Artificial intelligence has come to change the way we do things and can help us solve many problems in different areas. A major problem we currently face are threats to cybersecurity. Especially now that we live in a world that is permanently connected to the internet, it is more important than ever to make systems secure. Organizations are now expected to increase the use of cryptography to improve cybersecurity. However, to keep up with this process, they will need to incorporate advanced tools, like artificial intelligence (AI), to prevent, detect and remedy potential threats.

Search and tracking is the problem of locating a moving target and following it to its destination. In this work, we consider a scenario in which the target moves across a large geographical area by following a road network and the search is performed by a team of unmanned aerial vehicles (UAVs). We formulate search and tracking as a combinatorial optimization problem and prove that the objective function is submodular. We exploit this property to devise a greedy algorithm. Although this algorithm does not offer strong theoretical guarantees because of the presence of temporal constraints that limit the feasibility of the solutions, it presents remarkably good performance, especially when several UAVs are available for the mission. As the greedy algorithm suffers when resources are scarce, we investigate two alternative optimization techniques: Constraint Programming (CP) and AI planning. Both approaches struggle to cope with large problems, and so we strengthen them by leveraging the greedy algorithm. We use the greedy solution to warm start the CP model and to devise a domain-dependent heuristic for planning. Our extensive experimental evaluation studies the scalability of the different techniques and identifies the conditions under which one approach becomes preferable to the others.

From massive face-recognition-based surveillance and machine-learning-based decision systems predicting crime recidivism rates, to the move towards automated health diagnostic systems, artificial intelligence (AI) is being used in scenarios that have serious consequences in people's lives. However, this rapid permeation of AI into society has not been accompanied by a thorough investigation of the sociopolitical issues that cause certain groups of people to be harmed rather than advantaged by it. For instance, recent studies have shown that commercial face recognition systems have much higher error rates for dark skinned women while having minimal errors on light skinned men. A 2016 ProPublica investigation uncovered that machine learning based tools that assess crime recidivism rates in the US are biased against African Americans. Other studies show that natural language processing tools trained on newspapers exhibit societal biases (e.g. finishing the analogy "Man is to computer programmer as woman is to X" by homemaker). At the same time, books such as Weapons of Math Destruction and Automated Inequality detail how people in lower socioeconomic classes in the US are subjected to more automated decision making tools than those who are in the upper class. Thus, these tools are most often used on people towards whom they exhibit the most bias. While many technical solutions have been proposed to alleviate bias in machine learning systems, we have to take a holistic and multifaceted approach. This includes standardization bodies determining what types of systems can be used in which scenarios, making sure that automated decision tools are created by people from diverse backgrounds, and understanding the historical and political factors that disadvantage certain groups who are subjected to these tools.

The Federal Trade Commission is hosting a public workshop Wednesday on video game "loot boxes" amid backlash from players claiming it can create a "pay to win" structure. The workshop will bring together industry experts, consumer advocates and others "to discuss concerns regarding the marketing and use of loot boxes and other in-game purchases, and the potential behavioral impact of these virtual rewards on young consumers," according to an event description on the FTC's website. The concept has generated angst among video game players concerned it encourages a "pay to play" atmosphere where gamers must spend money to gain a competitive edge. Loot boxes are rewards players receive within video games containing random prizes. For example, in the Activision Blizzard game Overwatch, loot boxes generate stickers, quotes from characters, and special costumes.

What happens when injustices are propagated not by individuals or organizations but by a collection of machines? Lately, there's been increased attention on the downsides of artificial intelligence and the harms it may produce in our society, from unequitable access to opportunities to the escalation of polarization in our communities. Not surprisingly, there's been a corresponding rise in discussion around how to regulate AI. Do we need new laws and rules from governmental authorities to police companies and their conduct when designing and deploying AI into the world? Part of the conversation arises from the fact that the public questions -- and rightly so -- the ethical restraints that organizations voluntarily choose to comply with. According to Edelman's 2019 Trust Barometer global survey, only 56% of the general public has overall trust in the business community.

Over the weekend a Saildrone -- a 23-foot long uncrewed marine robot -- withstood the tempestuous seas around Antarctica to complete the first-ever circumnavigation of the continent by a drone. National Oceanic and Atmospheric Administration (NOAA) scientists collaborated with autonomous vehicle specialists, Saildrone, to test whether the seafaring robot could survive the rough waters, and make successful scientific observations. NOAA needs to gauge how much carbon dioxide -- the potent greenhouse gas now amassing in the atmosphere -- the southern seas are absorbing from the air, and it hopes Saildrones can help. Overall, the oceans soak up a huge amount of the CO2 that humanity emits into the atmosphere (some 30 percent), which has substantially curbed Earth's accelerating temperature rise. Now, understanding how much carbon the oceans will likely soak up in the future is critical to grasping how Earth's increasingly disrupted climate will transform society and the natural world.

The government has announced its third successive hand-out to the NHS in as many days with a pledge by Boris Johnson of £250m to be invested in artificial intelligence. The prime minister claimed AI would transform care and cut waiting times as he announced the money for a national artificial intelligence lab, to work on digital advances to improve the detection of diseases by predicting who is most likely to get them. However, health experts warned that the NHS had a poor record with technology and any new systems would need "robust evaluation" to ensure they did more good than harm as well as proper implementation with safety standards and training. They also raised concerns over where the money was coming from and whether it was the result of trade-offs elsewhere in the cash-strapped health service. AI is already being used in some hospitals to predict cancer survival and cut the number of missed appointments.

Pooled development investment fund, Strategic Elements, has added another string to its bow with the launch of a new artificial intelligence and robotics company, Stealth Technologies. Stealth is 100% venture-backed by Strategic and seeks to develop proprietary technologies and collaborate with commercial and government partners. Management said that whilst most artificial intelligence companies focus only on software development, Stealth's multi-disciplinary capabilities allow it to custom build automated robots and create artificial intelligence through machine learning and software development. This includes developing proprietary computer vision technologies, a branch of artificial intelligence that uses machine learning to quickly process and analyse visual data from photos or video like the human visual system can do. Machine learning involves the use of algorithms to perform specific tasks without explicit external instruction.

It was not a surprise to see Donald Trump and a cabal of other Republican politicians seeking to implicate video games in the US's latest mass shootings. The idea that young men can be driven to kill by Doom, Call of Duty or Fortnite is a seductive one: it's simple, it ties in with fears that older voters harbour about digital culture and screen time, and it conveniently draws attention away from more complex societal concerns such as poverty, neglect, easy access to deadly firearms and a violently confrontational political culture. There's just one problem: despite years of research and hundreds of studies, there is no compelling evidence that video game violence causes real-life bloodshed. Every time these claims are made, the industry seems unwilling to analyse or engage with the reasons why games are so often implicated in violent acts. The standard response is blanket outrage and denial – games don't cause real-world violence, they're "apolitical" fun, so we don't have to think about the issue, we don't have to consider how the shooters portray or utilise military violence.

In recent years, deep learning and machine learning have gained traction in so many areas that have a direct positive effect on our lives as well as complex tasks such as computer vision (image recognition), machine translation, and natural language processing. And with like so many other technologies that are changing our lives for good, it has the destructive potential to change it for bad, there is no reason why it won't also be used for malicious activities as well. Up until now, we haven't seen the use of AI for malicious activities in cybersecurity due to the high costs, lack of skills and the tools available. But just like any other technology, it's a matter of time before it happens in cybersecurity. Think about what would happen when attackers start using the power of deep learning and machine learning for their advantage?