ORANGE, Calif.--(BUSINESS WIRE)--Alignment Health Plan, a Southern Calif.-based Medicare Advantage plan, is expanding its coverage into eight greater Sacramento and Bay Area counties, where more than 862,000 Medicare recipients live1: Marin, Placer, Sacramento, San Francisco, San Mateo, Santa Cruz, Sonoma and Yolo. This means that the more than 5.3 million Medicare-eligible individuals who live in Alignments full service area will have the option to select one of its plans with exclusive access to a concierge-level member services team and health care black card during Medicares annual election period, Oct. 15 through Dec. 7, 2019.

Available only to Alignment Health Plan members, the ACCESS On-Demand Concierge program provides round-the-clock access to a board-certified doctor by phone or video; a dedicated concierge team; and a special black card that can be used as a debit card to buy eligible items at more than 50,000 retailers across the country.

The company today revealed its 2020 portfolio of health plans, featuring benefits2 designed to provide better care, service and value than traditional Medicare, including:

Alignments ACCESS On-Demand Concierge is evolving as health care consumers expectations become more sophisticated. We purposely built Alignment to provide seniors the care and service they deserve, and we are delighted to be able to give them peace of mind that all their health care needs will be taken care of, said John Kao, CEO of Alignment Healthcare, which owns and operates Alignment Health Plan.

Expanded ACCESS On-Demand Concierge Includes Groceries, Grandkids On-Demand

With new flexibility from federal regulators to provide supplemental benefits that address social determinants of health, Alignment has added both grocery and companion care benefits to certain plans in 2020 for members who have qualifying chronic diseases. The grocery benefit provides a monthly allowance of $10 to $20 per month depending on the plan, automatically loaded onto the members black card to buy eligible groceries at participating stores like CVS, Walmart and Walgreens. The companion care benefit connects college students with members who need assistance with non-medical services such as light house chores, technology lessons and general companionship. These grandkids on-demand are available to meet with qualified members for up to two hours per day, 12 hours per quarter and 48 hours per year. Medical records will be used to establish qualification and, once qualified, the member will qualify for the remainder of the plan year.

Expanded Coverage Options Now Available in Northern California

During this years annual election period, Alignment is offering new Medicare Advantage options that give Northern California members access to the Sutter Health network: Sutter Advantage (HMO) and My Choice (PPO). The six new HMO plans are available in eight counties3 and the four new PPO plans are available in seven counties.4 Depending on their plan, effective Jan. 1, 2020, members will be able to seek in-network care with the high-quality doctors, hospitals, medical foundations and other health care services affiliated with Sutter Health, a not-for-profit integrated health care system. The network also includes some providers associated with Sutter Independent Physicians and Mills-Peninsula Medical Group, two physician organizations aligned with Sutter.

In Marin County, Alignment is introducing its CalPlus (HMO) and Platinum (HMO) plans, with access starting Jan. 1, 2020 to in-network care from Brown & Toland Physicians, a network of more than 2,700 Bay Area doctors.

Alignment Healthcare is powered by AVA, the companys proprietary command center, an artificial intelligence-based platform that provides real-time analytics on every Alignment member, empowering clinicians with medical insights that lead to life-saving care, sometimes predicting care before its even needed. In addition to its expansion in Northern California, Alignment serves Los Angeles, Orange, Riverside, San Bernardino, San Diego, San Joaquin, Santa Clara and Stanislaus counties in California. For more information, visit alignmenthealthplan.com.

About Alignment Healthcare

Alignment Healthcare is redefining the business of health care by shifting the focus from payments to people. Weve created a new model for health care delivery that cuts costs and improves lives by unraveling the inefficiencies of the current system to drive patients, providers and payers toward a common goal of wellness. Harnessing best practices from Medicare Advantage, our innovative data-management technology allows us to commit to caring for seniors and those who need it most: the chronically ill and frail. With offices and care centers across the country, Alignment Healthcare provides partners and patients with customized care and service where they need it and when they need it, including clinical coordination, risk management and technology facilitation. Alignment Healthcare offers health plan options to California residents through Alignment Health Plan, and partners with select health plans in North Carolina and Florida to help deliver better benefits at lower costs. For more information, please visit http://www.alignmenthealthcare.com.

1 Medicare enrollment as of July 2019, https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/CMSProgramStatistics/Dashboard.html

2 Benefits vary by plan. Copays and certain restrictions may apply.

3 Placer, Sacramento, San Francisco, San Mateo, Santa Clara, Santa Cruz, Sonoma and Yolo

4 Placer, Sacramento, San Mateo, San Joaquin, Sonoma, Stanislaus and Yolo

Link:

On-Demand Concierge & Life-Saving Artificial Intelligence Now Available to 5.3M California Medicare Recipients Through Alignment Health Plan -...

Spending on artificial intelligence (AI) systems in the Middle East and Africa (MEA) is expected to maintain its strong growth trajectory as businesses continue to invest in projects that utilize the capabilities of AI software and platforms. That's according to the latest forecast from global technology research and consulting firm International Data Corporation (IDC), with a recent update to the firm's Worldwide Artificial Intelligence Systems Spending Guide showing that spending on AI systems in MEA is expected to reach $374.2 million next year, up from $261.8 million in 2018 and an anticipated $310.3 million in 2019. Over the longer term, IDC expects spending in the region to increase at a compound annual growth rate (CAGR) of 19% for the 2018-2023 period.

"The AI software applications and AI platforms markets continue to show steady growth in the MEA region, and we expect this momentum to continue over the forecast period," says Manish Ranjan, IDC's program manager for software and cloud in the Middle East, Africa, and Turkey. "The use of AI and machine learning (ML) is on the rise in a wide variety of business applications from ERP and CRM to analytics, content management, and collaboration solutions. Many global vendors have started embedding AI, ML, and cognitive applications to provide ultimate business benefits to their users."

Spending on AI systems in the region will be led by the banking and retail industries. Together, these verticals will account for more than 33% of spending in 2020, followed by federal/central governments and telecommunication industry. Investments in AI systems across MEA will continue to be driven by a wide range of use cases. The three largest use cases automated customer service agents, IT automation, and automated threat intelligence and prevention systems will account for around 30% of total AI spending in 2020.

"With the growing adoption of various use cases across all industries, organizations are continuing to invest significantly in optimizing their business processes, automating their operations and enhancing their products and services offerings in order to maximize the overall customer experience," says Ranjan.

