Category Archives: Artificial Intelligence

MEDFORD, Mass., Feb. 26, 2020 (GLOBE NEWSWIRE) -- Agero, a market leader in software-enabled driver assistance services for automotive manufacturers and insurance providers in North America, today announced that its advanced artificial intelligence (AI) services are now in use by eleven clients, including four of the top six insurance providers and one of North Americas largest automakers to improve driver assistance for millions of motorists in the U.S.

The award winningCommand Center, part of Ageros Managed Services, uses predictive analytics and machine learning developed from over 50 million events to combat unexpected delays in delivering roadside assistance caused by traffic, weather, high service volume and more. Command Center leverages real-time data to identify events that are trending outside expected service windows, and escalates those events for rapid recovery in order to dramatically improve the experience for drivers in their time of need. The result is up to a 30% reduction in customer complaints and the transformation of high-risk roadside events into positive, memorable experiences.

Breakdowns are a hugely stressful event for motorists, and everyone wants help to arrive as quickly as possible, notes Jeff Blecher, Ageros chief strategy officer. Predicting when and where potential problems will occur during delivery of that assistance and quickly recovering when they do is critical to ensuring the best customer experience for motorists on behalf of our insurance and auto clients.

Command Centers powerful ProactiveETA prediction model and proprietary machine learning algorithms have been trained through tens of millions of actual events to understand the impact of traffic, weather patterns, unexpected volume and other factors and compare that data with service provider and motorist location in real time to anticipate delays. The tool identifies and escalates at-risk cases early, before an arrival time is missed and often before the problem even becomes visible to motorists, across every zip code in the U.S. When problems do occur, highly trained case managers armed with Command Centers data proactively engage both service provider and motorist, doing what it takes to resolve issues and provide exceptional customer care.

Motorists are quick to recognize the high-touch service from Command Center, responding with effusive praise for the proactive contact and service escalation, and the repeated follow-up to ensure service and safety. Policyholder and vehicle owner compliments frequently include the sentiment that the insurer/OEM has earned a customer for life.

A days actual customer feedback includes:

I'm not from the area and my wife had a stroke so I was travelling to see her at the hospital. Debbie made the process so much easier and even found a place to get a new tire. She went above and beyond and must've called everyone in N.Y. to try and get me back on my way. Even when I called back in for an update I was transferred back to Debbie so I would not have to explain my situation again. motorist, December 21, 2019.

Ryan really helped me out today. I was stuck for hours and he spoke with me through the process, and went the extra mile to assist! I was afraid, and wasn't sure if I was going to get out, but Ryan kept checking if I was safe the entire time and made sure the help did arrive. motorist, December 21, 2019.

I have been a customer with [Insurance Company] for 40 years. Elaine is a good example of what customer service is all about. She is professional. She is polite. She is patient. She fixed my issue. motorist, December 21, 2019.

Consumer expectations for fast, seamless and transparent service have impacted every sector of the economy, including roadside assistance, driving ever greater need for responsive customer experiences. Agero covers 115 million motorists and two-thirds of all new cars in the U.S., putting the company in a unique position to combine the scale, data expertise and event volume needed to power an innovative AI-powered service like Command Center.

For more information on Command Center, please visit: https://info.agero.com/command_center. Additional details on Agero Roadside Managed Services can be obtained here; a sales representative can be contacted here.

About AgeroAgeros mission is to safeguard consumers on the road through a unique combination of platform intelligence and human powered solutions, strengthening our clients relationships with their drivers. We are a leading provider of driving solutions, including roadside assistance, accident management, consumer affairs and telematics. The company protects 115 million vehicles in partnership with leading automobile manufacturers, insurance carriers and other diversified clients. Managing one of the largest national networks of service providers, Agero responds to more than 12 million requests annually for assistance. Agero, a member company of The Cross Country Group, is headquartered in Medford, Mass., with operations throughout North America. To learn more, visit http://www.agero.comand follow on Twitter@AgeroNews.

