpful selection of initial projects. It collaborated with former chief AI officers from Brex, Uber, Facebook, and other companies to draft the first Quickstart, a Claude-powered scalable customer support assistant.
- The Missing Guide to the H100 GPU Market. This guide covers all the important factors of buying a GPU, such as availability considerations, pricing for various alternatives, and guaranteeing reliability in addition to highlighting the significance of other hardware features. It answers the most important queries consumers have about GPUs, including pricing, performance, and shipping.
- Efficient Camera Exposure Control for Visual Odometry via Deep Reinforcement Learning. A deep reinforcement learning framework is being developed in this research to enhance the stability of visual odometry (VO) systems in difficult-to-light settings.
- Multi-scale Cross-restoration Framework for Electrocardiogram Anomaly Detection. a sophisticated ECG diagnosis system that enhances the identification of uncommon but serious cardiac anomalies by self-supervised anomaly detection pertaining.
- RWKV.cpp. The great RWKV models have included a local inference model with its CPP project.
- MAPF-GPT. A novel learning-based method called MAPF-GPT has been developed to tackle the difficult multi-agent pathfinding (MAPF) problem. The model navigates agents by imitation learning; it does not require extra heuristics, reward functions, or communication.
- EnsLoss. An ensemble approach called EnsLoss integrates loss functions into the Empirical Risk Minimization (ERM) paradigm.
- Disentangled Motion Modeling for Video Frame Interpolation. MoMo is a novel diffusion-based approach for video frame interpolation (VFI). It enhances visual quality by focusing on intermediate motion modeling through a disentangled two-stage training process.
- repo2vec. Repo2vec is a new package that functions similarly to GitHub Copilot but with up-to-date repo information, making it simple to communicate with any public or private codebase.
- Building LLMs from the Ground Up: A 3-hour Coding Workshop. Great resource about LLM building from scratch
- SGLang v0.3 Release. The most recent release brings enhancements to SGLang inference, including Multi-Image/Video LLaVA-OneVision, 1.5x Faster torch.compile, and 7x Faster DeepSeek MLA.
- OLMoE: Open Mixture-of-Experts Language Models. Best in-class performance for 1B activated parameters in an excellent open MoE.
- StyleTokenizer: Defining Image Style by a Single Instance for Controlling Diffusion Models. This work presents StyleTokenizer, an approach that aligns style representation with text prompts to improve style control in text-to-image generation.
- Applied Machine Learning (Cornell CS5785, Fall 2024). Open resources for the Fall 2024 Applied ML class at Cornell.
- Laminar — Open-Source observability, analytics, evals and prompt chains for complex LLM apps. Laminar hosts background job queues of LLM pipelines. Outputs of those pipelines are turned into metrics.
- LongLLaVA. A multimodal model called LongLLaVA was created to handle long-context tasks like comprehending high-resolution images and videos.
Perspectives
- I learned the language of computer programming in my 50s — here’s what I discovered. A writer with no technical background recounts his incredible journey into the realm of coding and the invaluable lesson it taught him about the modern world
- Why A.I. Isn’t Going to Make Art. To create a novel or a painting, an artist makes choices that are fundamentally alien to artificial intelligence.
- Autonomous car bombs, online recruitment: Experts worry how AI can transform terrorism. Law enforcement has to anticipate novel AI uses and develop countermeasures
- Researchers built an ‘AI Scientist’ — what can it do? The large language model does everything from reading the literature to writing and reviewing its own papers, but it has a limited range of applicability so far.
- The Next Generation Pixar: How AI will Merge Film & Games. With its ability to combine dynamic gaming engagement with narrative depth, generative AI has the potential to completely transform storytelling. This change is being accelerated by recent developments in generative models, such as Luma AI’s Dream Machine and OpenAI’s Sora, which allow for the creation of interactive videos in real-time. This development, which combines AI, gaming, and film, could result in the next “Pixar” in interactive media.
- China’s robot makers chase Tesla to deliver humanoid workers. At the World Robot Conference in Beijing, more than 25 Chinese businesses featured humanoid robots designed for factory automation. These companies were supported by significant government funding and took advantage of China’s extensive supply network. By 2035, the market for humanoid robots is expected to reach $38 billion globally. By 2025, China hopes to have these robots in large quantities, stepping up the battle with Tesla’s planned Optimus robot. Tesla expects to roll out 1,000 Optimus robots in its factories over the course of the next year, while Chinese companies are predicting substantial cost savings on their models.
- Why AI can’t spell ‘strawberry’. Because of their tokenization techniques, large language models occasionally perform poorly on tasks like letter counting. This demonstrates how the LLM architecture has shortcomings that impact how well they comprehend text. Nevertheless, developments are still being made. For example, Google DeepMind’s AlphaGeometry 2 for formal math and OpenAI’s Strawberry for enhanced reasoning
- Diffusion is spectral autoregression. It’s common knowledge that auto-regressive models and diffusion models are essentially distinct types of methodologies. When it comes to diffusion models that genuinely take auto-regressive steps in the frequency domain, they might, in fact, be more comparable than we previously realized.