Looking at individual countries, IDC's forecast shows South Africa accounting for 20.5% of AI spending in MEA during 2020, followed by the UAE on 19.7%. Saudi Arabia will be the region's third-biggest spender next year with 15.7% share. Turkey will rank fourth, accounting for 11.1% of regional AI spending.

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Spending on Artificial Intelligence systems in the Middle East & Africa to top $374 million in 2020 - TelecomTV

All digital marketing operations are now affected by AI-powered tools

Digital marketers are trying hard to leverage AI for strategic planning and campaign decision making

A roadmap is required to stay ahead from the crowd, few tips to prepare for that

Artificial Intelligence (AI) technology is now a big thing now in digital marketing. All digital marketing operations are now affected by AI-powered tools. From startups to large firms are opting for AI-powered digital marketing tools to enhance campaign planning & decision making.

AI-based tools are now a flourishing market, with a drastic change in demand. According to most of the digital marketers AI enhancing all the areas where the predictive analysis, decision making & automation efforts required.

Digital marketers are trying hard to leverage AI for strategic planning and campaign decision making. Most of them found AI helpful and enhancing their productivity and reducing their efforts. AI-powered analytics tools provide better insights for campaign management, budget planning, & ROI analysis. AI can gather the insights from a truckload of unstructured and structured data sources in a fraction of sec.

All the human interactions with a business affect the digital marketing strategy and business revenue.

Reportedly, brands who have recently adopted AI for marketing strategy, predict a 37 percent reduction in costs along with a 39 percent increase in revenue figures on an average by the end of 2020 alone.

Artificial intelligence tools help digital marketers to understand customer behavior and make the right recommendation at the right time. A tool with the millions of predefined conditions knows how customer react to a particular situation, ad copy, videos or any other touchpoint. While human cant assess the large set of data better than a machine in a limited timeframe.

You can collect the insights on your fingertips with the help of AI. Where to find an audience? how to interact with them? What to send them? How to send them? What is the right time to connect? When to send a follow-up? All these answers lie in the AI-powered digital marketing platforms.

With a smart analysis pattern AI, tools can make better suggestions and help in decision making. A personalized content recommendation to the right audience at the right time guarantees the success of any campaign.

Digital marketers are really getting pushed harder to demonstrate the success of content and campaigns. With AI tools utilization of potential data is very easy and effective.

According to a2019 studyby Forrester and Albert, only 26% of marketers are making use of autonomous AI, while 74% take a more manual approach with assistance from AI.

AI technology evolving every aspect of digital marketing to name a few audience targeting, audience interest analysis, web optimization, smart content writing and recommendation, advanced tracking and reporting and more.

AI in digital marketing is poised to reach a global market of $21 Bn by 2023, growing at a steady Compound Annual Growth Rate (CAGR) of 26%.

Digital marketing technology platforms are evolving with great pace and it needs some specific set of skills. If you want to opt for smart marketing technology you should start using AI-powered tools from a small scale and increase the limits as you grow. A roadmap is required to stay ahead from the crowd, few tips to prepare for that:

AI has a remarkable impact on all the areas of digital marketing and will keep growing in the future. The future of digital marketing is here, the fast you learn the faster you grow. The days are gone when digital marketers run the data and find the insight and other team works on the campaign based on the insights. Things are moving with great pace in digital marketing space. The early adopters will win the game!

Original post:

How Artificial Intelligence Is Changing The Landscape Of Digital Marketing - Inc42 Media

Games are a serious matter, and they have been for thousands of years. Fun is fun, but games need rules. Before the dawn of proprietary gamesthe ones in boxesmost board games didnt come with instructions, though. The rules were passed on orally, and thats one of the reasons that ancient games still played today, such as mancala or backgammon, have murky origins. Now, an interdisciplinary team of software engineers, game historians, and archaeologists is attempting to piece together the lineage and rules of ancient board gameswith modern technology.

Board games are one of the oldest documented forms of leisure. It is hard to tell when hide-and-seek or chopsticks came along, because they dont leave any material evidence, but game boards and playing tokens have given archaeologists a lot to work with. They were etched on the landscape, left or lost in habitation sites, and even buried with the dead (for playing in the afterlife). They occur all over the world, from Viking hnefatafl to Chinese liubo to a mancala variant in Borneo, and involve a range of boards, dice, and pieces. And games spanned social divisions, from the general public, some of whom played on game boards incised into surfaces in temples, to ancient royalty, who had suitably luxurious game paraphernalia.

Tutankhamun had four senet sets in his tomb, says Cameron Browne, a computer scientist at Maastricht University, referring to an ancient Egyptian game about the passage to the afterlife. Browne is the head of the Digital Ludeme Project (lude from the Latin, to play), which is using archaeological evidence and modern game rules to figure out how hundreds of ancient games and their variants were played, and changed over time and by location.

As far as the archaeological record goes, board gaming began with senet, nearly 1,800 years before Tut was even born. About 700 years later, in ancient Sumer, the Royal Game of Ur was important enough that its rules were written downactually incised on a cuneiform tablet. (Otherwise, rules were almost never set in stone.) The names of many ancient games have slipped through the cracks of history, so researchers identify them by what remains. Theres 33 Circles from Egypt, 10-Ring from Bronze Age Crete, and the Middle Eastern 58 Holes. Since the rules have been lost over time, the way many ancient games were played is based on speculation. With so many games, and so many variations, Brownes work isnt merely an archaeological-meets-ludological project. Its genealogical, too.

Were really dealing with families of games, Browne says. The mancala games, the chess-like games, the tafl games, card games. Each of these probably have their own distinct family.

Like the humans that invented them, games evolved over time. Board sizes have changed, pieces have shifted shape, and rules have mutated as games have been passed along and slowly fanned out across the map.

The Silk Road is a perfect example for how the games spread, Browne says. Many of these games started in the Fertile Crescent and progressed through Europe and Asia.

And as they spread they left behind a convoluted lineage of adaptions and local flavor. For example, according to Ulrich Schdler, a games historian at the Swiss Museum of the Game and an editor of the journal Board Game Studies, the aristocrats at Versailles in the 17th century played a languid form of trictrac, a backgammon variant. When the game got to the banking Brits across the channel, with a sense of economy of time, it was compressed into 15-minute bouts.