Media Contact:

Kate PattyPR Manager, AgeroKPatty@agero.com781.306.3771

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Top US Insurers and Automakers using Artificial Intelligence-Based Services from Agero to Improve Driver Assistance - GlobeNewswire

BOSTON, Feb. 26, 2020 /PRNewswire/ --VODA.aiis excited to announce an Innospark Ventures led round of fundraising froma group of investors, including the Massachusetts Clean Energy Center (MassCEC).

The new investment round will enableVODA.ai to capitalize on its position as a leader in theapplication of Artificial Intelligence to serve the waterindustry globally. Smart water and city leaders are deploying VODA.ai's technology to proactively manage their aging water main pipes against water loss, disruptive breaks, and premature pipe replacements.

Product innovation is a key area of focus forVODA.ai.The funding will allow VODA.ai to focus on expanding product offerings to further increase customer benefits.

"Innospark Ventures and MassCEC are leaders in AI and the environment," VODA.ai CEO, George Demosthenous said. "We are all excited about our potential to positively impact customers and the communities they serve."

"We are thrilled to partner with VODA.ai as they embark on their journey to make water systems more efficient and more responsive to both consumers and the environment. George and the team are a shining example of what the Innospark team gets most excited about:AI for Good"said Dr. Venkat Srinivasan, Managing Director, Innospark Ventures.

"VODA.ai's technology is helping municipalities address some of their greatest energy, environmental, and operational challenges," said MassCEC CEO, Stephen Pike. "This fundraising fills a critical gap as the company grows its business."

About the Company

VODA.ai is an AI company founded in 2017 by experienced AI and water industry entrepreneurs. It helps utilities worldwide to make more informed decisions. It is headquartered in Boston, MA and operates in markets worldwide.

About Innospark

InnosparkVenturesis a new breed of early stage investor. They invest in bold founders leveraging AI to create a differential impact on society.

About the Massachusetts Clean Energy Center

The mission of MassCEC, a quasi-public entity, is to grow the state's clean energy economy and help meet the Commonwealth's energy, climate, and economic development goals.

James C. FitchettChief Operating Officer and Co-Founder978-502-1782234886@email4pr.com

voda.ai

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VODA.ai Raises A Second Round of Funding for Its Artificial Intelligence Based "Virtual Pipe Condition Assessment" Solution to Predict Water...

The medical technology conference med.con 2020, hosted by ENGEL at its Technologieforum in Stuttgart during mid February, was a huge success. The conference was a full house as more than 100 delegates attended it.

The main talk point at the conference was patient safety through advanced technology and was discussed from the various perspectives of plastics processing in the cleanroom and conveyed in a tangible way using live machine exhibits.

With artificial intelligence and big data being the focus, it was deliberated that the potential for more quality, safety, and cost-efficiency in medical technology is yet to be fully exploited.

Summing up the massive challenge that the volume of data generated is increasing, but the use of the data is not, Uwe Herbert, IT manager at Ypsomed, a manufacturer of injection systems for self-medication, in his keynote address mentions, We are passing up opportunities here

Uwe advocates that it is necessary to link the IT system of the individual department in the company and provide the employees with the freedom they need to experiment with the new possibilities to improve the quality of the products and reduce the unit costs. However, according to Uwe, the complexity of these projects is often underestimated.

Speaking of artificial intelligence, Christian Pommereau, principal engineer with pharmaceutical company Sanofi-Aventis Deutschland, emphasizes, We need to shift up a gear when it comes to artificial intelligence. To avoid the plastics processing industry losing touch, we need everyone around the table, adds Christian. He has witnessed within his own group of companies how far ahead the drug production industry is in this field.

Both the above-mentioned speakers sparked a lively discussion. It became clear that the industry has long recognized the great potential that Industry 4.0 has to offer. But obstacles often remain to adopt the new technologies for reflecting the specific requirements of cleanroom production. For instance, the validation of dynamic process control with the help of intelligent assistance, an important feature of the smart factory, has to be planned in detail and designed safely.

Christoph Lhota, vice president, ENGEL medical, reported on how ENGELs iQ weight control assistance system can be integrated into rules and regulations accepted by the auditors, based on ongoing development work.

The ENGEL developers have investigated various approaches to the validation process and ultimately derived a procedure that defines process windows for the parameters to be retroactively adjusted, enabling the validation of dynamically controlled processes in conformity with both EN ISO and the FDA.

