The first day of September should have marked the beginning of one of the busiest periods of the year for Jaguar Land Rover. It was a Monday, and the release of new 75 series number plates was expected to produce a surge in demand from eager car buyers. At factories in Solihull and Halewood, as well as at its engine plant in Wolverhampton, staff were expecting to be working flat out. Instead, when the early shift arrived, they were sent home. The production lines have remained idle ever since.

A few weeks ago, I watched a small team of artificial intelligence agents spend roughly 10 minutes trying to hack into my brand new vibe-coded website. The AI agents, developed by startup RunSybil, worked together to probe my poor site to identify weak spots. An orchestrator agent, called Sybil, oversees several more specialized agents all powered by a combination of custom language models and off-the-shelf APIs. Whereas conventional vulnerability scanners probe for specific known problems, Sybil is able to operate at a higher level, using artificial intuition to figure out weaknesses. It might, for example, work out that a guest user has privileged access--something a regular scanner might miss--and use this to build an attack.

As the summer begins to wane, cases of mosquito-borne diseases are creeping up in some parts of the United States. In other regions, the threat of malaria is a more constant issue even as vaccines continue to roll out. However, some new research on how they mate may help develop better improved techniques for controlling the mosquitoes that carry malaria. For male mosquitoes–who do not bite–the high-pitched buzzing of females is siren call that signals it is time to mate. However, there is even more to that signal than scientists first realized.

As the WEF mentioned, the growth of AI as an adversarial is already happening. Artificially Intelligent viruses and malware are already fighting against their AI cybersecurity counterparts. The initial application of AI in cyber-threat tended to focus on parsing through mountains of seemingly unconnected data to find patterns and relationships that help an attacker find a weak spot. That weak spot could be a way to attack a corporation's network, a pattern of human behaviour, or simply password cracking. However, this is already evolving into more sophisticated methods, as Dark Reading mentions here.

In September 2019, the National Institute of Standards and Technology issued its first-ever warning for an attack on a commercial artificial intelligence algorithm. Security researchers had devised a way to attack a Proofpoint product that uses machine learning to identify spam emails. The system produced email headers that included a "score" of how likely a message was to be spam. But analyzing these scores, along with the contents of messages, made it possible to build a clone of the machine-learning model and craft spam messages that evaded detection. The vulnerability notice may be the first of many.

Amino acids are critical to the structure of proteins, which are often visualized as 3D ribbon structures. Northeastern biochemists are studying the chemistry of amino acids within SARS-CoV-2 to predict the reactions they enable. Chemically speaking, proteins might be the most sophisticated molecules out there. Millions of different kinds of them live within our cells and work together as a fine-tuned orchestra catalyzing the biochemical reactions that keep us alive. Few things in the world would function without proteins--not the cells within your body, and certainly not SARS-CoV-2, the coronavirus responsible for COVID-19.

Big Data is unique in its size and scale. Add machine learning and Data Science, and this sheer volume will make it possible to reach unprecedented levels of accuracy and scope in predictions. When dealing with Big Data, there's no need to worry about insufficient sample sizes or test group results--because the sample size is no less than everything. So it's easy to think that with the famous 3 Vs (or 5, 6, 7 Vs) every single piece of possible input is at your service and disposal, meaning total control over complex, foolproof, endlessly scalable systems and services. However, when such vast and all-encompassing data amounts are processed automatically, numerous issues are bound to surface.

Using AI to analyze professional football players. The use of AI by HR departments is relatively low, but a few examples highlight the potential of the technology to help with talent management, albeit providing the data on performance is available. A domain where performance data is readily available is in football, where stats are generated not only on appearances, goals and other match day data, but also on physical and mental attributes associated with a player. A recent study was able to use this data to identify players whose transfer fees represented good value versus those that were over-priced. The researchers analyzed the salaries and performances of 6,082 professional football players using machine learning.

Have you ever joked that you wished you could clone yourself? Well, it looks like if you're an extremist of any stripe who spends a lot of time on social media, you'll soon be able to fulfill that dream. Swarms of real life, human trolls have already been able to achieve some remarkable things. For example, there's the well-known incident where Time's Man of the Year Poll met 4chan. But real-life trolls have to sleep.