This all adds up to a complex, poorly documented, and global family tree, and that makes defining any given, long-forgotten rule set a challenge. Games changed hands across cultures, over the course of millennia. Many games fell out of style, disappeared altogether, or evolved into something unrecognizable. The archaeological record is fragmentary at best, and game compendiasuch as Alfonso X of Castiles 1284 Libro de los Juegosare few and far between. What games did leave, in some cases, were boards and spare pieces, which in turn has led to a lot of guesswork among amateurs, enthusiasts, and interested insiders. The same Howard Carter and Lord Carnarvon who opened Tuts tomb proposed what is now a common play style for 58 Holes. Modern game experts can only pick up the pieces.

Lost rules are made in a very superficial way by people who dont really know games. From archaeologists, philologists, Ive read so many, Schdler says. Theyre made up by people who have never played a game, or dont know their mechanics very well. Bringing some order to this complicated history might take something more than speculation.

At the Fourth Annual Board Game Symposium in New York in September 2019, Walter Crist, an anthropologist with the Digital Ludeme Project, briefly described how their project is dealing with thisby going from we cant know what the rules of the game are to figuring out what the rules must have been, using artificial intelligence.

To do this, the researchers model each game using ludemes (literally, game memes) to digitally reconstruct the games based on their fundamental conceptual information. The ludeme idea breaks down the games formits physical components and any known rule setand separates it from function, or how those components are employed in reality. Ludemes are game genes, and once the genetic information is mapped, the Digital Ludeme Project can calculate the ludemic distance between games, or the number of steps necessary for one game to evolve into another. The ludeme concept makes all game information more manageable pieces of a much larger puzzle. By adding or removing any one component, a game might be a step closer to another one, and then with historical and archaeological data, the researchers can tell whether one game borrowed from another. Then, in a crucial step, the ludemes are loaded into a game system made specially for the projectLUDII game softwareand the computers go to work by playing every game thousands upon thousands of times in different variations.

In a game of Boggleproprietary, invented in 1972players shake an enclosed box of 16 cubes, each inscribed with a different letter on each side. Once the cubes settle, players try and make as many words as they can by connecting the face-up letters. This is precisely what the LUDII software is doing, with game simulations in the place of letters, and functional games in the place of words.

Schdler has long worked on reconstructing ancient games without the assistance of artificial intelligence, and says that to find out if a set of rules works, you need to play it a lot of times to find glitches where the rules break down and leave the game unplayable, unevenly matched, or impossible to win. A veteran of many, many of these playthroughs, Schdler is excited about the Digital Ludeme Project.

If we have a large number of ludemes, elements that make up a rule set, we can perhaps see that certain ludemes go together, he says. You can simply let the computer play 10,000 times and this will give you a result as to whether or not these rules work.

With so many ludemes involved with each game, what would have taken a lifetime of playtesting can be done in hours, says Eddie Duggan, a games historian at the University of Suffolk. Once the game has been programmed using the LUDII game system to model, play, and analyze the games, it will be possible to determine a games interestingness or game quality. The more test runs a given game gets in the machine learning system, the better the Digital Ludeme Project team can understand how the game would most optimally operate.

Our reconstructions will also, crucially, provide confidence scores for our reconstructions, says Crist, the teams anthropologist, to communicate our conclusions about the likelihood that these rule sets could reflect the reality of the past.

The game family tree operates like an evolutionary tree, and can be tracked through a method called computational phylogenetics. After each game is boggled, and thousands of different rule sets are tested, the Digital Ludeme Project determines how the game fits in with others, and can track their changes like in a game of telephone. Between points A and B, Duggan notes, there are many small steps, so the project provides opportunities to both interpolate new games and optimize existing ones.

As well as revealing something about ancient games, he says, it is likely that new games will be discovered that were not known or played in the ancient world.

The Digital Ludeme Projects ultimate goal is to make hundreds of reconstructed and optimized ancient games available online for everyone to learn about and enjoy.

Games without rulesplayis something all animals do, says Duggan. But one day somebody used a stick to scratch a line in the dirt and started to arrange pebbles or seeds along the line according to rules and invented a game. That is when we became human.

As the project continues, Browne says theyll pay special mind to quality and historical authenticity. The compendium of fun is set to officially publish in January 2020, though some of the games are playable now. To, Duggan it all illustrates our collective adaptabilitywhen applied to the task of fun.

Whether we are modern board game players, Vikings, ancient Romans, ancient Egyptians, ancient Babylonians, he says, we are all Homo ludens, the species that plays games.

Whats the oldest game youve ever played? Let us know, and share any other thoughts and feelings you might have about the story in the Atlas Obscura Community Forums!

Originally posted here:

Researchers Are Using Artificial Intelligence to Reconstruct Ancient Games - Atlas Obscura

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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, Wide Release

Gross USA: $78,616,689, 23 September 2001

Cumulative Worldwide Gross: $235,927,000

Runtime: 146 min

Aspect Ratio: 1.85 : 1

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A.I. Artificial Intelligence (2001) - IMDb

"Artificial intelligence" might sound like an overused buzzword to many people. But astute investors know AI is already positively changing the way companies all over the world do business.

With that in mind, we asked three top Motley Fool contributors to each discuss an artificial intelligence stock you should be watching closely this month. Read on to see why they chose Facebook (NASDAQ:FB), Visa (NYSE:V), and Alphabet (NASDAQ:GOOG) (NASDAQ:GOOGL).

IMAGE SOURCE: GETTY IMAGES.

Steve Symington (Facebook): Facebook is set to release first-quarter 2019 results next Wednesday, April 24, 2019, and what's contained in the report will help set the tone for both the online advertising and broader tech industries. Shares of the social media juggernaut are already up 36% so far in 2018 -- albeit after plunging 26% last year as it traversed a difficult combination of data-privacy scandals, the spread of misinformation on its platforms, and concerns over decelerating top-line growth as Facebook builds on a larger base. With the help of AI, Facebook is striving to address those concerns.

That said, Facebook management did offer some color on what to expect during the January conference call, namely that first-quarter revenue growth should decelerate by a mid-single-digit-percent range (from 30% in Q4 2018). To that end -- and though we don't usually pay close attention to Wall Street's demands -- most analysts see the company delivering earnings of $1.63 per share on a 25.1% increase in revenue, to $14.97 billion.