In his keynote, Christoph gave an outlook on other topics that are gaining in importance in medical technology and on which ENGELs developers are working intensively. These comprise of injection molding of liquid silicone rubber in the cleanroom, efficient injection molding of very small batch sizes and sterile injection molding, like cleanroom class ISO 5 is increasingly required in plastics processing.

Talking about ISO 5, Christoph informs, It is a totally different planet. The opening speed of the injection molding machine is significant here. To specifically adapt its machines, robots and technologies to this new class of requirements, ENGEL operates its own clean room at its headquarters in Schwertberg.

Among the eight presentations during the keynote session, other speakers were Martin Maier from Waldorf Technik, Reinhard Steger from Braunform, Martin Jungbluth from Max Petek Reinraumtechnik, and Jrg Leonhartsberger and Claus Wilde from ENGEL.

During the conference, especially in the breaks, and following the talks, ENGEL opened up its technology center with live exhibits and a partner exhibition.

ENGELs high level of expertise in systems solutions was noticeable in the clean room injection molding applications. Sophisticated medical products were manufactured in highly-integrated and automated production cells throughout the event.

There were thick-walled housing parts which can be produced in an 8-cavity mold using servo-electric Vario-Spinstack technology from Hack Formenbau in particularly short cycle times and with a correspondingly low unit cost, thanks to the two-component process.

It also showcased needle holders for 1 ml safety syringes in a 16-cavity mold by Fostag Formenbau with a particularly low shot weight of 0.08 grams per part. The needle holders very thin and different wall thicknesses require extremely precise process control, which ENGEL ensures with iQ weight control.

The needle holders are taken off by a viper linear robot and transferred to the pipe distribution system, developed by ENGEL and made completely of stainless steel, in order to package the filigree mouded parts sorted by cavity.

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Artificial Intelligence and Big Data forms focus on ENGEL's medical technology conference - Plastics Insight

The time an airplane spends waiting for a gate after landing or waiting in line to take off could also be reduced. A group at SITA focused on airport management systems is helping to design technology that can synthesize data from many sources, including changing aircraft arrival times, weather conditions at destination airports and logistical issues to improve runway schedules and gate assignments.

Artificial intelligence software can also make a difference with rebooking algorithms, Mr. Etzioni said. When weather or mechanical issues disrupt travel, the airlines speed in recomputing, rerouting and rescheduling matters, he said.

The data streams get even more complex when the whole airport is considered, Ms. Stein of SITA said. A number of airports are creating a digital twin of their operations using central locations with banks of screens that show the systems, people and objects at the airport, including airplane locations and gate activity, line lengths at security checkpoints, and the heating, cooling and electrical systems monitored by employees who can send help when needed. These digital systems can also be used to help with emergency planning.

The same types of thermal, audio and visual sensors that can be used to supply data to digital twins are also being used to reduce equipment breakdowns. Karen Panetta, the dean of graduate engineering at Tufts University and a fellow at the Institute of Electrical and Electronics Engineers, said hand-held thermal imagers used before takeoff and after landing can alert maintenance crews if an area inside the airplanes engine or electrical system is hotter than normal, a sign something may be amiss. The alert would help the crew schedule maintenance right away, rather than be forced to take the aircraft out of service at an unexpected time and inconvenience passengers.

At the moment, people, rather than technology, evaluate most of the data collected, Dr. Panetta said. But eventually, with enough data accumulated and shared, more A.I. systems could be built and trained to analyze the data and recommend actions faster and more cost effectively, she said.

Air travel isnt the only segment of the transportation industry to begin using artificial intelligence and machine learning systems to reduce equipment failure. In the maritime industry, a Seattle company, ioCurrents, digitally monitors shipping vessel engines, generators, gauges, winches and a variety of other mechanical systems onboard. Their data is transmitted in real time to a cloud-based A.I. analytics platform, which flags potential mechanical issues for workers on the ship and on land.

A.I. systems like these and others will continue to grow in importance as passenger volume increases, Ms. Stein said. Airports can only scale so much, build so much and hire so many people.

