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Artificial Intelligence

Human intelligence is about ability to adapt to physical and social world, and playing Go is a particular adaptation performed by human intelligence, and developing algorithm to learn to play Go is more performant adaptation, and developing mathematical theory to play Go might be even more performant.

It makes more sense to compare a human and AI not by effectiveness/efficiency of end product of adaptation (in games played between human and agent) but by effectiveness/efficiency of process of adaptation (in games played between human-coded agent and machine-learned agent after limited practice).

Winter Academy on Artificial Intelligence and International Law, 11-15 February 2019

Over the course of one week, this innovative training programme provides insights into the current and future issues raised by artificial intelligence from the perspective of international law.

It has the potential of providing for cognitive abilities going beyond human capacities, which could lead to significant scientific and societal progresses.

For instance, complex algorithms that compile and analyse large data sets could contribute to more accurate and precise policy-making.

At the same time, the use of technologies that display increasing degrees of autonomy brings in significant ethical, legal, and policy challenges.

Programme The Winter Academy offers foundational knowledge on key issues at the interface of international law and artificial intelligence, and provides a platform for critical debate and engagement on emerging questions.

AI-fueled organizations Reaching AI’s full potential in the enterprise

For some organizations, harnessing artificial intelligence’s full potential begins tentatively with explorations of select enterprise opportunities and a few potential use cases.

Davenport describes three stages in the journey that companies can take toward achieving full utilization of artificial intelligence.1 In the first stage, which Davenport calls assisted intelligence, companies harness large-scale data programs, the power of the cloud, and science-based approaches to make data-driven business decisions.

Today, companies at the vanguard of the AI revolution are already working toward the next stage—augmented intelligence—in which machine learning (ML) capabilities layered on top of existing information management systems work to augment human analytical competencies.

According to Davenport, in the coming years, more companies will progress toward autonomous intelligence, the third AI utilization stage, in which processes are digitized and automated to a degree whereby machines, bots, and systems can directly act upon intelligence derived from them.

The journey from the assisted to augmented intelligence stages, and then on to fully autonomous intelligence, is part of a growing trend in which companies transform themselves into “AI-fueled organizations.” This trend is also about a sustained commitment to redesigning core systems, processes, and business strategies around AI and its possibilities.

The AI-fueled organization trend, as we recognize it today, found its footing during the last several years when a few pioneering companies began experimenting with bots and other cognitive technologies to better understand their potential impact on productivity.2 We now see companies representing all industries and regions embarking on their own AI-fueled journeys.

Notably, of the surveyed executives from pioneering companies, 90 percent report already having AI strategies in place.5 The number of companies following in the footsteps of AI pioneers will likely increase in the next 18 to 24 months as companies identify ways to use cognitive technologies to achieve strategic goals.

Cognitive technologies/AI has consistently topped the list.6 Though these CIOs—much like society at large—may be fascinated by cognitive technologies’ sci-fi-like possibilities, their AI ambitions are likely grounded in more practical (and achievable) benefits: Pursued strategically across cognitive’s three stages, AI can increase productivity, strengthen regulatory compliance through automation, and help organizations derive meaning from ever-larger data sets.7 Enterprise tech leaders, start your engines.

Today, the possibility of achieving the next quantum leap in productivity propels our march toward autonomous intelligence.9 The human brain can decipher and derive meaning from large volumes of data, but this unique ability is limited by the amount of data our brains can absorb at any moment.

While this may sound like a straightforward proposition, its disruptive ramifications will likely ripple across the enterprise, with particular impacts in the following areas: Ultimately, the AI-fueled journey presents CIOs with an opportunity to redefine their own role, from chief information officer to “chief insight officer”—the organizational leader who serves as custodian, facilitator, and catalyst for informed decision-making at the corporate level.25 The speed with which pharmaceutical company Pfizer has deployed artificial intelligence to accelerate innovation across its organization demonstrates best practices that could serve as a model for the entire industry.

Without it, it will take too long to have the kind of impact we need.”26 Technology and business leaders within the organization quickly recognized AI’s potential to deepen Pfizer’s understanding of patients and their diseases, as well as accelerate the process of drug discovery and delivery to the market.