Just as important will be the trends underlying that growth. Last quarter, the company saw daily and monthly active users each climb 9%, to 1.52 billion and 2.32 billion, respectively, and roughly 2.7 billion people hopped on at least one of its platforms -- including Facebook, Instagram, WhatsApp, and Messenger -- every month. If Facebook is seeing success with its recent efforts to combat last year's problems and win back the trust of the public, we should hope to see its number of users continue to steadily climb.

Anders Bylund (Visa): Payment-processing veteran Visa may not be the first company that springs to mind in a discussion about artificial intelligence, but maybe it should. Take this snippet from a management presentation at a recent industry conference:

"The services that we offer that are extremely high value-added and in many ways have economics that are very similar to our core business and very much leverage of core business or things like risks services, fraud management and all that, authentication services, data analytics where we continue to add to our capabilities with AI," said CFO Vasant Prabhu, according to a transcript compiled by Seeking Alpha. "We've always been doing machine learning, especially on fraud."

Yep, you can thank artificial intelligence for the fraud warnings that drop in whenever your card issuer finds suspicious activity in your credit card accounts. Visa and its peers have actually been on the bleeding edge of AI tools in practical use for many years. These antifraud processes should continue improving over time as Visa takes advantage of more advanced technologies, ranging from neural networks to blockchain transactions.

Visa is gearing up for a second-quarter earnings report next Wednesday. AI probably won't be a big talking point in that presentation, but you can rest assured that Visa takes advantage of the technology in a big way and will only continue to deepen its machine learning roots.

Chris Neiger (Alphabet): Alphabet makes the bulk of its revenue by selling ads across its various Google services platforms, but the company is also an artificial intelligence powerhouse, and it's making considerable investments in AI now -- so it can benefit later.

Take, for example, Alphabet's self-driving vehicle company Waymo. The company has logged more than 10 million miles of autonomous driving, and at the end of 2018, Waymo launched one of the first commercial self-driving car services in the country. Without AI, Waymo's vehicles wouldn't be able to navigate traffic, avoid pedestrians, or learn from past driving experiences. The company stands to benefit from this AI pursuit by licensing some of its tech to other companies and selling its own services, all of which could help Waymo generate nearly $100 billion in sales over the next decade.

Autonomous vehicles aren't Alphabet's only AI bet though. Alphabet also owns DeepMind, an artificial-intelligence tech company that's already proving itself invaluable. Just last month, DeepMind used some of its machine learning algorithms to predict when some of Google's wind farms would produce the most wind, thereby generating the most energy, and scheduled some of the energy to be sent back to the electric grid. Why is this important? Because AI-powered tools like this are poised to help add $15.7 trillion to the global economy by 2030 by creating new services and making existing ones more efficient. Imagine the same tech being used not just for Google's wind farms but in power grids across the country.

With Alphabet already knee deep in developing the AI tools and services that will make our lives more efficient, investors should consider snatching up shares of this tech giant now.

In today's fast-changing technology world, we certainly can't guarantee that any given stock will go on to deliver outsized returns and beat the market in the process. But with the help of AI, it appears Facebook, Visa, and Alphabet could be poised to do exactly that. And we think at the very least, investors would do well to add these stocks to their watch lists this month.

The rest is here:

3 Top Artificial Intelligence Stocks to Watch in April ...

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Since the invention of computers or machines, their capability to perform various tasks went on growing exponentially. Humans have developed the power of computer systems in terms of their diverse working domains, their increasing speed, and reducing size with respect to time.

A branch of Computer Science named Artificial Intelligence pursues creating the computers or machines as intelligent as human beings.

According to the father of Artificial Intelligence, John McCarthy, it is The science and engineering of making intelligent machines, especially intelligent computer programs.

Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think.

AI is accomplished by studying how human brain thinks, and how humans learn, decide, and work while trying to solve a problem, and then using the outcomes of this study as a basis of developing intelligent software and systems.

While exploiting the power of the computer systems, the curiosity of human, lead him to wonder, Can a machine think and behave like humans do?

Thus, the development of AI started with the intention of creating similar intelligence in machines that we find and regard high in humans.

To Create Expert Systems The systems which exhibit intelligent behavior, learn, demonstrate, explain, and advice its users.

To Implement Human Intelligence in Machines Creating systems that understand, think, learn, and behave like humans.

Artificial intelligence is a science and technology based on disciplines such as Computer Science, Biology, Psychology, Linguistics, Mathematics, and Engineering. A major thrust of AI is in the development of computer functions associated with human intelligence, such as reasoning, learning, and problem solving.

Out of the following areas, one or multiple areas can contribute to build an intelligent system.

The programming without and with AI is different in following ways

In the real world, the knowledge has some unwelcomed properties

AI Technique is a manner to organize and use the knowledge efficiently in such a way that

AI techniques elevate the speed of execution of the complex program it is equipped with.

AI has been dominant in various fields such as

Gaming AI plays crucial role in strategic games such as chess, poker, tic-tac-toe, etc., where machine can think of large number of possible positions based on heuristic knowledge.

Natural Language Processing It is possible to interact with the computer that understands natural language spoken by humans.

Expert Systems There are some applications which integrate machine, software, and special information to impart reasoning and advising. They provide explanation and advice to the users.

Vision Systems These systems understand, interpret, and comprehend visual input on the computer. For example,

A spying aeroplane takes photographs, which are used to figure out spatial information or map of the areas.

Doctors use clinical expert system to diagnose the patient.

Police use computer software that can recognize the face of criminal with the stored portrait made by forensic artist.

Speech Recognition Some intelligent systems are capable of hearing and comprehending the language in terms of sentences and their meanings while a human talks to it. It can handle different accents, slang words, noise in the background, change in humans noise due to cold, etc.

Handwriting Recognition The handwriting recognition software reads the text written on paper by a pen or on screen by a stylus. It can recognize the shapes of the letters and convert it into editable text.

Intelligent Robots Robots are able to perform the tasks given by a human. They have sensors to detect physical data from the real world such as light, heat, temperature, movement, sound, bump, and pressure. They have efficient processors, multiple sensors and huge memory, to exhibit intelligence. In addition, they are capable of learning from their mistakes and they can adapt to the new environment.

Here is the history of AI during 20th century

Karel apek play named Rossum's Universal Robots (RUR) opens in London, first use of the word "robot" in English.

Foundations for neural networks laid.

Isaac Asimov, a Columbia University alumni, coined the term Robotics.