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Air Travelers Cant See All of It, but More Tech Is Moving Them Along - The New York Times

The White Houses recent efforts to chart a national artificial intelligence (AI) policy are welcome and, frankly, overdue. Funding for AI research and updating agency IT systems is a good start. So is guidance for agencies as they begin to regulate industry use of AI. But theres a glaring gap: The White House has been silent about the rules that apply when agencies use AI to perform critical governance tasks.

This matters because, of all the ways AI is transforming our world, some of the most worrying come at the intersection of AI and the awesome power of the state. AI drives the facial recognition police use to surveil citizens. It enables the autonomous weapons changing warfare. And it powers the tools judges use to make life-changing bail, sentencing and parole decisions. Concerns about each have fueled debate and, as to facial recognition in particular, new laws banning use.

Sitting just beyond the headlines, however, is a little-known fact: AI use already is pervasive in government. Prohibition for most uses is not an option, or at least not a wise one. Needed instead is a frank conversation about how to give the government the resources it needs to develop high-quality and fairly deployed AI tools and build sensible accountability mechanisms around their use.

We know because we led a team of lawyers and computer scientists at Stanford and New York universities to advise federal agencies on how to develop and oversee their new algorithmic toolkit.

Our research shows that AI use spans government. By our estimates, half of major federal agencies have experimented with AI. Among the 160 AI uses we found, some such as facial recognition are fueling public outcries. But many others fly under the radar. The Securities and Exchange Commission (SEC) uses AI to flag insider trading; the Centers for Medicare and Medicaid Services uses it to ferret out health care fraud. The Social Security Administration is piloting AI tools to help decide who gets disability benefits, and the Patent and Trademark Office to decide who gets patent protection.

Still other agencies are developing AI tools to communicate with the public, by sifting millions of consumer complaints or using chatbots to field questions from welfare beneficiaries, asylum seekers and taxpayers.

Our research also highlights AIs potential to make government work better and at lower cost. AI tools that help administrative judges spot errors in draft decisions can shrink backlogs that leave some veterans waiting years (sometimes, close to a decade) for benefits. AI can help ensure that the decision to launch a potentially ruinous enforcement action does not reflect the mistakes, biases, or whims of human prosecutors. And AI can help make more precise judgments about which drugs threaten public health.

But the picture is not all rosy.

First, the government has a long way to go. Our teams computer scientists found that few agency AI uses rival the sophistication found in the private sector, making it harder to realize accuracy and efficiency gains. Some may wish to keep agencies low-tech to limit surveillance or otherwise hamstring government. Its not that simple: Government use of makeshift and insecure AI systems puts everyone at risk. Disabled persons, veterans and all of us deserve better.

Second, AI poses deep accountability challenges. When public officials make decisions affecting rights, the law generally requires an explanation. This reason-giving requirement is deeply embedded in law and even enshrined in the Constitution. Yet sophisticated AI tools are opaque; they do not serve up explanations with their outputs. A crucial challenge is how to subject these tools to meaningful accountability and ensure fidelity to longstanding commitments to transparency, reason-giving and non-discrimination.

To address concerns, agencies could be required to politically ventilate AI tools the way they must new regulations. Or they could be made to benchmark AI tools, reserving a pool of cases for human decision and comparing results to AI-assisted ones. However, there are no one-size-fits-all solutions. Open-sourcing computer code might make sense when agencies distribute welfare benefits. But disclosing details when tax enforcers use AI to identify cheaters will just aid evasion.

Third, if we want agencies to make responsible use of AI, their capacity must come from within. Our research shows that many of the best-designed AI tools were created by innovative, public-spirited agency technologists not profit-driven private contractors. The AI tools that help adjudicate disability benefits at the Social Security Administration came from agency insiders with intimate knowledge of governing law and how administrative judges work.

This makes sense. Government work is often complex. Recruiting skilled technologists and updating outmoded computing systems is crucial to building high-quality AI tools and administering them fairly. But it wont be cheap.

Last, AI can fuel political anxieties. Government AI use creates a risk of gaming by better-heeled groups with resources and knowhow. The SECs algorithmic predictions may fall more heavily on smaller companies that, unlike big Wall Street players, lack a stable of quants who can reverse-engineer the model and keep out of the agencys cross-hairs. If citizens come to believe AI systems are rigged, political support for a more effective, tech-savvy government will evaporate.

