The soldier who is a split second quicker on the draw may walk away from a firefight unscathed; the ship that sinks an enemy vessel first may spare itself a volley of missiles. In cases where humans can't keep up with the pace of modern conflict, machines step in. When a rocket-propelled grenade is streaking toward an armored ground vehicle, an automated system onboard the vehicle identifies the threat, tracks it, and fires a countermeasure to intercept it, all before the crew inside is even aware. Similarly, US Navy ships equipped with the Aegis combat system can switch on Auto-Special mode, which automatically swats down incoming warheads according to carefully programmed rules. These kinds of defensive systems have been around for decades, and at least 30 countries now use them.
Companies using artificial intelligence (AI) across their business units should consider creating a C-suite position to oversee how AI is used and guard against the risk of making bad decisions based on biased algorithms, experts say. Only a few companies, like Levi Strauss & Co, have established a chief artificial intelligence officer (CAIO) position, and fewer have created a C-level position dedicated solely to AI ethics. Brian Kropp, chief of research in the HR practice at Gartner, said chief technology officers and chief information officers will struggle with handling AI-related decisions and ethical dilemmas. "CTOs and CIOs are going to be thinking about the role through the lens of how they can make the technology work," Kropp said. However, "artificial intelligence is not a question of how you get the technology to work; it's a question of how do you think through the implications of the technology?"
"If you look at the key names in the global debate on AI ethics, it is in fact dominated by women who have many different types of backgrounds, not only tech backgrounds." Artificial Intelligence (AI) is the game-changer in the industry, turbocharging new use cases in transportation, law enforcement, e-commerce, retail, healthcare, and entertainment. However, the quick pace of transformation and adoption is not accompanied by concrete industry standards on AI ethics and fairness in Machine Learning algorithms. While ethics in AI have been a dominant narrative for sometime, Big Tech is still seeking ways to design a code of conduct when building ML algorithms. Some tech giants like Microsoft have laid down guidelines to responsible AI and has operationalized responsible AI at scale, others are yet to follow suit.
Last week, Microsoft gathered experts from academia, civil society, policy making and more to discuss one of the most important topics in tech at the moment: responsible AI (RAI). Microsoft's Data Science and Law Forum in Brussels was the setting for the discussion, which focused on rules for effective governance of AI. Whilst AI governance and regulation may not be everyone's cup of tea, the event covered an array of subjects where this has become a red hot issue, such as the militarization of AI, liability rules in AI systems, facial recognition technology and the future of quantum computing and more. The event also gave Microsoft an opportunity to showcase its strategy around this important area. A few highlights are worth sharing, so let's dig a bit deeper into what Microsoft is doing in RAI, why it's important and what it means for the market moving forward.
The statistical branch of Artificial Intelligence has enamored organizations across industries, spurred an immense amount of capital dedicated to its technologies, and entranced numerous media outlets for the past couple of years. All of this attention, however, will ultimately prove unwarranted unless organizations, data scientists, and various vendors can answer one simple question: can they provide Explainable AI? Although the ability to explain the results of Machine Learning models--and produce consistent results from them--has never been easy, a number of emergent techniques have recently appeared to open the proverbial'black box' rendering these models so difficult to explain. One of the most useful involves modeling real-world events with the adaptive schema of knowledge graphs and, via Machine Learning, gleaning whether they're related and how frequently they take place together. When the knowledge graph environment becomes endowed with an additional temporal dimension that organizations can traverse forwards and backwards with dynamic visualizations, they can understand what actually triggered these events, how one affected others, and the critical aspect of causation necessary for Explainable AI.
The umbrella term for software and hardware it automates the human resources function in organization. One of the most discussed and debated trends of the contemporary times in the HR Technology is the use of Artificial Intelligence (AI). As per recent predictions, AI is going to be the crunch point, in terms of productivity for HR professionals. It has been feared by many professionals that machine is going to take away their jobs. Basically there is no reason to be cautiously optimistic; this is quite early to predict the actual impact of AI in HR and Talent Acquisition.
Almost every industry is seeking top quality Artificial Intelligence (AI) and analytics professionals across the world. Apart from top academic institutions, industry has also been targetting scientific research labs in order to tap those who possess competencies in quantitative techniques proficient in building models and are getting them oriented to design business solutions. The AI as a service market size was valued at $1.13 billion in 2017 and is expected to be $10.88 billion by 2023, thus opening up a huge demand for AI talent pool. The AI-powered services in the form of Application Programming Interface (API) and Software Development Kit (SDK) are primarily driving the demand for AI and analytics professionals. In addition to these, startups working on path breaking ideas are also in need of smart data science professionals.
While speaking on a panel recently, Landing AI founder and Google Brain cofounder Andrew Ng described a moment when he read the OECD's AI ethics principles to an engineer, and the engineer told him the words give no instruction on how he should change how he does his job. That's why, Ng said, any code of conduct should be designed by and for ML practitioners. Well, Microsoft Research must've heard that, because it recently created an AI ethics checklist together with nearly 50 engineers from a dozen tech companies. Authors said the checklist is intended to spark conversation and "good tension" within organizations. The list avoids yes or no questions, uses words like "scrutinize," and asks teams to "define fairness criteria."
Despite the hype, AI has had very little measurable effect on the economy. Yes, people spend a lot of time on social media and playing ultra-realistic video games. But does that boost or diminish productivity? Technology in general and AI in particular are supposed to be creating a new New Economy, where algorithms and robots do all our work for us, increasing productivity by unheard-of amounts. The reality has been the opposite.
The pace of worldwide adoption continues to accelerate. Not only are companies and researchers competing with each other for advantage in the world of artificial intelligence and machine learning, so too are entire countries. As such, countries need to figure out what laws and regulations should be put in place to help manage this new emerging technology.