✂️✂️ ML Talent Layoffs and Priorities Reset
Weekly news digest curated by the industry insiders
We are living through one of the most impressive corrections in technology market valuations of the last two decades. Since the 2008/09 financial crises, public markets have been in a 13-year bull run that has concurred with the golden era of machine learning (ML). As a result, most tech companies hired ML talent aggressively and embarked on super-ambitious AI projects. In recent days, we have witnessed the shutdown of innovative AI startups such as Argo AI and Kittyhawk, as well as massive ML talent layoffs from companies like Stripe, Meta, SoundHound, and many others. These layoffs have, awkwardly, coincided with sizable funding rounds by ML startups in areas such as generative AI.
What’s really happening with ML talent?
The explanation is that ML focus is changing given the current market conditions. During the bull market, large tech companies tended to over-hire ML talent, and funding was available for capital-intensive ML efforts such as self-driving cars. However, the strong economic headwinds and the massive downturn in tech public equities have forced companies to reevaluate their ML priorities. With most self-driving cars or self-flying taxi startups failing to deliver on their original vision, venture capital has been fluctuating towards ML areas that can bring value in the short term, such as generative AI. At the same time, the layoffs of ML talent in tech giants have pushed talent toward VC-backed startups. The result is a rebalance in the distribution of ML talent from tech incumbents to VC-backed startups and from long-term super-ambitious projects to more practical initiatives. As world economies fight their way out of the current downturn, the talent balance will likely shift again.
🔺🔻TheSequence Scope – our Sunday edition with the industry’s development overview – is free. To receive high-quality content about the most relevant developments in the ML world every Tuesday and Thursday, please subscribe to TheSequence Edge 🔺🔻
🗓 Next week in TheSequence Edge:
Edge#243: we recap our longest and the most popular series about text-to-image synthesis models. Subscribe to receive them all!
Edge#244: we deep dive into ReAct, a Google model that combines reasoning and acting in a single language model
📌 Real-time Machine Learning with Hopsworks*
Real-time ML is a challenging system’s domain, but one where huge value can be created, as shown by companies such as TikTok. The best-personalized search and recommendation systems are based on real-time ML with a Feature Store, a Vector Database, and a Model Serving platform, serving recommendations on-demand taking into account user history and context (such as ‘trending’ content).
In this webinar, Javier de la Rúa Martínez, Software Engineer at Hopsworks, will analyze the synergies of an integrated Feature Store and Model Serving platform for the operationalization of real-time ML-enabled services, including the key MLOps principles needed to ensure integrated version management for upgrading and downgrading models and the features that feed them. He will also show an implementation of a real-time, personalized recommendation system using Hopsworks.
Now, let’s review the most important developments in the AI industry this week
🔎 ML Research
Emergent Abilities in Large Language Models
Google Research published an article about research that unveils emerging abilities in large language models that don’t surface in smaller models →read more
Image to 3D
Google Research published a paper detailing InfiniteNature-Zero, a model that can generate 3D representations from images of natural scenes →read more
Microsoft Research team discusses their vision for applying AI to enable the generation of ITops solutions →read more
Bias in Face Recognition Systems
Amazon Science published a paper detailing a model that can predict bias in face recognition systems using unlabeled data →read more
🤖 Cool AI Tech Releases
At its Universe conference, GitHub discussed new features for CoPilot, its generative code solution powered by OpenAI Codex. The new capabilities include a voice code generation interface and an enterprise license model →read more
🛠 Real World ML
AutoML at Walmart
Walmart discusses WALTS, an AutoML framework powering many of their internal ML workflows →read more
💸 Money in AI
Drug discovery company Insilico Medicine signed a strategic research collaboration with Sanofi worth up to $1.2 billion. The collaboration will leverage Insilico Medicine’s AI platform, Pharma.AI, to advance drug development candidates for up to six new targets.