In short, this is a pivotal moment for government. Managed well, agency AI use can make the government more efficient, accurate and fair. Managed poorly, AI can widen the public-private technology gap, make agencies more vulnerable and less transparent, and heighten concerns about government arbitrariness and biases that are coursing through American politics.

Wherever the nation lands on facial recognition, government AI use is here to stay. The question now is which of these two visions becomes reality.

David F. Engstrom and Daniel E. Ho are professors of law at Stanford University. Catherine M. Sharkey is a professor of law at New York University. Mariano-Florentino Cullar is a justice on the California Supreme Court and professor of law at Stanford University.

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What to do about artificially intelligent government | TheHill - The Hill

DUBLIN--(BUSINESS WIRE)--The "Enterprise Artificial Intelligence Market: Global Opportunity Analysis And Industry Forecast, 2019-2026" report has been added to ResearchAndMarkets.com's offering.

According to this report, the global enterprise artificial intelligence market was valued at $4.68 billion in 2018, and is projected to reach $53.06 billion by 2026, registering a CAGR of 35.4% from 2019 to 2026.

Artificial intelligence has been one of the fastest growing technologies in recent years. AI is associated to human intelligence with similar characteristics, such as language understanding, reasoning, learning, problem solving, and others. Manufacturers in the market witness enormous underlying intellectual challenges in the development and revision of such technology. AI is positioned at the core of the nextogen software technologies in the market. Companies, such as Google, IBM, Microsoft, and other leading players, have actively implemented AI as a crucial part of their technologies.

The increase in number of innovative start-ups and advancements in technology have led to rise in investment in artificial intelligence technologies. Moreover, escalating demand for analyzing and interpreting large amount of data boosts the requirement of artificial intelligence industry solutions. Moreover, development of more reliable cloud computing infrastructures and improvements in dynamic artificial intelligence solutions have a strong impact on the growth potential of the AI market. However, lack of trained and experienced staff hinders the growth of the enterprise Artificial Intelligence (AI) market. Furthermore, increase in adoption of AI in developing economies, such as China, and India are expected to provide major opportunities for the market growth in the upcoming years. Also, ongoing developments in smart virtual assistants and robots are anticipated to be opportunistic for the growth of the enterprise artificial intelligence (AI) market.

KEY BENEFITS

Key Topics Covered:

Chapter 1: Introduction

1.1. Report Description

1.2. Key Benefits For Stakeholders

1.3. Key Market Segments

1.4. Research Methodology

1.4.1. Secondary Research

1.4.2. Primary Research

1.4.3. Analyst Tools & Models

Chapter 2: Executive Summary

2.1. Cxo Perspective

Chapter 3: Market Overview

3.1. Market Definition And Scope

3.2. Key Findings

3.2.1. Top Investment Pockets

3.2.2. Top Impacting Factors

3.3. Porter'S Five Forces Analysis

3.4. Key Player Positioning, 2017

3.5. Market Dynamics

3.5.1. Drivers

3.5.1.1. Increasing Investment In Ai Technologies

3.5.1.2. Growing Need For Analyzing And Interpreting Large Amounts of Data

3.5.1.3. Increasing Customer Satisfaction And Adoption of Reliable Cloud Applications

3.5.2. Restraints

3.5.2.1. Lack of Trained And Experienced Staff

3.5.3. Opportunities

3.5.3.1. Increase In Adoption of Ai In Developing Economies

3.5.3.2. Developing Smarter Virtual Assistants And Robots

3.6. Market Evolution/ Industry Roadmap

Chapter 4: Global Enterprise Artificial Intelligence (Ai) Market, By Deployment Type

4.1. Market Overview

4.2. Cloud

4.2.1. Key Market Trends, Growth Factors, And Opportunities

4.2.2. Market Size And Forecast, By Region

4.2.3. Market Analysis, By Country

4.3. On-Premise

4.3.1. Key Market Trends, Growth Factors, And Opportunities

4.3.2. Market Size And Forecast, By Region

4.3.3. Market Analysis, By Country

Chapter 5: Global Enterprise Artificial Intelligence (Ai) Market, By Technology

5.1. Market Overview

5.2. Machine Learning

5.3. Natural Language Processing (Nlp)

5.4. Image Processing

5.5. Speech Recognition

Chapter 6: Global Enterprise Artificial Intelligence (Ai) Market, By Organization Size