Alan Turing introduced Turing Test for evaluation of intelligence and published Computing Machinery and Intelligence. Claude Shannon published Detailed Analysis of Chess Playing as a search.

John McCarthy coined the term Artificial Intelligence. Demonstration of the first running AI program at Carnegie Mellon University.

John McCarthy invents LISP programming language for AI.

Danny Bobrow's dissertation at MIT showed that computers can understand natural language well enough to solve algebra word problems correctly.

Joseph Weizenbaum at MIT built ELIZA, an interactive problem that carries on a dialogue in English.

Scientists at Stanford Research Institute Developed Shakey, a robot, equipped with locomotion, perception, and problem solving.

The Assembly Robotics group at Edinburgh University built Freddy, the Famous Scottish Robot, capable of using vision to locate and assemble models.

The first computer-controlled autonomous vehicle, Stanford Cart, was built.

Harold Cohen created and demonstrated the drawing program, Aaron.

Major advances in all areas of AI

The Deep Blue Chess Program beats the then world chess champion, Garry Kasparov.

Interactive robot pets become commercially available. MIT displays Kismet, a robot with a face that expresses emotions. The robot Nomad explores remote regions of Antarctica and locates meteorites.

Originally posted here:

Artificial Intelligence - Overview - tutorialspoint.com

Sept. 20, 2018 5:30 a.m. ET

Some Fortune 500 companies are using tools that deploy artificial intelligence to weed out job applicants. But is this practice fair? In this episode of Moving Upstream, WSJ's Jason Bellini investigates.

Some Fortune 500 companies are using tools that deploy artificial intelligence to weed out job applicants. But is this practice fair? In this episode of Moving Upstream, WSJ's Jason Bellini investigates.

Hiring is undergoing a profound revolution.

Nearly all Fortune 500 companies now use some form of automation -- from robot avatars interviewing job candidates to computers weeding out potential employees by scanning keywords in resumes. And more and more companies are using artificial intelligence and machine learning tools to assess possible employees.

DeepSense, based in San Francisco and India, helps hiring managers scan peoples social media accounts to surface underlying personality traits. The company says it uses a scientifically based personality test, and it can be done with or without a potential candidates knowledge.

The practice is part of a general trend of some hiring companies to move away from assessing candidates based on their resumes and skills, towards making hiring decisions based on peoples personalities.

The Robot Revolution: An inside look at how humanoid robots are evolving.

WSJS Jason Bellini explores breakthrough technologies that are reshaping our world and beginning to impact human happiness, health and productivity. Catch the latest episode by signing up here.

Cornell sociology and law professor Ifeoma Ajunwa said shes concerned about these tools potential for bias. Given the large scale of these automatic assessments, she believes potentially faulty algorithms could do more damage than one biased human manager. And she wants scientists to test if the algorithms are fair, transparent and accurate.

In the episode of Moving Upstream above, correspondent Jason Bellini visits South Jordan, Utah-based HireVue, which is delivering AI-based assessments of digital interviews to over 50 companies. HireVue says its algorithm compares candidates tone of voice, word clusters and micro facial expressionsCC with people who have previously been identified as high performers on the job.

Write to Jason Bellini at jason.bellini@wsj.com and Hilke Schellmann at hilke.schellmann@wsj.com

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Artificial Intelligence: The Robots Are Now Hiring - WSJ

AI (artificial intelligence) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction. Particular applications of AI include expert systems, speech recognition and machine vision.

AI can be categorized in any number of ways, but here are two examples.

The first classifies AI systems as either weak AI or strong AI.Weak AI, also known as narrow AI, is an AI system that is designed and trained for a particular task. Virtual personal assistants, such as Apple's Siri, are a form of weak AI.

Strong AI, also known as artificial general intelligence, is an AI system with generalized human cognitive abilities so that when presented with an unfamiliar task, it has enough intelligence to find a solution. TheTuring Test, developed by mathematician Alan Turing in 1950, is a method used to determine if a computer can actually think like a human, although the method is controversial.

Alec Ross on AI and robotics

The second example comes from Arend Hintze, an assistant professor of integrative biology and computer science and engineering at Michigan State University. He categorizes AI into four types, from the kind of AI systems that exist today to sentient systems, which do not yet exist. His categories are as follows:

AI is incorporated into a variety of different types of technology. Here are seven examples.

Artificial intelligence has made its way into a number of areas. Here are six examples.

While AI tools present a range of new functionality for businesses, artificial intellignce also raises some ethical questions. Deep learning algorithms, which underpin many of the most advanced AI tools, only know what's in the data used during training. Most available data sets for training likely contain traces of human bias. This in turn can make the AI tools biased in their function. This has been seen in the Microsoft chatbot Tay, which learned a misogynistic and anti-Semitic vocabulary from Twitter users, and the Google Photo image classification tool that classified a group of African Americans as gorillas.

The application of AI in the realm of self-driving cars also raises ethical concerns. When an autonomous vehicle is involved in an accident, liability is unclear. Autonomous vehicles may also be put in a position where an accident is unavoidable, forcing it to make ethical decisions about how to minimize damage.

Another major concern is the potential for abuse of AI tools. Hackers are starting to use sophisticated machine learning tools to gain access to sensitive systems, complicating the issue of security beyond its current state.

Deep learning-based video and audio generation tools also present bad actors with the tools necessary to create so-called deepfakes, convincingly fabricated videos of public figures saying or doing things that never took place.

Despite these potential risks, there are few regulations governing the use AI tools, and where laws do exist, the typically pertain to AI only indirectly. For example, federal Fair Lending regulations require financial institutions to explain credit decisions to potential customers, which limit the extent to which lenders can use deep learning algorithms, which by their nature are typically opaque. Europe's GDPR puts strict limits on how enterprises can use consumer data, which impedes the training and functionality of many consumer-facing AI applications.

In 2016, the National Science and Technology Council issued a report examining the potential role governmental regulation might play in AI development, but it did not recommend specific legislation be considered. Since that time the issue has received little attention from lawmakers.

John McCarthy, an American computer scientist, coined the term "artificial intelligence" in 1956 at the Dartmouth Conference where the discipline was born. Today, it is an umbrella term that encompasses everything from robotic process automation to actual robotics. It has gained prominence recently due, in part, tobig data, or the increase in speed, size and variety of data businesses now collect. AI can perform tasks such as identifying patterns in data more efficiently than humans, enabling businesses to gain more insight from theirdata.

