In Part 1 of this article, we spoke to Kevin Gordon of NexOptic – a company working to solve the problems of enhanced visibility and pixel-level clarity for an initiative on self-driving cars that goes by the name of ALIIS (Alice), based in South Korea. Kevin explained how NexOptic – originally an optics company – came to be solving the problems that might well one day render self-driving cars the reality we've been promised is coming for a decade. We learned of the trials of getting noisy images cleaned, and the setbacks the company had had with scaling its initial process. In particular, Kevin explained that the South Korean government had been helpful in facilitating the dataset build out for such projects, allowing younger companies at the forefront of innovation to get on board. That's quite the contrast to the way things have gone with Western developments in self-driving cars, isn't it?

As Artificial Intelligence moves from the world of academia to commercial applications, we see the technology sector fragment into categories. Decision Intelligence (DI) is the application of AI to the decision-making process. It is industry- and department-agnostic – decisions are the one thing every function in every business has in common. Gartner predicts that over a third of large organizations will be using DI by 2023, and pioneers like Peak are firmly of the opinion that this is the route by which most businesses will adopt AI. But what is meant by Decision Intelligence?

Job interviews are rarely fun for anyone on either side of the table. For candidates, there's the pressure to perform, to walk the tightrope between doing and saying the right things and being enough of "themselves" to give the recruiter or the team leader a true sense of what they'll bring to the team. And for recruiters and managers, they involve taking time out from their other duties, matching resumes with actual candidates, and getting a sense for who fits which role – if they do at all. Artificial intelligence (AI) has been used in recruitment for some time, usually as a first-stage filter for potentially hundreds of resumes – though it's had a very checkered history, which has included imposing a kind of'digital bigotry' or'digital misogyny.' But both AI and virtual reality could have an increasing role to play in making sure your company gets the staff it needs – in a recruitment process that stands a chance of equalizing the playing field and erasing some ingrained prejudices.

We live in a fast-moving world that not long ago would have been considered science fiction. One aspect of technology that has driven us from fantasy into reality is the emergence of autonomous vehicles. Also known as self-driving cars, autonomous vehicles still confuse many people. How does the technology work? And what do we actually mean by work in the context of self-driving cars?

As the global population has expanded over time, modernizing agriculture with the aid of innovations like AI has been humanity's prevailing approach to staving off famine. A variety of mechanical and chemical innovations delivered during the 1950s and 1960s represented the third agricultural revolution. The adoption of pesticides, fertilizers and high-yield crop breeds, among other measures, transformed agriculture and ensured a secure food supply for many millions of people over several decades. Concurrently, modern agriculture has emerged as a culprit of global warming, responsible for one-third of greenhouse gas emissions, namely carbon dioxide and methane. Meanwhile, inflation on the price of food is reaching an all-time high, while malnutrition is rising dramatically.

What happens when AI and ML turn rogue? Artificial intelligence (AI), for years, has been a wide-ranging tool that changes how we integrate information, analyze data, and use the resulting insights to improve decision making. However, just like how the advent of AI will lead to groundbreaking possibilities in a host of fields, cyber threat actors can also use this technology to wreak havoc. In fact, more and more organizations are beginning to employ machine learning (ML) and AI as part of their defenses against cyber threats. In a recent report, Bloomberg noted that the combination of AI and cybersecurity is inevitable as both fields sought better tools and new uses for their technology.

Cybersecurity teams have always had to adapt to new attack methods and change the tools they use to fit the organization's processes better. A prime example of adapting to fit ways of work is the increased preponderance of cloud-based business services and applications. If most of the company's work takes place on web-based SaaS platforms, perimeter-based cybersecurity protection loses importance, and CISOs start to look at cloud-based zero-trust frameworks, for example. Similarly, as more companies move their workflows to Google Suite or Office 365, the secure email gateway that protected the on-prem email server and clients gets mothballed in favor of ICES (integrated cloud email security) solutions. At the same time, agent-based endpoint protection that uses heuristic scanning or rule-based algorithms with pushed/pulled updates are proving more ineffective against very, very smart phishing attacks that exploit weaknesses in every device's "biological interface." User education in online hygiene may have a role in solving that problem, but even seasoned cybersecurity veterans reading these pages will know that they too have, in a moment of inattention, clicked the odd suspect link.

Autonomous driving regulators in the US have finally made it official: fully self-driving transportation in the country will no longer have to equip human driving controls in order to meet traditional road safety standards. The momentous rule change means that for the first time, automated vehicle makers will no longer have to include manual driving controls such as brake pedals and steering wheels for vehicles designated fully self-driving, representing a mammoth shift in safety expectations and regulatory oversight of the autonomous driving market. According to the National Highway Traffic Safety Administration (NHTSA), the previous standard that all road vehicles had been held to, had actually been created in an era where fully autonomous driving had not even been conceived of -- essentially, the rules presumed that a vehicle "will always have a driver's seat, a steering wheel and accompanying steering column, or just one front outboard passenger seating position." These regulations have obviously become passe in an era where fully autonomous driving is now a distinct possibility, but even though the rules have been revised, lingering questions concerning road safety still remain. Last month, General Motors' self-driving subsidiary Cruise petitioned the NHTSA to approve and fully deploy its fully-autonomous Origin into full commercial operations -- a problem at the time since Origin did not come with human-centered features, like a steering wheel or a sun visor.

Jaguar Land Rover, the UK's largest automotive manufacturer, has formed a multi-year strategic partnership with NVIDIA to deploy artificial intelligence (AI) into self-driving cars and create stunning virtual reality experiences. This announcement is a game-changer for the automotive industry as the all-new Jaguar, and Land Rover vehicles will be built on the NVIDIA DRIVE software-defined platform. The platform will deliver a broad spectrum of active safety, automated driving and parking systems, and driver assistance systems. The system will deliver AI features inside the vehicle, including driver and occupant monitoring and advanced visualization of the vehicle's environment. This full-stack solution is based on NVIDIA DRIVE Hyperion, which features DRIVE Orin centralized AV computers; DRIVE AV and DRIVE IX software; safety, security, and networking systems; plus surround sensors.

Insurance technology brings in more innovative solutions for homeowners and insurers thanks to artificial intelligence (AI). AI is disrupting the industry by allowing for faster and more user-friendly claims and creating more transparent and customized policies that suit the client's situation. This replaces the traditionally long and painful process of getting a claim or settling for a one-size-fits-all property insurance policy. AI Property from Tractable, for example, allows anyone with a smartphone to access damage quickly and efficiently to buildings caused by hurricanes, floods, and other natural disasters. Use its mobile-friendly web-based app to take photos of the external conditions and submit them to Tractable's AI platform, trained on an extensive database of claims and damaged property.