After a particularly lean week for tech news, yesterday exploded. We've got Google's next-generation AI model, Gemini 2.0, a barrage of games to intrigue us in 2025, MasterClass is going AI and, finally, Apple's most headline-grabbing AI tricks and features broke cover, built into the latest iOS update. That's what I want to kick off with. A lot of features in iOS 18.2 are only for the iPhone 15 Pro, 16 and 16 Pro, which pack the necessary chip smarts to run Apple Intelligence. Image Playground, available as a standalone app and through Messages, can generate image suggestions based on your text prompts or contents of your conversations.

The biggest mistake I see tech business owners make when implementing AI is trying to adopt too many different tools at once. AI is a delicate tool that can provide tremendous value to your business, but you have to be attentive and improve it. Some people think AI is "set it and forget it," so they implement many different AI programs at once and ultimately don't see positive results. You must first define the problem you are trying to solve and how you will measure the impact of a solution. I've seen too many companies start AI initiatives without clear objectives, hoping to find something.

The objective of many real-world tasks is complex and difficult to procedurally specify. This makes it necessary to use reward or imitation learning algorithms to infer a reward or policy directly from human data. Existing benchmarks for these algorithms focus on realism, testing in complex environments. Unfortunately, these benchmarks are slow, unreliable and cannot isolate failures. As a complementary approach, we develop a suite of simple diagnostic tasks that test individual facets of algorithm performance in isolation. We evaluate a range of common reward and imitation learning algorithms on our tasks. Our results confirm that algorithm performance is highly sensitive to implementation details. Moreover, in a case-study into a popular preference-based reward learning implementation, we illustrate how the suite can pinpoint design flaws and rapidly evaluate candidate solutions. The environments are available at https://github.com/HumanCompatibleAI/seals .

Donald Trump's latest salvo against China threatens to derail a $1 billion coming-out party for a prominent startup backed by Alibaba Group Holding Ltd., while curtailing the country's broader ambitions of leading artificial intelligence in the coming decade. The US placed eight Chinese technology giants on a US blacklist on Monday, accusing them of being implicated in human rights violations against Muslim minorities in the country's far-western region of Xinjiang. Among those singled out for sweeping American export restrictions were SenseTime Group Ltd., the world's largest AI startup, and Megvii Technology Ltd. -- two giant enterprises Beijing is counting on to spearhead advances into a revolutionary technology, aided by billions of dollars in foreign backing. The White House's actions -- announced days before sensitive trade negotiations resume in Washington -- cast a pall over not just Megvii's capital-raising effort but the burgeoning Chinese sector. Leading players like SenseTime and Megvii, already having trouble securing financing during an economic downturn, had considered international forays to sustain a sizzling pace of growth.

HONG KONG/BEIJING – Donald Trump's latest salvo against China threatens to derail a $1 billion coming-out party for a prominent startup backed by Alibaba Group Holding Ltd., while curtailing the country's broader ambitions of leading artificial intelligence in the coming decade. The U.S. placed eight Chinese technology giants on a U.S. blacklist Monday, accusing them of being implicated in human rights violations against Muslim minorities in the country's far-western region of Xinjiang. Among those singled out for sweeping American export restrictions were SenseTime Group Ltd., the world's largest AI startup, and Megvii Technology Ltd. -- two giant enterprises Beijing is counting on to spearhead advances into a revolutionary technology, aided by billions of dollars in foreign backing. The White House's actions -- announced days before sensitive trade negotiations resume in Washington -- cast a pall over not just Megvii's capital-raising effort but the burgeoning Chinese sector. Leading players like SenseTime and Megvii, already having trouble securing financing during an economic downturn, had considered international forays to sustain a sizzling pace of growth.

Right now, artificial intelligence is used to make snap trading decisions in response to news reports. It could write this press release. Companies are rushing to integrate AI into products and services. In the rush, it is likely many companies – especially entrepreneurial companies - will make costly mistakes. According to Tal, "As executives become enamored and anxious about adopting AI technology, there is an increased chance for making a technology bet that can consume the assets of a company. It is critical that companies map out their opportunities, test and prove a tech integration before making a huge investment that could actually derail a company strategy."

Of course this is the Watson that was built by IBM to understand answers on Jeopardy and come up with the right questions. Since his appearance on the game show in 2011, IBM has expanded Watson's talents, building on the algorithms that allow him to read and derive meaning from natural language. And among other functions, IBM adapted Watson for use in medicine. Toronto Western, part of the University Health Network, is the first hospital in Canada to use Watson for research in Parkinson's, a neurological disorder. The centre has a track record of running clinical trials for off-label drug use, which means taking a drug approved for treatment of one condition and repurposing it for another.

It's been abundantly clear for a while that in 2017, artificial intelligence (AI) is going to be front and center of vendor and enterprise interest. Not that AI is new - it's been around for decades as a computer science discipline. What's different now is that advances in technology have made it possible for companies ranging from search engine providers to camera and smartphone manufacturers to deliver AI-enabled products and services, many of which have become an integral part of many people's daily lives. More than that, those same AI techniques and building blocks are increasingly available for enterprises to leverage in their own products and services without needing to bring on board AI experts, a breed that's rare and expensive. The next wave of IT innovation will be powered by artificial intelligence and machine learning.

If you're not sure whether algorithmic bias could derail your plan, you should be. Megan Garcia (@meganegarcia) is a senior fellow and director of New America California, where she studies cybersecurity, AI, and diversity in technology. Algorithmic bias--when seemingly innocuous programming takes on the prejudices either of its creators or the data it is fed--causes everything from warped Google searches to barring qualified women from medical school. It doesn't take active prejudice to produce skewed results (more on that later) in web searches, data-driven home loan decisions, or photo-recognition software. It just takes distorted data that no one notices and corrects for.