- Can AI Scaling Continue Through 2030? AI training is expanding at a rate that has never been seen before — four times faster than previous technology advances in genome sequencing and mobile use. According to research, the main limitations in scaling AI training could last until 2030 and are related to power availability and chip production capacity. If hundreds of billions are committed, training runs up to 2e29 FLOP would become feasible, representing significant advancement comparable to the transition from GPT-2 to GPT-4. Advanced network topologies and multimodal and synthetic data production methodologies might help overcome difficulties like data shortages and latency.
- GPU Utilization is a Misleading Metric. Although frequently tracked, GPU utilization may not fully capture GPU performance in machine learning workloads since it does not take into consideration whether the GPU’s computational power is being utilized to its fullest. Trainy found this out when, during LLM training, 100% GPU usage was achieved, but only ~20% model FLOPS utilization (MFU) was achieved. It suggests using fused kernel optimization and the appropriate model parallelism level to obtain a 4x speedup in training time and tracking SM efficiency for a better performance indication.
- AI-Implanted False Memories. In simulated criminal witness interviews, generative chatbots driven by massive language models greatly increased the generation of false memories, inducing roughly three times more instantaneous false recollections than a control group, according to a study by MIT Media Lab.
- The biology of smell is a mystery — AI is helping to solve it. Scientists are beginning to crack the fiendishly complex code that helps us to sense odours.
- How much is AI hurting the planet? Big tech won’t tell us. big tech companies, like Google, are not disclosing the full environmental impact of AI, while emissions from their operations have significantly increased, with Google’s greenhouse gas emissions rising by 48% between 2019 and 2023
- AI Has Created a Battle Over Web Crawling. A research by the Data Provenance Initiative cautions that when websites restrict crawler bots more and more, high-quality data may become inaccessible to generative AI models. This trend, which is motivated by worries about data exploitation, may cause AI training to rely more on low-quality data rather than well-maintained sources. Businesses may use direct licensing or synthetic data to preserve the effectiveness of AI models in the face of increasing data scarcity.
- What Succeeding at AI Safety Will Involve. Sam from Anthropic hazard a guess as to what will have to be done in order for AI safety to be successful while creating superhuman AI systems.
- the art of programming and why i won’t use llm. Although LLMs are praised for increasing productivity and are being incorporated into coding workflows more and more, some contend that their programming effectiveness is overstated.
- ‘He was in mystic delirium’: was this hermit mathematician a forgotten genius whose ideas could transform AI — or a lonely madman? In isolation, Alexander Grothendieck seemed to have lost touch with reality, but some say his metaphysical theories could contain wonders
- AI Checkers Forcing Kids To Write Like A Robot To Avoid Being Called A Robot. Can the fear of students using generative AI and the rise of questionable AI “checker” tools create a culture devoid of creativity?
- The AI Arms Race Isn’t Inevitable. Prominent AI labs are pushing Western governments to support swift AI developments in order to prevent rivals like China from gaining a decisive technological advantage. They are increasingly portraying AI research as a geopolitical zero-sum game crucial for national security. This story supports drastic steps to ensure AI domination, even at the expense of escalating geopolitical tensions and possibly jeopardizing safety and ethical standards.
- Is AI eating all the energy? AI’s total energy footprint is influenced by both rising demand and rising energy efficiency. Power, heat, carbon, and water use are all positively connected with AI’s energy consumption. The general trend of AI processing becoming more power-hungry is being countered by hardware efficiency improvements. Although its influence is lessened by broad use, AI still accounts for a small but growing portion of data center power consumption, with training activities using a lot more energy than inference.
- Debate over “open source AI” term brings new push to formalize definition. In an effort to clarify the meaning and address the term’s overuse, the Open Source Initiative (OSI) published a proposed definition of “open source AI” that includes usage rights, study, modification, and sharing freedoms. With this step, researchers and engineers will be able to assess AI systems in a more transparent manner. In October, a stable version of the definition is anticipated, which may have an impact on upcoming releases of AI models and regulations.
- Predicting AI. This author considers their forecasts for AI and notes that they were correct to predict the growth of open source, multimodal models, and improved tool usability.
- Bill Gates has a good feeling about AI. The Verge spoke with Bill Gates about AI, misinformation, and climate change.
- Enterprise AI Infrastructure: Privacy, Maturity, Resources. An interesting interview with BentoML’s CEO discusses how to enhance business tooling, make sure you can expand, and avoid over-engineering it from the start.
Meme of the week
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