AI News, Front of mind: AI in manufacturing artificial intelligence


The Legal 500 recognizes Mr. Pastore for both his intellectual property and cybersecurity and data privacy work, describing him as a “skilled litigator” who is “highly accomplished.” Chambers USA 2018 recognizes Mr. Pastore as a leading lawyer for Privacy and Data Security, where sources explain “he knows the technical side of matters and is great at interfacing when there is federal involvement."

Named as a Cybersecurity Trailblazer by The National Law Journal, Mr. Pastore has also twice been named to Cybersecurity Docket’s “Incident Response 30,” a collection of 30 of the “best and brightest” incident response attorneys in the country.

Mr. Pastore has assisted a broad range of clients in cybersecurity and data privacy matters, including The Home Depot (in connection with its 2014 data breach);

Mr. Pastore also led Operation Dirty R.A.T., which targeted the creators and users of Blackshades ransom and malware, resulting in the largest ever worldwide law enforcement action against cybercriminals.

Prior to 2009, Mr. Pastore was an associate at Debevoise, working on a variety of high-profile intellectual property matters, including the well-publicized Google books copyright litigation.

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Widely considered as one of the three pioneers of deep learning, as well as the most impactful recent advances in machine learning, Dr. Bengio is a world-renowned researcher with more than 300 publications and over 145,000 citations to his name.

Front of mind: AI in manufacturing - Control Engineering

Artificial intelligence (AI), long a hot topic for information technology departments (IT), is increasingly finding its way into boardroom discussions as they look to implement smart systems in manufacturing are becoming more frequent.

In conjunction with IoTW promotor we.Conect Global Leaders GmbH, HPE polled more than 850 executives, directors, and line managers on AI’s impact on their companies, business processes, and bottom lines.

These include double-digit growth in both profits (10.4%) and margin (11%), even as a corresponding number (11%) of the largely European survey population had successfully executed AI implementations.

said Dale Rickert, the global director for the IOTW conference series, noting 95% of respondents that had completed projects had achieved the targets set for them in the planning stages.

The locations of those projects—ranging from data centers to the production line—attests to AI’s scope for cutting costs and increasing efficiency in core and peripheral operations.

Survey respondents were roughly split on deployments at the edge—where data is collected and instructions executed—and in the datacenter, where the computational and analytical capabilities that underpin machine learning are located.

A 15% increase is forecast for data center deployments in the same timeframe, meaning hybrid strategies to AI will remain the norm as AI projects grow in number.

Legacy IT systems are necessitating this bifurcated approach with almost half of respondents reporting the quantity and quality of the data needed to run AI models as an impediment to implementation.

Contrary to popular fears about the potential job losses in the rise of the machine, nearly two-thirds of respondents said they expect the technology to create more positions than it eliminates.

Because these jobs will revolve less around production technologies and more around data science means demand is high for those with the sets of skills necessary to implement and maintain AI projects.

Artificial Intelligence (AI) in Canada

Long before the first computers were built, the idea of machines capable of acting like human beings fascinated thinkers around the world.

More broadly, animism, or the belief that objects and non-human beings could possess an animating spirit allowing them to engage with the minds of humans, has been a strong component of many world religions, including North American Indigenous spiritual practices.

With access to precision metalworking technologies, many societies around the world began to develop automata, or mechanical devices able to partially mimic human behaviour.

The European Renaissance saw Italian inventor and humanist Leonardo da Vinci (1452–1519) design a fully armoured mechanical knight, and mechanical figures were installed as part of church clocks in German cities such as Nuremberg.

Famous examples of these include a doll-size mechanical boy able to write in ink, created by Swiss clockmaker Pierre Jaquet-Droz, and Frenchman Jacques de Vaucanson’s “digesting duck,” a mechanical bird that impressed audiences by ingesting real food — and seemingly expelling it afterward.

While mechanical automata could repeat particular human behaviours, their “intelligence” — their ability to accomplish complex, adaptive and socially contextual goals — proved simplistic and one-dimensional.

