Amman, Jordan – The Israeli army released footage on October 22 of its Maglan commando unit deploying a new precision-guided 120mm mortar bomb called the Iron Sting, against Hamas in Gaza. The bomb's Haifa-based manufacturer, Elbit Systems, has been advertising its qualities on the public relations page of its website since March 2021, when it was integrated into the Israeli military. Benny Gantz, then Israel's defence minister and now a part of Prime Minister Benjamin Netanyahu's war cabinet, described the Iron Sting as "designed to engage targets precisely, in both open terrains and urban environments, while reducing the possibility of collateral damage and preventing injury to non-combatants". It's a claim echoed by Mark Regev, Netanyahu's former spokesperson, for the country's overall approach to its war on Gaza, in which, he has said, Israel is "trying to be as surgical as humanly possible". Yet, more than one month after Israel launched the aerial bombardment of Gaza following a surprise Hamas attack, it has killed at least 11,400 Palestinian civilians, and injured 30,000 in the besieged strip and the occupied West Bank.

Everyday activities like using a GPS to map out the best driving route or translating a document consume energy, water and mineral resources -- lots of it. These applications run in the cloud, a nebulous term for the millions of powerful computers in vast data centers worldwide. Mobile applications depend on legions of computers to store trillions of data and perform split-second operations (e.g. Estimates of the energy consumption of data centers range between 1-2% of total global consumption. All signs indicate that data center energy consumption is about to skyrocket.

In early February, first Google, then Microsoft, announced major overhauls to their search engines. Both tech giants have spent big on building or buying generative AI tools, which use large language models to understand and respond to complex questions. Now they are trying to integrate them into search, hoping they'll give users a richer, more accurate experience. The Chinese search company Baidu has announced it will follow suit. But the excitement over these new tools could be concealing a dirty secret.

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ARTIFICIAL intelligence breakthroughs have become a regular occurrence in recent years. One of the most impressive achievements so far was in 2016, when Google DeepMind's AlphaGo AI beat champion Lee Sedol at one of the world's most complex games, Go. The feat made headlines around the world as an example of machines besting humans, but in some sense it wasn't a fair fight.

Almost no one knows how to utilize the technology at scale. More precisely, only a very small handful of organizations truly understand how to manage the risks of machine learning (ML) when implemented widely. Those risks include navigating the legal, reputational, and ethical issues ML can create – from wildly offensive chatbots and image classifiers to furthering racial disparities amongst zip codes, and much, much more. And that's not even taking into account the deceptively complex requirement of being able to predict how ML models will behave over long periods of time, or new laws like the EU's GDPR and their impact on ML. That's why we're thrilled to partner with the Future of Privacy Forum to release the first-ever guide to managing risk in machine learning, written specifically for practitioners.

Too many businesses now are pitching AI almost as though it's batteries included. I think that's dangerous because it's going to potentially lead to over-investment in things that overpromise. Then when they under-deliver, it has a deflationary effect on people's attitudes toward the space. Not everything requires the latest whiz-bang technology. In fact, the dirty secret of machine learning--and, in a way, venture capital--is so many problems could be solved by just applying simple regression analysis. Yet, very few people, very few industries do the bare minimum.

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