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What is AI (artificial intelligence)? - Definition from WhatIs.com

Many AI researchers roll their eyes when seeing this headline:Stephen Hawking warns that rise of robots may be disastrous for mankind. And as many havelost count of how many similar articles theyveseen.Typically, these articles are accompanied by an evil-looking robot carrying a weapon, and they suggest we should worry about robots rising up and killing us because theyve become conscious and/or evil.On a lighter note, such articles are actually rather impressive, because they succinctly summarize the scenario that AI researchers dontworry about. That scenario combines as many as three separate misconceptions: concern about consciousness, evil, androbots.

If you drive down the road, you have a subjective experience of colors, sounds, etc. But does a self-driving car have a subjective experience? Does it feel like anything at all to be a self-driving car?Although this mystery of consciousness is interesting in its own right, its irrelevant to AI risk. If you get struck by a driverless car, it makes no difference to you whether it subjectively feels conscious. In the same way, what will affect us humans is what superintelligent AIdoes, not how it subjectively feels.

The fear of machines turning evil is another red herring. The real worry isnt malevolence, but competence. A superintelligent AI is by definition very good at attaining its goals, whatever they may be, so we need to ensure that its goals are aligned with ours. Humans dont generally hate ants, but were more intelligent than they are so if we want to build a hydroelectric dam and theres an anthill there, too bad for the ants. The beneficial-AI movement wants to avoid placing humanity in the position of those ants.

The consciousness misconception is related to the myth that machines cant have goals.Machines can obviously have goals in the narrow sense of exhibiting goal-oriented behavior: the behavior of a heat-seeking missile is most economically explained as a goal to hit a target.If you feel threatened by a machine whose goals are misaligned with yours, then it is precisely its goals in this narrow sense that troubles you, not whether the machine is conscious and experiences a sense of purpose.If that heat-seeking missile were chasing you, you probably wouldnt exclaim: Im not worried, because machines cant have goals!

I sympathize with Rodney Brooks and other robotics pioneers who feel unfairly demonized by scaremongering tabloids,because some journalists seem obsessively fixated on robots and adorn many of their articles with evil-looking metal monsters with red shiny eyes. In fact, the main concern of the beneficial-AI movement isnt with robots but with intelligence itself: specifically, intelligence whose goals are misaligned with ours. To cause us trouble, such misaligned superhuman intelligence needs no robotic body, merely an internet connection this may enable outsmarting financial markets, out-inventing human researchers, out-manipulating human leaders, and developing weapons we cannot even understand. Even if building robots were physically impossible, a super-intelligent and super-wealthy AI could easily pay or manipulate many humans to unwittingly do its bidding.

The robot misconception is related to the myth that machines cant control humans. Intelligence enables control: humans control tigers not because we are stronger, but because we are smarter. This means that if we cede our position as smartest on our planet, its possible that we might also cede control.

Excerpt from:

Benefits & Risks of Artificial Intelligence - Future of Life Institute

Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry.

Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as:

Knowledge engineering is a core part of AI research. Machines can often act and react like humans only if they have abundant information relating to the world. Artificial intelligence must have access to objects, categories, properties and relations between all of them to implement knowledge engineering. Initiating common sense, reasoning and problem-solving power in machines is a difficult and tedious task.

Machine learning is also a core part of AI. Learning without any kind of supervision requires an ability to identify patterns in streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions. Classification determines the category an object belongs to and regression deals with obtaining a set of numerical input or output examples, thereby discovering functions enabling the generation of suitable outputs from respective inputs. Mathematical analysis of machine learning algorithms and their performance is a well-defined branch of theoretical computer science often referred to as computational learning theory.

Machine perception deals with the capability to use sensory inputs to deduce the different aspects of the world, while computer vision is the power to analyze visual inputs with a few sub-problems such as facial, object and gesture recognition.

Robotics is also a major field related to AI. Robots require intelligence to handle tasks such as object manipulation and navigation, along with sub-problems of localization, motion planning and mapping.

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What is Artificial Intelligence (AI)? - Definition from Techopedia

In 2017, we published a popular post on artificial intelligence (AI) technologies that would dominate that year, based on Forresters TechRadar report.

Heres the updated version, which includes 9 more technologies to watch out for this year.

We hope they inspire you to join the 62% of companies boosting their enterprises in 2018.

Natural language generation is an AI sub-discipline that converts data into text, enabling computers to communicate ideas with perfect accuracy.

It is used in customer service to generate reports and market summaries and is offered by companies like Attivio, Automated Insights, Cambridge Semantics, Digital Reasoning, Lucidworks, Narrative Science, SAS, and Yseop.

Siri is just one of the systems that can understand you.

Every day, more and more systems are created that can transcribe human language, reaching hundreds of thousands through voice-response interactive systems and mobile apps.

Companies offering speech recognition services include NICE, Nuance Communications, OpenText and Verint Systems.

A virtual agent is nothing more than a computer agent or program capable of interacting with humans.

The most common example of this kind of technology are chatbots.

Virtual agents are currently being used for customer service and support and as smart home managers.

Some of the companies that provide virtual agents include Amazon, Apple, Artificial Solutions, Assist AI, Creative Virtual, Google, IBM, IPsoft, Microsoft and Satisfi.

These days, computers can also easily learn, and they can be incredibly intelligent!

Machine learning (ML) is a subdiscipline of computer science and a branch of AI. Its goal is to develop techniques that allow computers to learn.

By providing algorithms, APIs (application programming interface), development and training tools, big data, applications and other machines, ML platforms are gaining more and more traction every day.

They are currently mainly being used for prediction and classification.

Some of the companies selling ML platforms include Amazon, Fractal Analytics, Google, H2O.ai, Microsoft, SAS, Skytree and Adext.

This last one is particularly interesting for one simple reason: Adext AI is the first and only audience management tool in the world that applies real AI and machine learning to digital advertising to find the most profitable audience or demographic group for any ad. You can learn more about it here.

AI technology makes hardware much friendlier.

How?

Through new graphic and central processing units and processing devices specifically designed and structured to execute AI-oriented tasks.

And if you havent seen them already, expect the imminent appearance and wide acceptance of AI-optimized silicon chips that can be inserted right into your portable devices and elsewhere.

You can get access to this technology through Alluviate, Cray, Google, IBM, Intel, and Nvidia.