However, with the development of electronic digital computers in the 1930s and ‘40s, many scientists became convinced that computing machines could be made to exhibit intelligent behaviour by replicating the functioning of the human brain.

As soon as electronic computers became more widely accessible to academic, government and military researchers at the end of the Second World War, computing researchers began to program them to do things that had been considered reserved for the human mind.

In 1950, Turing developed what he called the “imitation game,” which is now better known as the “Turing Test.” Turing first proposed a scenario where a human interrogator sitting in one room had to guess whether two other individuals sitting in another room were a man or a woman solely based on their written responses;

A number of academics working at US institutions — including Harvard’s Marvin Minsky, the Carnegie Institute of Technology’s Allen Newell and Herbert Simon, Claude Shannon of Bell Labs and Dartmouth’s own John McCarthy — gathered there for two months to “find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.” According to McCarthy, early AI research focused on how to “consider the computer as a tool for solving certain classes of problems.” AI was therefore “created as a branch of computer science and not as a branch of psychology,” a decision that profoundly shaped the history of the field by formally separating it from the social sciences.

Some of these tools, which we often take for granted today, include list-processing programming languages like LISP (invented by McCarthy from 1956 to 1958 and still in use) and time sharing (enabling multiple individuals to make use of one computer’s processing capabilities at the same time).

Even though the quality of translation was considered high, government funding for the latter project was abandoned in 1981 because the cost of editing the system’s translations and updating its dictionary made it uneconomical to use.

Such “expert systems” supported certain narrow areas of expertise, including specialized medical diagnosis, chemical analysis, circuit design and mineralprospecting.

Facilitating collaboration between industry, government and university researchers, it worked to develop new commercial applications of AI and helped Canadian companies compete in global markets.

By the late 1980s, desktop computers utilizing fourth-generation microprocessor chips from firms like Intel had become more computationally powerful and had found a wider range of uses in everyday life.

Examples of such tasks include retrieving all digital images of a cat within a set, or successfully identifying and interpreting words in a particular human language (known as Natural Language Processing, or NLP).

In 1992, a major machine translation conference in Montreal saw a spirited debate between two strands of machine translation research: “rationalists,” who used linguistic theories to structure computer translation, and “empiricists,” who used large-scale statistical processing of enormous data sets.

The program (now known as Learning in Machines and Brains) sought to revive interest in neural networks by bringing together experts from computer science, biology, neuroscience and psychology.

It also aims to support a national research community focused on AI, increase the number of AI researchers in Canada, and “develop global thought leadership on the economic, ethical, policy and legal implications of advances in artificial intelligence.” Ahead of the 2018 G7 Summit in Charlevoix, Quebec, Trudeau and French president Emmanuel Macron committed to create an international study group on inclusive and ethical AI.

But applied AI technologies like machine learning and intelligent agents (e.g., chatbots on customer service web pages or virtual agents like Apple’s Siri on smartphones) are already part of Canadian society.

These technologies have a variety of potential positive uses in commercial and government contexts: a 2018 report by the Treasury Board of Canada Secretariat notes the potential for AI technologies to assist Canadians and businesses with routine transactions via virtual service agents, to monitor industries for early warning signs of regulatory non-compliance, and to shape public policy by facilitating new insights into government data.

Techniques such as machine learning are based on large amounts of digital data about the world, and it is difficult to avoid transferring and amplifying the racism, sexism and other human prejudices expressed in that data into AI systems.

By the same token, AI technologies reliant on large amounts of digital data raise considerable privacy concerns, potentially allowing large institutions to make assumptions about personal details Canadians don’t want shared.

Information Society.) As more and more companies and governments have deployed AI technologies, concerned members of civil society groups, professional associations, and even technology firms themselves have increasingly worried about the ethics of using these technologies or even developing them at all.

International Journal of Artificial Intelligence in Education

Coverage extends to agent-based learning environments, architectures for AIED systems, bayesian and statistical methods, cognitive tools for learning, computer-assisted language learning, distributed learning environments, educational robotics, human factors and interface design, intelligent agents on the internet, natural language interfaces for instructional systems, real-world applications of AIED systems, tools for administration and curriculum integration, and more.

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