The cardinal focus of every business is to boost productivity, increase revenue and streamline operations, three things that are imperative to sustain in today's highly-competitive landscape. Over the last few years, many companies have accelerated their digital transformation journey to meet the aforementioned goals effectively. They have started leveraging robotic process automation (RPA), machine learning (ML), artificial intelligence (AI), big data analytics and more such cutting-edge technologies. And while the pandemic has brought drastic changes in our lifestyle, it has also taught us to stay prepared for uncertain and unexpected events. Business leaders are also taking this lesson to heart and using futuristic technologies like automation to remain agile in the unforeseeable future.

Every weekday, The Detail makes sense of the big news stories. This week, we talked about the burgeoning concerns over artificial intelligence, talked to two former political party presidents about their hidden role, visited a food bank operating in the wealthy North Shore, looked at the fight to keep foot-and-mouth disease out of our farms, and finished the week with a new Supreme Court case trying to hold big corporations liable for contributing to climate change. Whakarongo mai to any episodes you might have missed. Artificial intelligence systems running rogue might seem like the stuff of science-fiction, but these systems are increasingly common in many high-tech elements of society, from self-driving cars to digital assistants, facial identification, Netflix recommendations, and much, much more. The capabilities of artificial intelligence are growing at pace; a pace that's outstripping regulatory frameworks.

Earlier this month, Meta (the corporation formerly known as Facebook) released an AI chatbot with the innocuous name Blenderbot that anyone in the US can talk with. Immediately, users all over the country started posting the AI's takes condemning Facebook, while pointing out that, as has often been the case with language models like this one, it's really easy to get the AI to spread racist stereotypes and conspiracy theories. When I played with Blenderbot, I definitely saw my share of bizarre AI-generated conspiracy theories, like one about how big government is suppressing the true Bible, plus plenty of horrifying moral claims. But that wasn't what surprised me. We know language models, even advanced ones, still struggle with bias and truthfulness.

As I write this, Amazon is announcing its purchase of iRobot, adding its room-mapping robotic vacuum technology to the company's existing home surveillance suite, the Ring doorbell and prototype aerial drone. This is in addition to Amazon already knowing what you order online, what websites you visit, what foods you eat and, soon, every last scrap of personal medical data you possess. The trend of our gadgets and infrastructure constantly, often invasively, monitoring their users shows little sign of slowing -- not when there's so much money to be made. Of course it hasn't been all bad for humanity, what with AI's help in advancing medical, communications and logistics tech in recent years. In his new book, Machines Behaving Badly: The Morality of AI, Scientia Professor of Artificial Intelligence at the University of New South Wales, Dr. Toby Walsh, explores the duality of potential that artificial intelligence/machine learning systems offer and, in the excerpt below, how to claw back a bit of your privacy from an industry built for omniscience. Published by La Trobe University Press. The Second Law of Thermodynamics states that the total entropy of a system – the amount of disorder – only ever increases.

Republican Rep. Greg Steube weighs in on the U.S.'s announcement for an additional $775 million in new Ukraine military aid, along with the impact of illegal immigration on Florida's coastal border. The U.S. reportedly is set to provide Ukraine with M982 Excalibur munitions. "These precision-guided munitions will aid Ukraine's counter-offensive against Russia, which is focused on targeting ammunition depots and military installations," foreign relations expert, Samuel Ramani, tweeted. "Scan Eagle surveillance drones, which the U.S. also provides, will amplify their impact." The Ukrainian outlet appeared to draw from reporting by Politico, tweeting: "A source close to the deliberations of the latest military aid package shared with Politico that the M982 Excalibur rounds, traveling up to 70km, will be sent to Ukraine'at some point in the future.'"

Although most cultures, particularly in the west, provide a great many commemorations of distant ancestors – statues, portraits, buildings – we are much less willing to consider our far-off descendants. We might invoke grandchildren, at a push great-grandchildren, but after that, it all becomes a bit vague and, well, unimaginable. And while we look with awe and fascination at the Egyptian pyramids, built 5,000 years ago, we seem incapable of thinking, or even contemplating, 5,000 years in the future. That lies in the realm of science fiction, which is tantamount to fantasy. But the chances are, barring a global catastrophe, humanity will still be very much around in 5,000 years, and going by the average existence of mammal species, should still be thriving in 500,000 years. If we play our cards right, we could even be here in 5m or 500m years, which means that there may be thousands or even millions times more human beings to come than have already existed.

This is the third in a series of posts about how international competition could reshape the U.S. economy. As you might expect from the picture at the top of this post, I'm a little ambivalent when it comes to breathless reports that America is falling behind its rivals technologically. That picture is from the movie Dr. Strangelove, which (among other things) lampooned America's Cold War obsession with the "missile gap". On one hand, in a geopolitical contest such as the one we now find ourselves in with Russia and China, we need to prioritize which battles to concentrate resources on -- a point made very convincingly by Hal Brands and Michael Beckley's new book Danger Zone. But on the other hand, our obsession with the "missile gap" gave us the space race and the moon landings and all the technological spinoffs from those, plus a boost to our semiconductor industry.

Recently, I had the honor of addressing the "Commercialising Quantum" conference on how organisations should prepare for quantum today, held under the stewardship of The Economist's editors. My message was that some aspects of quantum technologies are on the horizon, and we should be prepared to invest ensure that quantum capabilities for economic prosperity and national security are developed accordingly.

Indian law enforcement is starting to place huge importance on facial recognition technology. Delhi police, looking into identifying people involved in civil unrest in northern India in the past few years, said that they would consider 80 percent accuracy and above as a "positive" match, according to documents obtained by the Internet Freedom Foundation through a public records request. Facial recognition's arrival in India's capital region marks the expansion of Indian law enforcement officials using facial recognition data as evidence for potential prosecution, ringing alarm bells among privacy and civil liberties experts. There are also concerns about the 80 percent accuracy threshold, which critics say is arbitrary and far too low, given the potential consequences for those marked as a match. India's lack of a comprehensive data protection law makes matters even more concerning.

Anti-cancer drug discoveries have been serendipitous, we sought to present the Open Molecular Graph Learning Benchmark, named CandidateDrug4Cancer, a challenging and realistic benchmark dataset to facilitate scalable, robust, and reproducible graph machine learning research for anti-cancer drug discovery. CandidateDrug4Cancer dataset encompasses multiple most-mentioned 29 targets for cancer, covering 54869 cancer-related drug molecules which are ranged from pre-clinical, clinical and FDA-approved. Besides building the datasets, we also perform benchmark experiments with effective Drug Target Interaction (DTI) prediction baselines using descriptors and expressive graph neural networks. Experimental results suggest that CandidateDrug4Cancer presents significant challenges for learning molecular graphs and targets in practical application, indicating opportunities for future researches on developing candidate drugs for treating cancers.