Intelligent machines are capable of introducing rules and logic to AI systems so you can use them for initial setup/training, ongoing maintenance, and tuning.

Decision management has already been incorporated into a variety of corporate applications to assist and execute automated decision, making your business as profitable as possible.

Check out Advanced Systems Concepts, Informatica, Maana, Pegasystems, and UiPath for additional options.

Deep learning platforms use a unique form of ML that involves artificial neural circuits with various abstraction layers that can mimic the human brain, processing data and creating patterns for decision making.

It is currently mainly being used to recognize patterns and classify applications that are only compatible with large-scale data sets.

Deep Instinct, Ersatz Labs, Fluid AI, MathWorks, Peltarion, Saffron Technology and Sentient Technologies all have deep learning options worthy of exploring.

This technology can identify, measure and analyze human behavior and physical aspects of the bodys structure and form.

It allows for more natural interactions between humans and machines, including interactions related to touch, image, speech and body language recognition, and is big within the market research field.

3VR, Affectiva, Agnitio, FaceFirst, Sensory, Synqera and Tahzoo are all biometrics companies working hard to develop this area.

Robotic processes automation uses scripts and methods that mimic and automate human tasks to support corporate processes.

It is particularly useful for situations when hiring humans for a specific job or task is too expensive or inefficient.

The good example is Adext AI, a platform that automates digital advertising processes using AI, saving businesses from devoting hours to mechanical and repetitive tasks.

Its a solution that lets you make the most of your human talent and move employees into more strategic and creative positions, so their actions can really make an impact on the company's growth.

Advanced Systems Concepts, Automation Anywhere, Blue Prism, UiPath, and WorkFusion are other examples of robotic processes automation companies.

This technology uses text analytics to understand the structure of sentences, as well as their meaning and intention, through statistical methods and ML.

Text analytics and NLP are currently being used for security systems and fraud detection.

They are also being used by a vast array of automated assistants and apps to extract unstructured data.

Some of the service providers and suppliers of these technologies include Basis Technology, Coveo, Expert System, Indico, Knime, Lexalytics, Linguamatics, Mindbreeze, Sinequa, Stratifyd, and Synapsify.

A digital twin is a software construct that bridges the gap between physical systems and the digital world.

General Electric (GE), for example, is building an AI workforce to monitor its aircraft engines, locomotives and gas turbines and predict failures with cloud-hosted software models of GEs machines. Their digital twins are mainly lines of software code, but the most elaborate versions look like 3-D computer-aided design drawings full of interactive charts, diagrams, and data points.

Companies using digital twin and AI modeling technologies include VEERUM, in the capital project delivery space; Akselos, which is using it to protect critical infrastructure, and Supply Dynamics, which has developed a SaaS solution to manage raw material sourcing in complex, highly distributed manufacturing environments.

Cyber defense is a computer network defense mechanism that focuses on preventing, detecting and providing timely responses to attacks or threats to infrastructure and information.

AI and ML are now being used to move cyberdefense into a new evolutionary phase in response to an increasingly hostile environment: Breach Level Index detected a total of over 2 billion breached records during 2017. Seventy-six percent of the records in the survey were lost accidentally, and 69% were an identity theft type of breach.

Recurrent neural networks, which are capable of processing sequences of inputs, can be used in combination with ML techniques to create supervised learning technologies, which uncover suspicious user activity and detect up to 85% of all cyber attacks.

Startups such as Darktrace, which pairs behavioral analytics with advanced mathematics to automatically detect abnormal behavior within organizations and Cylance, which applies AI algorithms to stop malware and mitigate damage from zero-day attacks, are both working in the area of AI-powered cyber defense.

DeepInstinct, another cyber defense company, is a deep learning project named Most Disruptive Startup by Nvidias Silicon Valley ceremony, protects enterprises' endpoints, servers, and mobile devices.

Compliance is the certification or confirmation that a person or organization meets the requirements of accepted practices, legislation, rules and regulations, standards or the terms of a contract, and there is a significant industry that upholds it.

We are now seeing the first wave of regulatory compliance solutions that use AI to deliver efficiency through automation and comprehensive risk coverage.

Some examples of AIs use in compliance are showing up across the world. For example, NLP (Natural Language Processing) solutions can scan regulatory text and match its patterns with a cluster of keywords to identify the changes that are relevant to an organization.

Capital stress testing solutions with predictive analytics and scenario builders can help organizations stay compliant with regulatory capital requirements. And the volume of transaction activities flagged as potential examples of money laundering can be reduced as deep learning is used to apply increasingly sophisticated business rules to each one.

Companies working in this area include Compliance.ai, a Retch company that matches regulatory documents to a corresponding business function; Merlon Intelligence, a global compliance technology company that supports the financial services industry to combat financial crimes, and Socure, whose patented predictive analytics platform boosts customer acceptance rates while reducing fraud and manual reviews.

While some are rightfully concerned about AI replacing people in the workplace, lets not forget that AI technology also has the potential to vastly help employees in their work, especially those in knowledge work.

In fact, the automation of knowledge work has been listed as the #2 most disruptive emerging tech trend.

The medical and legal professions, which are heavily reliant on knowledge workers, is where workers will increasingly use AI as a diagnostic tool.

There is an increasing number of companies working on technologies in this area. Kim Technologies, whose aim is to empower knowledge workers who have little to no IT programming experience with the tools to create new workflow and document processes with the help of AI, is one of them. Kyndi is another, whose platform is designed to help knowledge workers process vast amounts of information.

Content creation now includes any material people contribute to the online world, such as videos, ads, blog posts, white papers, infographics and other visual or written assets.

Brands like USA Today, Hearst and CBS, are already using AI to generate their content.

Wibbitz, a SaaS tool that helps publishers create videos from written content in minutes with AI video production technology, is a great example of a solution from this field. Wordsmith is another tool, created by Automated Insights, that applies NLP (Natural Language Processing) to generate news stories based on earnings data.

Peer-to-peer networks, in their purest form, are created when two or more PCs connect and share resources without the data going through a server computer.

But peer-to-peer networks are also used by cryptocurrencies, and have the potential to even solve some of the worlds most challenging problems, by collecting and analyzing large amounts of data, says Ben Hartman, CEO of Bet Capital LLC, to Entrepreneur.

Nano Vision, a startup that rewards users with cryptocurrency for their molecular data, aims to change the way we approach threats to human health, such as superbugs, infectious diseases, and cancer, among others.

