Artificial Intelligence Training in Tambaram

How artificial intelligence is transforming the world

Most people are not very familiar with the concept of artificial intelligence (AI). As an illustration, when 1,500 senior business leaders in the United States in 2017 were asked about AI, only 17 percent said they were familiar with it. A number of them were not sure what it was or how it would affect their particular companies. They understood there was considerable potential for altering business processes, but were not clear how AI could be deployed within their own organizations.

we discuss novel applications in finance, national security, health care, criminal justice, transportation, and smart cities, and address issues such as data access problems, algorithmic bias, AI ethics and transparency, and legal liability for AI decisions. We contrast the regulatory approaches of the U.S. and European Union, and close by making a number of recommendations for getting the most out of AI while still protecting important human values.

In order to maximize AI benefits, we recommend nine steps for going forward:

• Encourage greater data access for researchers without compromising users’ personal privacy,
• invest more government funding in unclassified AI research,
• promote new models of digital education and AI workforce development so employees have the skills needed in the 21st-century economy,
• create a federal AI advisory committee to make policy recommendations,
• engage with state and local officials so they enact effective policies,
• regulate broad AI principles rather than specific algorithms,
• take bias complaints seriously so AI does not replicate historic injustice, unfairness, or discrimination in data or algorithms,
• maintain mechanisms for human oversight and control, and
• penalize malicious AI behavior and promote cybersecurity.

QUALITIES OF ARTIFICIAL INTELLIGENCE

• Although there is no uniformly agreed upon definition, AI generally is thought to refer to “machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment and intention.

Intentionality

• Artificial intelligence algorithms are designed to make decisions, often using real-time data.
• They are unlike passive machines that are capable only of mechanical or predetermined responses. Using sensors, digital data, or remote inputs, they combine information from a variety of different sources, analyze the material instantly, and act on the insights derived from those data.
• With massive improvements in storage systems, processing speeds, and analytic techniques, they are capable of tremendous sophistication in analysis and decision making.

Artificial intelligence is already altering the world and raising important questions for society, the economy, and governance.

Intelligence

• AI generally is undertaken in conjunction with machine learning and data analytics. Machine learning takes data and looks for underlying trends. If it spots something that is relevant for a practical problem, software designers can take that knowledge and use it to analyze specific issues.
• All that is required are data that are sufficiently robust that algorithms can discern useful patterns. Data can come in the form of digital information, satellite imagery, visual information, text, or unstructured data.

Adaptability

• AI systems have the ability to learn and adapt as they make decisions. In the transportation area, for example, semi-autonomous vehicles have tools that let drivers and vehicles know about upcoming congestion, potholes, highway construction, or other possible traffic impediments.
• Vehicles can take advantage of the experience of other vehicles on the road, without human involvement, and the entire corpus of their achieved “experience” is immediately and fully transferable to other similarly configured vehicles.
• Their advanced algorithms, sensors, and cameras incorporate experience in current operations, and use dashboards and visual displays to present information in real time so human drivers are able to make sense of ongoing traffic and vehicular conditions. And in the case of fully autonomous vehicles, advanced systems can completely control the car or truck, and make all the navigational decisions.

APPLICATIONS IN DIVERSE SECTORS

• AI is not a futuristic vision, but rather something that is here today and being integrated with and deployed into a variety of sectors. This includes fields such as finance, national security, health care, criminal justice, transportation, and smart cities.
• There are numerous examples where AI already is making an impact on the world and augmenting human capabilities in significant ways.
• One of the reasons for the growing role of AI is the tremendous opportunities for economic development that it presents. A project undertaken by Price Waterhouse Coopers estimated that “artificial intelligence technologies could increase global GDP by $15.7 trillion, a full 14%, by 2030.
• That includes advances of $7 trillion in China, $3.7 trillion in North America, $1.8 trillion in Northern Europe, $1.2 trillion for Africa and Oceania, $0.9 trillion in the rest of Asia outside of China, $0.7 trillion in Southern Europe, and $0.5 trillion in Latin America. China is making rapid strides because it has set a national goal of investing $150 billion in AI and becoming the global leader in this area by 2030.

Finance

• Investments in financial AI in the United States tripled between 2013 and 2014 to a total of $12.2 billion. According to observers in that sector, “Decisions about loans are now being made by software that can take into account a variety of finely parsed data about a borrower, rather than just a credit score and a background check.” In addition, there are so-called robo-advisers that “create personalized investment portfolios, obviating the need for stockbrokers and financial advisers.” These advances are designed to take the emotion out of investing and undertake decisions based on analytical considerations, and make these choices in a matter of minutes.
• A prominent example of this is taking place in stock exchanges, where high-frequency trading by machines has replaced much of human decision making. People submit buy and sell orders, and computers match them in the blink of an eye without human intervention. Machines can spot trading inefficiencies or market differentials on a very small scale and execute trades that make money according to investor instructions.
• Powered in some places by advanced computing, these tools have much greater capacities for storing information because of their emphasis not on a zero or a one, but on “quantum bits” that can store multiple values in each location. That dramatically increases storage capacity and decreases processing times.
• Fraud detection represents another way AI is helpful in financial systems. It sometimes is difficult to discern fraudulent activities in large organizations, but AI can identify abnormalities, outliers, or deviant cases requiring additional investigation. That helps managers find problems early in the cycle, before they reach dangerous levels.

