Welcome back to, TIME's new twice-weekly newsletter about AI. If you're reading this in your browser, why not subscribe to have the next one delivered straight to your inbox? The three most valuable private companies in the U.S. have big reputations: OpenAI, SpaceX, and Anthropic. But the fourth, Databricks, flies a little more under the radar. This company, which is currently raising funds at a valuation of $134 billion according to reports this week, is a quiet workhorse of the AI revolution.

In the span of just a few years, drones have become instrumental in warfare. Conflicts in Ukraine, Iran, Nagorno-Karabakh, Sudan, and elsewhere have shown how autonomous vehicles have become a quintessential part of modern combat. It's a fact that Taiwan knows all too well. The island nation, fearing imminent invasion from China, has both the need, know-how, and industry necessary to build a robust and advanced drone program. Yet Taiwan, which has set an ambitious target of producing 180,000 drones per year by 2028, is struggling to create this industry from scratch.

The Biden Administration's move on Jan. 13 to curb exports on the advanced computer chips used to power artificial intelligence (AI) arrived in the wake of two major events over the Christmas holidays that rattled the world of AI. First, OpenAI released its latest model, o3, which achieved an 88% on a set of difficult reasoning tests on which no AI system had previously scored above 5%. "All intuition about AI capabilities will need to get updated" in light of the results, said Francois Chollet, a former AI researcher at Google and a prominent skeptic of the argument that "artificial general intelligence" (AGI) would be achieved any time soon. Second, the Chinese company DeepSeek released an open-source AI model that outperformed any American open-source language model, including Meta's Llama series. The achievement surprised many AI researchers and U.S. officials, who had believed China lagged behind in terms of AI capabilities.

Ever since the ChatGPT API opened up, all sorts of apps have been strapping on AI functionality. I've personally noticed this a lot in email clients: Apps like Spark and Canary are prominently bragging about their built-in AI functionality. The most common features will write replies for you, or even generate an entire email using only a prompt. Some will summarize a long email in your inbox or even a thread. It's a great idea in the abstract, but I think integrations like these conspire to make communication less efficient instead of more efficient. You should feel free to try such features--they're fun!--but don't expect them to change your life.

For corporate America, the biggest trend to latch onto at the moment is artificial intelligence, stoked by the popularity of ChatGPT. But worries about the dangers of widespread A.I. use are growing as well. There's one big hitch: Governments -- notably Washington -- haven't kept pace with regulations for the technology. That could lead to dire consequences: "By failing to establish such guardrails, policymakers are creating the conditions for a race to the bottom in irresponsible A.I.," Carly Kind, the director of the Ada Lovelace Institute, a policy research group, told The Times. Washington has been largely hands off on A.I. rules, even as several lawmakers have pushed to tighten oversight.

At some DHL shipping centers, artificial intelligence now helps employees make sure pallets will load safely into cargo planes. A computer vision system captures each pallet, and an algorithm judges whether it can be stacked with other pallets or may be too awkward to fit on the next flight. DHL is one of a growing number of companies using AI. Besides the pallet scanning system, AI helps route deliveries, control robots that ferry packages around warehouses, and control an experimental robot arm that picks and sorts parcels. DHL is also among a small minority of companies using AI--just 11 percent--that say they've reaped a significant return on investment from using the technology, according to a new report.

At some DHL shipping centers, artificial intelligence now helps employees make sure pallets will load safely into cargo planes. A computer vision system captures each pallet, and an algorithm judges whether it can be stacked with other pallets or may be too awkward to fit on the next flight. DHL is one of a growing number of companies using AI. Besides the pallet scanning system, AI helps route deliveries, control robots that ferry packages around warehouses, and control an experimental robot arm that picks and sorts parcels. DHL is also among a small minority of companies using AI--just 11 percent--that say they've reaped a significant return on investment from using the technology, according to a new report.

Fear of successful cyberattacks meets fear of unintended consequences when machine learning is your first line of defense. Fear can be a great motivator. If you are afraid that a human cannot make a decision fast enough to stop a cyberattack, you might opt for an artificial intelligence (AI), machine learning system. But although fear, uncertainty and doubt -- the FUD factor -- of not responding quickly enough might motivate you to take this action, that same FUD factor that the action your automated system takes might be wrong is an equally strong motivator not to employ this technology. Welcome to this year's Catch 22. In the 1983 sci-fi classic War Games, a computer was employed to replace the soldiers who manned the intercontinental ballistic missile silos because, it was believed, the computer could launch the missiles dispassionately and not be swayed by indecision in case of a nuclear attack. A teenager hacked the system thinking it was an unreleased video game.

Matrix calculations, derivatives, eigenvalues, Set Theory, functions, vectors, linear transformations, etc. are extremely important to understand the theory behind statistical methods and programming. Therefore, before starting your next MOOC or Machine Learning book it's crucial to review all those concepts again. Most schools request students to be proficient at these methods in order to graduate, but the silver lining is that it won't require too much of your time to refresh or obtain this knowledge. There are plenty of resources to start, but what worked for me was The Manga Guide to Linear Algebra, which is very simple, graphic and provides a great foundation prior getting into more complex stuff. My suggestion is to schedule some weeks to review these concepts and to use the Feynman Technique to be able to explain in simple terms each of these topics. One of the issues people face today when trying to get into a field such as Data Science is Information Overload, a term used when talking in relation to the effect of having too many resources at the disposal.

Democratisation of artificial intelligence is making machine learning tools and technologies widely available for masses and businesses at an affordable cost. But what is actually driving this AI democratisation? According to a survey conducted by Qualtric, only 10 percent of internet users worldwide consider themselves as AI experts. On the other hand, 53 per cent said they know about AI, but wouldn't call themselves an expert on the subject. To help provide resources more to the masses to use AI in their work and everyday life, many tech companies are educating and making it more accessible to the masses.