6.1. Market Overview

6.2. Large Enterprises

6.3. Small And Medium Enterprises (Smes)

Chapter 7: Global Enterprise Artificial Intelligence (Ai) Market, By Industry Vertical

7.1. Market Overview

7.2. Media & Advertising

7.3. Bfsi

7.4. It & Telecom

7.5. Retail

7.6. Healthcare

7.7. Automotive & Transportation

7.8. Others

Chapter 8: Global Enterprise Artificial Intelligence (Ai) Market, By Region

8.1. Market Overview

8.2. North America

8.3. Europe

8.4. Asia-Pacific

8.5. LAMEA

Chapter 9: Competitive Landscape

9.1. Competitive Dashboard

9.2. Key Developments

9.3. Top Winning Strategies

Chapter 10: Company Profiles

10.1. Alphabet Inc.

10.2. Apple Inc.

10.3. Amazon Web Services, Inc.

10.4. International Business Machines Corporation

10.5. Ipsoft Inc.

10.6. Microstrategy Incorporated

10.7. Nvidia Corporation

10.8. Sap Se

10.9. Verint Systems Inc.

10.10. Wipro Limited

For more information about this report visit https://www.researchandmarkets.com/r/w3dpp3

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Global Enterprise Artificial Intelligence Market Expected to Grow with a CAGR of 35.4% Over the Forecast Period, 2019-2026 - ResearchAndMarkets.com -...

Over 40% of privacy compliance technology will rely on artificial intelligence (AI) by 2023, up from 5% today, according to Gartner, Inc. Privacy laws, such as General Data Protection Regulation (GDPR), presented a compelling business case for privacy compliance and inspired many other jurisdictions worldwide to follow, said Bart Willemsen, research vice president at Gartner.

More than 60 jurisdictions around the world have proposed or are drafting postmodern privacy and data protection laws as a result. Canada, for example, is looking to modernize their Personal Information Protection and Electronic Documents Act (PIPEDA), in part to maintain the adequacy standing with the EU post-GDPR.

Privacy leaders are under pressure to ensure that all personal data processed is brought in scope and under control, which is difficult and expensive to manage without technology aid. This is where the use of AI-powered applications that reduce administrative burdens and manual workloads come in.

AI-Powered Privacy Technology Lessens Compliance Headaches

At the forefront of a positive privacy user experience (UX) is the ability of an organization to promptly handle subject rights requests (SRRs). SRRs cover a defined set of rights, where individuals have the power to make requests regarding their data and organizations must respond to them in a defined time frame.

According to the 2019 Gartner Security and Risk Survey, many organizations are not capable of delivering swift and precise answers to the SRRs they receive. Two-thirds of respondents indicated it takes them two or more weeks to respond to a single SRR. Often done manually as well, the average costs of these workflows are roughly $1,400 USD, which pile up over time.

The speed and consistency by which AI-powered tools can help address large volumes of SRRs not only saves an organization excessive spend, but also repairs customer trust, said Mr. Willemsen. With the loss of customers serving as privacy leaders second highest concern, such tools will ensure that their privacy demands are met.

Global Privacy Spending on Compliance Tooling Will Rise to $8 Billion Through 2022

Through 2022, privacy-driven spending on compliance tooling will rise to $8 billion worldwide. Gartner expects privacy spending to impact connected stakeholders purchasing strategies, including those of CIOs, CDOs and CMOs. Todays post-GDPR era demands a wide array of technological capabilities, well beyond the standard Excel sheets of the past, said Mr. Willemsen.

The privacy-driven technology market is still emerging, said Mr. Willemsen. What is certain is that privacy, as a conscious and deliberate discipline, will play a considerable role in how and why vendors develop their products. As AI turbocharges privacy readiness by assisting organizations in areas like SRR management and data discovery, well start to see more AI capabilities offered by service providers.