National security

• AI plays a substantial role in national defence. Through its Project Maven, the American military is deploying AI “to sift through the massive troves of data and video captured by surveillance and then alert human analysts of patterns or when there is abnormal or suspicious activity.”
• According to Deputy Secretary of Défence Patrick Shanahan, the goal of emerging technologies in this area is “to meet our warfighters’ needs and to increase [the] speed and agility [of] technology development and procurement.”

The increasing penetration of AI into many aspects of life is altering decision making within organizations and improving efficiency. At the same time, though, these developments raise important policy, regulatory, and ethical issues.

Data access problems

• The key to getting the most out of AI is having a “data-friendly ecosystem with unified standards and cross-platform sharing.”
• AI depends on data that can be analyzed in real time and brought to bear on concrete problems. Having data that are “accessible for exploration” in the research community is a prerequisite for successful AI development.

Biases in data and algorithms

• In some instances, certain AI systems are thought to have enabled discriminatory or biased practices. For example, Airbnb has been accused of having homeowners on its platform who discriminate against racial minorities.
• A research project undertaken by the Harvard Business School found that “Airbnb users with distinctly African American names were roughly 16 percent less likely to be accepted as guests than those with distinctly white names.”Racial issues also come up with facial recognition software.
• Most such systems operate by comparing a person’s face to a range of faces in a large database. As pointed out by Joy Buolamwini of the Algorithmic Justice League, “If your facial recognition data contains mostly Caucasian faces, that’s what your program will learn to recognize.” Unless the databases have access to diverse data, these programs perform poorly when attempting to recognize African-American or Asian-American features.
• Many historical data sets reflect traditional values, which may or may not represent the preferences wanted in a current system. As Buolamwini notes, such an approach risks repeating inequities of the past:
• The rise of automation and the increased reliance on algorithms for high-stakes decisions such as whether someone get insurance or not, your likelihood to default on a loan or somebody’s risk of recidivism means this is something that needs to be addressed. Even admissions decisions are increasingly automated—what school our children go to and what opportunities they have. We don’t have to bring the structural inequalities of the past into the future we create.

AI ethics and transparency

• Algorithms embed ethical considerations and value choices into program decisions. As such, these systems raise questions concerning the criteria used in automated decision making. Some people want to have a better understanding of how algorithms function and what choices are being made.
• In the United States, many urban schools use algorithms for enrolment decisions based on a variety of considerations, such as parent preferences, neighbourhood qualities, income level, and demographic background. According to Brookings researcher Jon Valant, the New Orleans–based Bricolage Academy “gives priority to economically disadvantaged applicants for up to 33 percent of available seats.
• In practice, though, most cities have opted for categories that prioritize siblings of current students, children of school employees, and families that live in school’s broad geographic area.” Enrolment choices can be expected to be very different when considerations of this sort come into play.
• Depending on how AI systems are set up, they can facilitate the redlining of mortgage applications, help people discriminate against individuals they don’t like, or help screen or build rosters of individuals based on unfair criteria. The types of considerations that go into programming decisions matter a lot in terms of how the systems operate and how they affect customers.

Legal liability

• There are questions concerning the legal liability of AI systems. If there are harms or infractions (or fatalities in the case of driverless cars), the operators of the algorithm likely will fall under product liability rules. A body of case law has shown that the situation’s facts and circumstances determine liability and influence the kind of penalties that are imposed. Those can range from civil fines to imprisonment for major harms.
• The Uber-related fatality in Arizona will be an important test case for legal liability. The state actively recruited Uber to test its autonomous vehicles and gave the company considerable latitude in terms of road testing. It remains to be seen if there will be lawsuits in this case and who is sued: the human backup driver, the state of Arizona, the Phoenix suburb where the accident took place, Uber, software developers, or the auto manufacturer. Given the multiple people and organizations involved in the road testing, there are many legal questions to be resolved.
• In non-transportation areas, digital platforms often have limited liability for what happens on their sites. For example, in the case of Airbnb, the firm “requires that people agree to waive their right to sue, or to join in any class-action lawsuit or class-action arbitration, to use the service.” By demanding that its users sacrifice basic rights, the company limits consumer protections and therefore curtails the ability of people to fight discrimination arising from unfair algorithms. But whether the principle of neutral networks holds up in many sectors is yet to be determined on a widespread basis.

RECOMMENDATIONS

• In order to balance innovation with basic human values, we propose a number of recommendations for moving forward with AI. This includes improving data access, increasing government investment in AI, promoting AI workforce development, creating a federal advisory committee, engaging with state and local officials to ensure they enact effective policies, regulating broad objectives as opposed to specific algorithms, taking bias seriously as an AI issue, maintaining mechanisms for human control and oversight, and penalizing malicious behavior and promoting cybersecurity.

Improving data access

• AI requires data to test and improve its learning capacity. Without structured and unstructured data sets, it will be nearly impossible to gain the full benefits of artificial intelligence.
• In general, the research community needs better access to government and business data, although with appropriate safeguards to make sure researchers do not misuse data in the way Cambridge Analytica did with Facebook information. There is a variety of ways researchers could gain data access.