Understanding what’s possible with AI—and conversely, what the technology can’t solve—has played a vital role in helping Pfizer’s business units formulate their end goals, focused on driving speed, quality, and efficiency in areas as diverse as research and development, patient safety, medical, finance, and the global supply chain.

Pfizer’s IT team—which placed a premium on agility and open-source technology—believed that the company would be served best by building an in-house AI workforce that could employ a wide range of tools, from natural language processing to neural networks to statistical models and more.

As one business unit innovates and begins to accelerate—such as the medical group did with natural language processing—other teams piggyback on that success, taking the lessons learned and applying that knowledge to their own business unit.”27 To date, Pfizer’s AI-fueled approach has enabled 30,000 colleague hours, a number that is continuously growing as more AI-fueled automation is deployed each month.

Alex Benay, CIO of Canada, sees a convincing go-to-market imperative for governments to leverage artificial intelligence.28 The need to keep up with industries such as banking and telecommunications—those with which the government does business and regulates—and the vast opportunities that AI affords its agencies are helping to drive Canada’s push to operationalize the technology.

Nevertheless, we collectively have to close the digital gap between government and the industries we’re charged with overseeing by using the same technology they are.” Setting standards and guiding principles for the government’s AI deployment—and those organizations doing business with the government—is at the heart of Canada’s digital strategy.

Canada’s leaders, notably Scott Brison, the first-ever minister of digital government, hope to set an example and are working one-on-one with other countries and as part of the Digital 9 group of nations.29 Benay shared the government’s draft directive at the November 2018 D9 meeting to elicit both feedback and support from the member nations.

Benay (also co-founder of the nonprofit CIO Strategy Council, a Canadian collaborative that discusses digital transformation issues and looks to help set industry standards30) stresses that the country’s AI directive is only the first iteration: It will be reviewed quarterly or semiannually and adjusted as the technology and ethical environment evolves.

“But that’s a good thing on the ethical front: It’s important that the government takes the time to do AI properly and—more importantly—to respect Canada’s values.” Many have speculated about the potential for artificial intelligence to replace much of the human workforce,31 but the Adecco Group foresees a future in which augmented intelligence will enhance rather than replace human skills such as critical thinking, emotional intelligence, and value judgments.

“We don’t see ourselves as a tech company,” says the Adecco Group CEO, Alain Dehaze.32 “But we see an opportunity to leverage technology to complement, advance, and even disrupt our existing business to stay attuned to today’s workforce.” Currently, its general staffing brand, Adecco, uses Mya Systems' chatbot to streamline its initial screening of candidates.

The Adecco Group’s initial foray into AI prompted the company to explore other opportunities to scale the technology for greater efficiencies within other core processes across the enterprise, such as using robotic process automation to manage the time registration and payroll administration of 700,000 temporary workers daily, as well as applying analytics to prioritize work for company recruiters.

it created YOSS (your own boss), an end-to-end digital marketplace that leverages AI to match supply with demand.33 The platform helps build trust between freelancers and hiring companies and can match freelancers with benefits and training as well as handle payment arrangements.

We want to grow these capabilities geographically as well as expand into additional vertical markets.” While many companies are just beginning to explore the potential benefits of artificial intelligence, Google recognized the value the technology can bring to its business model from the beginning.34 Over the course of the last five years, the organization has gone from deploying AI in narrow strategic areas to becoming “AI-first” and mandating its use across the Google enterprise.

“We believe every company is going to be transforming itself with AI over the course of the next 10 years, so we felt it was imperative that we deploy it broadly as part of our own business strategy.” One of the keys to successfully applying artificial intelligence, Sheth says, is identifying an internal business challenge and then exploring how AI might solve it.

Another use case for AI allowed Google Cloud to improve the energy efficiency in its data centers: Using machine learning to set cooling system algorithms and reinforcement learning—where the system tries different things to test an outcome and then retrains itself based on findings—the system learned, on its own, what the optimal settings were.The cooling energy needed was reduced by 40 percent, resulting in a 15 percent reduction in overall data-center energy usage and producing significant cost savings for the organization.

Cybersecurity professionals are becoming exceedingly aware of the threat of hackers using artificial intelligence to gain access to customer and organizational data.35 However, as we navigate the risk, security, and privacy implications of AI technology, it is important to understand that AI also can be an effective tool to fight cybercrime, fraud, and threats.

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