For more information, visit http://www.gartner.com

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Compliance technology will rely on artificial intelligence in the future - ELE Times

The term artificial intelligence was coined in 1956, but AI has become more popular today thanks to increased data volumes, advanced algorithms, and improvements in computing power and storage.

Early AI research in the 1950s explored topics like problem solving and symbolic methods. In the 1960s, the US Department of Defense took interest in this type of work and began training computers to mimic basic human reasoning. For example, the Defense Advanced Research Projects Agency (DARPA) completed street mapping projects in the 1970s. And DARPA produced intelligent personal assistants in 2003, long before Siri, Alexa or Cortana were household names.

This early work paved the way for the automation and formal reasoning that we see in computers today, including decision support systems and smart search systems that can be designed to complement and augment human abilities.

While Hollywood movies and science fiction novels depict AI as human-like robots that take over the world, the current evolution of AI technologies isnt that scary or quite that smart. Instead, AI has evolved to provide many specific benefits in every industry. Keep reading for modern examples of artificial intelligence in health care, retail and more.

Why is artificial intelligence important?

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Artificial Intelligence What it is and why it matters | SAS

Can machines think? Alan Turing, 1950

Less than a decade after breaking the Nazi encryption machine Enigma and helping the Allied Forces win World War II, mathematician Alan Turing changed history a second time with a simple question: "Can machines think?"

Turing's paper "Computing Machinery and Intelligence" (1950), and it's subsequent Turing Test, established the fundamental goal and vision of artificial intelligence.

At it's core, AI is the branch of computer science that aims to answer Turing's question in the affirmative. It is the endeavor to replicate or simulate human intelligence in machines.

The expansive goal of artificial intelligence has given rise to manyquestions and debates. So much so, that no singular definition of the field is universally accepted.

The major limitation in defining AI as simply "building machines that are intelligent" is that it doesn't actually explain what artificial intelligence is? What makes a machine intelligent?

In their groundbreaking textbook Artificial Intelligence: A Modern Approach, authors Stuart Russell and Peter Norvig approach the question by unifying their work around the theme of intelligent agents in machines. With this in mind, AI is "the study of agents that receive percepts from the environment and perform actions." (Russel and Norvig viii)

Norvig and Russell go on to explore four different approaches that have historically defined the field of AI:

The first two ideas concern thought processes and reasoning, while the others deal with behavior. Norvig and Russell focus particularly on rational agents that act to achieve the best outcome, noting "all the skills needed for the Turing Test also allow an agent to act rationally." (Russel and Norvig 4).

Patrick Winston, the Ford professor of artificial intelligence and computer science at MIT, defines AI as "algorithms enabled by constraints, exposed by representations that support models targeted at loops that tie thinking, perception and action together."

While these definitions may seem abstract to the average person, they help focus the field as an area of computer science and provide a blueprint for infusing machines and programs with machine learning and other subsets of artificial intelligence.

While addressing a crowd at the Japan AI Experience in 2017, DataRobot CEO Jeremy Achin began his speech by offering the following definition of how AI is used today:

"AI is a computer system able to perform tasks that ordinarily require human intelligence... Many of these artificial intelligence systems are powered by machine learning, some of them are powered by deep learning and some of them are powered by very boring things like rules."

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What is Artificial Intelligence? How Does AI Work? | Built In

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In the not-so-far future the polar ice caps have melted and the resulting rise of the ocean waters has drowned all the coastal cities of the world. Withdrawn to the interior of the continents, the human race keeps advancing, reaching the point of creating realistic robots (called mechas) to serve them. One of the mecha-producing companies builds David, an artificial kid which is the first to have real feelings, especially a never-ending love for his "mother", Monica. Monica is the woman who adopted him as a substitute for her real son, who remains in cryo-stasis, stricken by an incurable disease. David is living happily with Monica and her husband, but when their real son returns home after a cure is discovered, his life changes dramatically. Written byChris Makrozahopoulos

Budget:$100,000,000 (estimated)

Opening Weekend USA: $29,352,630,1 July 2001

Gross USA: $78,616,689

Cumulative Worldwide Gross: $235,926,552

Runtime: 146 min

Aspect Ratio: 1.85 : 1

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A.I. Artificial Intelligence (2001) - IMDb