This is based on current regulations from the U.S. Department of Education. Post-Baccalaureate Certificate in Artificial Intelligence and Machine Learning accepts applicants who hold Bachelor degrees in Computer Science, or completed a Post-Baccalaureate Certificate in Computer Science, and offers them opportunities to learn the fundamentals of artificial intelligence and machine learning. The aim is to provide a strong foundation in this emerging area, with a focus on mathematical foundations, algorithms, and real-world applications. The certificate program may also serve as an onramp to a Master of Science in Computer Science, the Master of Science in Data Science, or the Master of Science in Artificial Intelligence and Machine Learning if completed with predetermined grade requirements. Please visit the College of Computing & Informatics website to learn more about admission requirements.

AI is everywhere in tech, and you can bet it will be a vital topic at TechCrunch Disrupt 2021 on September 21-23. As always, every Disrupt features peerless experts and boundary-pushing visionaries and Disrupt 2021 will not disappoint on that or any other score. With more than 80 interviews, panel discussions, events and breakout sessions -- and counting -- we're shining a spotlight on sessions related to AI and robotics. Get amongst it: Buy your pass today, join thousands of attendees from around the world and get ready to learn about the latest in AI, robotics, rockets and so much more. Here are just some of the AI and robotics presentations we have on tap.

For most of the past decade, public concerns about digital technology have focused on the potential abuse of personal data. People were uncomfortable with the way companies could track their movements online, often gathering credit card numbers, addresses, and other critical information. They found it creepy to be followed around the web by ads that had clearly been triggered by their idle searches, and they worried about identity theft and fraud. Those concerns led to the passage of measures in the United States and Europe guaranteeing internet users some level of control over their personal data and images--most notably, the European Union's 2018 General Data Protection Regulation (GDPR). Some argue that curbing it will hamper the economic performance of Europe and the United States relative to less restrictive countries, notably China, whose digital giants have thrived with the help of ready, lightly regulated access to personal information of all sorts. Others point out that there's plenty of evidence that tighter regulation has put smaller European companies at a considerable disadvantage to deeper-pocketed U.S. rivals such as Google and Amazon. But the debate is entering a new phase. As companies increasingly embed artificial intelligence in their products, services, processes, and decision-making, attention is shifting to how data is used by the software--particularly by complex, evolving algorithms that might diagnose a cancer, drive a car, or approve a loan.

Singapore is well known for its tough laws (and penalties for flouting them). Now it has a new ally in the fight against chewing gum, littering and bigger misdemeanors. The country has started testing a robot named Xavier. Over the next three weeks, Xavier robots will monitor the crowds of Singapore's Toa Payoh Central to look for what the nation's authorities describe as "undesirable social behaviors" -- including any group of people. The country's current COVID-19 safety measure forbids congregations of more than five people. To gauge the crowds, Xavier models have cameras that create 360-degree views.

Artificial Intelligence technology can bridge the gaps in security technology, help build next-generation security teams and introduce security culture into enterprise

One data vendor in this business is X-Mode, which collects data from millions of users across hundreds of apps. The company was kicked off the Apple and Google platforms last year over its national security work with the US government, according to an account in The Wall Street Journal. However, the company is being acquired by Digital Envoy, Inc. of Atlanta, and will be rebranded as Outlogic. It's chief executive, Joshua Anton, will join Digital Envoy as chief strategy officer. The purchase price was not disclosed.

Artificial intelligence (AI)¹ has been rapidly growing worldwide, with new applications being discovered every day. While AI has applications across many sectors, one area where it is commonly utilized is in AI surveillance and facial recognition technology to combat crimes. As of 2019, at least seventy-five countries globally are actively using AI technologies for surveillance purposes, including smart city/safe city platforms, facial recognition systems, and smart policing initiatives (Feldstein 2019: 1). However, the widespread use of AI in the name of combating crimes does not come without a cost; multiple ethical concerns have arisen in the past couple of years, which questions the feasibility of implementing AI technology to combat crimes. This article will examine two prominent ethical concerns regarding AI in fighting crimes: biases in facial recognition technology and authoritarian governments exploiting AI surveillance in the name of public safety.

The US District Court for the Eastern District of Virginia on Wednesday ruled that an artificial intelligence (AI) machine cannot be an inventor under the Patent Act. The action was a motion for summary judgement concerning two patent applications filed by Stephen Thaler for an AI machine called DABUS. DABUS was listed as the inventor for Neural Flame--a light beacon that flashes in a new and inventive manner to attract attention--and Fractal Container--a beverage container based on fractal geometry. Thaler's patent applications were rejected by the US Patent and Trademarks Office (USPTO) and he challenged this refusal as "arbitrary, capricious, an abuse of direction and not in accordance with the law". He filed this action seeking a declaration that a patent application should not be rejected only on grounds that there is no natural person identified as the inventor and that a patent application for an invention by AI should list the AI as the inventor when the criteria for inventorship has been fulfilled by the AI. The court rejected Thaler's contentions, holding that the definitions provided by Congress for "inventor" within the Patent Act reference an "individual" whose ordinary dictionary and statutory meaning is a natural person or a human being.

Given the success of reinforcement learning (RL) in various domains, it is promising to explore the application of its methods to the development of intelligent and autonomous cyber agents. Enabling this development requires a representative RL training environment. To that end, this work presents CyGIL: an experimental testbed of an emulated RL training environment for network cyber operations. CyGIL uses a stateless environment architecture and incorporates the MITRE ATT&CK framework to establish a high fidelity training environment, while presenting a sufficiently abstracted interface to enable RL training. Its comprehensive action space and flexible game design allow the agent training to focus on particular advanced persistent threat (APT) profiles, and to incorporate a broad range of potential threats and vulnerabilities. By striking a balance between fidelity and simplicity, it aims to leverage state of the art RL algorithms for application to real-world cyber defence.

In this commentary, we respond to a recent editorial letter by Professor Luciano Floridi entitled 'AI as a public service: Learning from Amsterdam and Helsinki'. Here, Floridi considers the positive impact of these municipal AI registers, which collect a limited number of algorithmic systems used by the city of Amsterdam and Helsinki. There are a number of assumptions about AI registers as a governance model for automated systems that we seek to question. Starting with recent attempts to normalize AI by decontextualizing and depoliticizing it, which is a fraught political project that encourages what we call 'ethics theater' given the proven dangers of using these systems in the context of the digital welfare state. We agree with Floridi that much can be learned from these registers about the role of AI systems in municipal city management. Yet, the lessons we draw, on the basis of our extensive ethnographic engagement with digital well-fare states are distinctly less optimistic.