Members of the public sector, private sector, and academia convened for the second AI Policy Forum Symposium last month to explore critical directions and questions posed by artificial intelligence in our economies and societies. The virtual event, hosted by the AI Policy Forum (AIPF) -- an undertaking by the MIT Schwarzman College of Computing to bridge high-level principles of AI policy with the practices and trade-offs of governing -- brought together an array of distinguished panelists to delve into four cross-cutting topics: law, auditing, health care, and mobility. In the last year there have been substantial changes in the regulatory and policy landscape around AI in several countries -- most notably in Europe with the development of the European Union Artificial Intelligence Act, the first attempt by a major regulator to propose a law on artificial intelligence. In the United States, the National AI Initiative Act of 2020, which became law in January 2021, is providing a coordinated program across federal government to accelerate AI research and application for economic prosperity and security gains. Finally, China recently advanced several new regulations of its own. Each of these developments represents a different approach to legislating AI, but what makes a good AI law?

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By defining the current limits (and thereby the frontiers), many boundaries are shaping, and will continue to shape, the future of Artificial Intelligence (AI). We must push on these boundaries to make further progress into what were yesterday's frontiers. They are both pliable and resilient--always creating new boundaries of what AI can (or should) achieve. Among these are technical boundaries (such as processing capacity), psychological boundaries (such as human trust in AI systems), ethical boundaries (such as with AI weapons), and conceptual boundaries (such as the AI people can imagine). It is within these boundaries that we find the construct of needs and the limitations that our current concept of need places on the future AI.

The Ukrainian army's astute use of drones has been a cornerstone of its defence against the powerful Russian invader, but experts say their role is beginning to fade as heavy artillery takes over. In the early phase of the war, Ukraine's sky seemed filled with the remote-controlled aircraft deployed by President Volodymyr Zelensky's army to spy on the enemy, or go on the attack. During Moscow's early advance on Kyiv "it would have been extremely challenging for Ukraine to block (Russian President Vladimir) Putin's army without drones", said Paul Lushenko, a US Army Lieutenant Colonel and PhD student at Cornell University. "They could compound or exacerbate Putin's strategic and logistical challenges," he told AFP. The Turkish-made Bayraktar drone, known as TB-2, already famous worldwide, added to its stellar reputation during the defence of Ukraine's capital. On top of providing intelligence on Russian movements, drones also helped Ukraine offset much of its air force's weakness compared to that of Russia.

Equity is not the same principle as equality. Within the social context they both relate to fairness; equality means treating everyone the same regardless of need, while equity means treating people differently depending on their needs. Consider vaccinations, if we based public health policy on equality, perhaps there would be a lottery system to decide who gets vaccinated first, giving everyone an equal chance. In practice, however, vaccinations are prioritized based on equity, those with the greatest risk, frontline healthcare workers and the elderly, understandably, are first in line. Assuming we understand the causal relationship between treatment and outcome, the question then is, how do we identify the subgroups who experience the greatest average causal effects, whether positive or negative.

Douglas Hofstadter, a cognitive scientist, recently wrote in the Economist that he believes that GPT-3 is "cluelessly clueless." By this he means that GPT-3 has no idea about what it is saying. To illustrate, he and a colleague asked it a few questions. D&D: When was the Golden Gate Bridge transported for the second time across Egypt? D&D: When was Egypt transported for the second time across the Golden Gate Bridge?

Machine learning can enable positive change in society, says Adam Benzion, Chief Experience Officer at Edge Impulse. Read on to learn how the company is preventing unethical uses of its ML/AI development platform. Priscilla Haring-Kuipers: What Ethics in Electronics are you are working on? Adam Benzion: At Edge Impulse, we try to connect our work to doing good in the world as a core value to our culture and operating philosophy. Our founders, Zach Shelby and Jan Jongboom define this as "Machine learning can enable positive change in society, and we are dedicated to support applications for good."

South Africa and the rest of the world are currently experiencing a shift in how the world operates as technology makes certain tasks quicker, simpler and convenient for consumers. Most business leaders today are looking into adopting Artificial Intelligence (AI) to boost their company's future productivity, increase communication with customers, and enhance their customer's experience. Infobip's Regional Sales Engineering Manager for Africa, Orediretse Moleboloa claims that South African businesses are adopting artificial intelligence. "Especially companies that have no legacy of older technology. The digital native organisations or businesses, companies don't have an issue with older digital architecture. They just look at the latest and greatest on what's available. And chatbots and automated contact center solutions are the in thing at the moment", says Moleboloa.

Please note this role is eligible for remote working within Hungary. Black Swan Data is a fast-growing technology and data science business, with offices in the UK, South Africa, Hungary. We build high quality SaaS solutions which automate data science using advanced machine learning and deep learning techniques. We use some of the coolest technology on the planet so you will never get bored of doing the same thing. You'll be part of a dynamic and growing global team As we continue to grow across the world, you'll find every day brings with it fresh challenges and opportunities to try new things.

Deep learning[133] uses several layers of neurons between the network's inputs and outputs. The multiple layers can progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces.[134] Deep learning has drastically improved the performance of programs in many important subfields of artificial intelligence, including computer vision, speech recognition, image classification[135] and others. Deep learning often uses convolutional neural networks for many or all of its layers.