"Please think forward to the year 2030. Analysts expect that people will become even more dependent on networked artificial intelligence (AI) in complex digital systems. Some say we will continue on the historic arc of augmenting our lives with mostly positive results as we widely implement these networked tools. Some say our increasing dependence on these AI and related systems is likely to lead to widespread difficulties. Our question: By 2030, do you think it is most likely that advancing AI and related technology systems will enhance human capacities and empower them? That is, most of the time, will most people be better off than they are today? Or is it most likely that advancing AI and related technology systems will lessen human autonomy and agency to such an extent that most people will not be better off than the way things are today? Please explain why you chose the answer you did and sketch out a vision of how the human-machine/AI collaboration will function in 2030.

Looking for more information on bias and other pitfalls in AI? Check out the "Ethics, Privacy, and Security" sessions at the AI Conference in New York, April 15–18, 2019. In a recent paper, Hila Gonen and Yoav Goldberg argue that methods for de-biasing language models aren't effective; they make bias less apparent, but don't actually remove it. De-biasing might even make bias more dangerous by hiding it, rather than leaving it out in the open. The toughest problems are often the ones you only think you've solved. Language models are based on "word embeddings," which are essentially lists of word combinations derived from human language.

The Age of Intelligent Machines is upon us, and we are at a reflection point. The proliferation of fast-moving technologies, including forms of artificial intelligence, will cause us to confront profound questions about ourselves. The era of human intellectual superiority is ending, and, as a species, we need to plan for this monumental shift. A Human Algorithm: How Artificial Intelligence Is Redefining Who We Are examines the immense impact intelligent technology will have on humanity. These machines, while challenging our personal beliefs and our socioeconomic world order, also have the potential to transform our health and well-being, alleviate poverty and suffering, and reveal the mysteries of intelligence and consciousness.

Von Neumann Architecture Neuromorphic Architecture Neuromorphic architectures address challenges like high power consumption, low speed, and other efficiency-related bottlenecks prevalent in the traditional von Neumann architecture Architecture Bottleneck CPU Memory Neuromorphic architectures integrate processing and storage, getting rid of the bus bottleneck connecting the CPU and memory Encoding Scheme and Signals Unlike the von Neumann architecture with sudden highs and lows in the form of binary encoding, neuromorphic chips offer a continuous analog transition in the form of spiking signals Devices and Components CPU, memory, logic gates, etc. Artificial neurons and synapses Neuromorphic devices and components are more complex than logic gates Versus Versus Versus 10. NEUROMORPHIC CHIPSETS 10 SAMPLE REPORT Neuromorphic Chipsets vs. GPUs Parameters Neuromorphic Chips GPU Chips Basic Operation Based on the emulation of the biological nature of neurons onto a chip Use parallel processing to perform mathematical operations Parallelism Inherent parallelism enabled by neurons and synapses Require the development of architectures for parallel processing to handle multiple tasks simultaneously Data Processing High High Power Low Power-intensive Accuracy Low High Industry Adoption Still in the experimental stage More accessible Software New tools and methodologies need to be developed for programming neuromorphic hardware Easier to program than neuromorphic silicons Memory Integrated memory and neural processing Use of an external memory Limitations • Not suitable for precise calculations and programming- related challenges • Creation of neuromorphic devices is difficult due to the complexity of interconnections • Thread limited • Suboptimal for massively parallel structures Neuromorphic chipsets are at an early stage of development, and would take approximately 20 years to be at the same level as GPUs. The asynchronous operation of neuromorphic chips makes them more efficient than other processing units.

John McCarthy, the father of Artificial Intelligence, describes it as "the science and engineering of making intelligent machines, especially intelligent computer programs". Artificial Intelligence is a way of making a computer and software related to computer which can think intelligently and autonomously, kind of similar to a human mind. In general understanding artificial intelligence is accomplished by studying how a human brain works while solving a problem and in what manner it learns and makes decisions, where outcomes of such kind of study are used as the basis of developing intelligent software and systems. Till now this field was dominated by quasi-artificial intelligent systems called "expert systems," which mainly used a rules-based decision-making process.1 In other words, we can interpret that these systems were not fully autonomous and, therefore, not truly intelligent, because they lacked the ability to learn and produce unpredictable results, and mostly they acted in a manner predetermined by their programming.2

Intellectual Property (IP) defines intangible inventions that are a result of creativity, and increasingly is linked to new technology innovations. In a digital age, it's important to manage IP effectively and capitalize on the commercial value. The opportunity to commercialize IP in the Artificial Intelligence (AI) space has never been higher: in January, the UN's World Intellectual Property Organization (WIPO) highlighted the rapid increase in AI patent applications worldwide. More than half of the 340,000 inventions patented had taken place since 2013. Without a patent or legally enforceable IP protection, it is very difficult to commercialize an invention or idea - it undermines investment, research and development and growth opportunities.

As the'AI era' of increasingly complex, smart, autonomous, big-data-based tech comes upon us, the algorithms that fuel it are getting under more and more scrutiny. Whether you're a data scientist or not, it becomes obvious that the inner workings of machine learning, deep learning, and black-box neural networks are not exactly transparent. In the wake of high-profile news reports concerning user data breaches, leaks, violations, and biased algorithms, that is rapidly becoming one of the biggest -- if not the biggest -- sources of problems on the way to mass AI integration in both the public and private sectors. Here's where the push for better AI interpretability and explainability takes root. Already a focal point of machine learning consulting and a notable topic in the 2019 AI discussions, it's only likely to accelerate and become one of the central conversations of 2020 regarding the questions of both security and ethics of artificial intelligence. The days of the ideas like'machines will become too smart and independent and will rise against humanity' are long behind us, with the sentiment firmly relegated to the realm of science fiction and entertainment.

The lighter your skin, the better AI-powered facial recognition systems work for you. The UK Home Office knows this, because the government's been briefed several times on the problem. And a recent report shows that it knew it was developing a passport program built on biased, racist AI. The UK's passport program went live in 2016. It uses an AI-powered facial recognition feature to determine whether user-uploaded photos meet the requirements and standards for use as a passport photo.

A new Senate bill would require tech companies to label internet-connected devices equipped with either a camera or microphone. Introduced by Cory Gardner, a Republican senator from Colorado, the Protecting Privacy in our Homes Act is intended to enhance consumer privacy as more and more tech devices come equipped with surveillance tools that aren't always obvious. The Federal Trade Commission would be responsible for creating the specific language for the label and for determining and enforcing penalties for non-compliance. Amazon's Alexa (pictured above) comes with a microphone that records users even when they're not using the device. The bill would exclude devices marketed specifically as cameras or microphones.

For corporate finance advisers, AI presents many exciting opportunities to increase efficiency, reduce costs and create new services. But given corporate deal-making's complexity and economic importance, careful thought is needed about how best to combine human expertise with this transformative new technology. The Corporate Finance Faculty convened an expert group to investigate how the sector can ready itself for the Age of AI. The impact of artificial intelligence (AI) on finance and business will be profound. The Corporate Finance Faculty has been tracking the application of AI to'big data' in corporate transactions.