Another player utilizing peer-to-peer networks and AI is Presearch, a decentralized search engine thats powered by the community and rewards members with tokens for a more transparent search system.

This technology allows software to read the emotions on a human face using advanced image processing or audio data processing. We are now at the point where we can capture micro-expressions, or subtle body language cues, and vocal intonation that betrays a persons feelings.

Law enforcers can use this technology to try to detect more information about someone during interrogation. But it also has a wide range of applications for marketers.

There are increasing numbers of startups working in this area. Beyond Verbal analyzes audio inputs to describe a persons character traits, including how positive, how excited, angry or moody they are. nViso uses emotion video analytics to inspire new product ideas, identify upgrades and enhance the consumer experience. And Affectivas Emotion AI is used in the gaming, automotive, robotics, education, healthcare industries, and other fields, to apply facial coding and emotion analytics from face and voice data.

Image recognition is the process of identifying and detecting an object or feature in a digital image or video, and AI is increasingly being stacked on top of this technology to great effect.

AI can search social media platforms for photos and compare them to a wide range of data sets to decide which ones are most relevant during image searches.

Image recognition technology can also be used to detect license plates, diagnose disease, analyze clients and their opinions and verify users based on their face.

Clarifai provides image recognition systems for customers to detect near-duplicates and find similar uncategorized images.

SenseTime is one of the leaders in this industry and develops face recognition technology that can be applied to payment and picture analysis for bank card verification and other applications. And GumGums mission is to unlock the value of images and videos produced across the web using AI technology.

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19 Artificial Intelligence Technologies That Will Dominate In 2018

2017 saw an explosion of machine learning in production use, with even deep learning and artificial intelligence (AI) being leveraged for practical applications.

"Basic analytics are out; machine learning (and beyond) are in," says Kenneth Sanford, U.S. lead analytics architect for collaborative data science platform Dataiku, as he looks back on 2017.

Sanford says practical applications of machine learning, deep learning, and AI are "everywhere and out in the open these days," pointing to the "super billboards" in London's Piccadilly Circus that leverage hidden cameras gathering data on foot and road traffic (including the make and model of passing cars) to deliver targeted advertisements.

So where will these frameworks and tools take us in 2018? We spoke with a number of IT leaders and industry experts about what to expect in the coming year.

AI is already here, whether we recognize it or not.

"Many organizations are using AI already, but they may not refer to it as 'AI,'" says Scott Gnau, CTO of Hortonworks. "For example, any organization using a chatbot feature to engage with customers is using artificial intelligence."

But many of the deployments leveraging AI technologies and tools have been small-scale. Expect organizations to ramp up in a big way in 2018.

"Enterprises have spent the past few years educating themselves on various AI frameworks and tools," says Nima Negahban, CTO and co-founder of Kinetica, a specialist in GPU-accelerated databases for high-performance analytics. "But as AI goes mainstream, it will move beyond small-scale experiments to being automated and operationalized. As enterprises move forward with operationalizing AI, they will look for products and tools to automate, manage, and streamline the entire machine learning and deep learning life cycle."

Negahban predicts 2018 will see an increase in investments in AI life cycle management, and technologies that house the data and supervise the process will mature.

Ramon Chen, chief product officer of master data management specialist Reltio, is less sanguine. Chen says there have been repeated predictions for several years that tout potential breakthroughs in the use of AI and machine learning, but the reality is that most enterprises have yet to see quantifiable benefits from their investments in these areas.

Chen says the hype to date has been overblown, and most enterprises are reluctant to get started due to a combination of skepticism, lack of expertise, and most important of all, a lack of confidence in the reliability of their data sets.

"In fact, while the headlines will be mostly about AI, most enterprises will need to first focus on IA (information augmentation): getting their data organized in a manner that ensures it can be reconciled, refined, and related, to uncover relevant insights that support efficient business execution across all departments, while addressing the burden of regulatory compliance," Chen says.

Chad Meley, vice president of marketing at Teradata, agrees that 2018 will see a backlash against AI hype, but believes a more balanced approach of deep learning and shallow learning application to business opportunities will emerge as a result.

While there may be a backlash against the hype, it won't stop large enterprises from investing in AI and related technologies.

"AI is the new big data: Companies race to do it whether they know they need it or not," says Monte Zweben, CEO of Splice Machine.

Meley points to Teradata's recently released 2017 State of Artificial Intelligence for Enterprises report, which identified a lack of IT infrastructure as the greatest barrier to realizing benefits from AI, surpassing issues like access to talent, lack of budget, and weak or unknown business cases.

"Companies will respond in 2018 with enterprise-grade AI product and supporting offerings that overcome the growing pains associated with AI adoption," Meley says.

Reltio's Chen isn't alone in his conviction that enterprises need to get their data in order. Tomer Shiran, CEO and co-founder of analytics startup Dremio, a driving force behind the open source Apache Arrow project, believes a debate about data sets will take center stage in 2018.

"Everywhere you turn, companies are adding AI to their products to make them smarter, more efficient, and even autonomous," Shiran says. "In 2017, we heard competing arguments for whether AI would create jobs or eliminate them, with some even proposing the end of the human race. What has started to emerge as a key part of the conversation is how training data sets shape the behavior of these models."

It turns out, Shiran says, that models are only as good as the training data they use, and developing a representative, effective training data set is very challenging.

"As a trivial example, consider the example tweeted by a Facebook engineer of a soap dispenser that works for white people but not those with darker skin," Shiran says. "Humans are hopelessly biased, and the question for AI will become whether we can do better in terms of bias or will we do worse. This debate will center around data ownership what data we own about ourselves, and the companies like Google, Facebook, Amazon, Uber, etc. who have amassed enormous data sets that will feed our models."

One of the big barriers to the adoption of AI, particularly in regulated industries, is the difficulty in showing exactly how an AI reached a decision. Kinetica's Negahban says creating AI audit trails will be essential.

"AI is increasingly getting used for applications like drug discovery or the connected car, and these applications can have a detrimental impact on human life if an incorrect decision is made," Negahban says. "Detecting exactly what caused the final incorrect decision leading to a serious problem is something enterprises will start to look at in 2018. Auditing and tracking every input and every score that a framework produces will help with detecting the human-written code that ultimately caused the problem."

Horia Margarit, principal data scientist for big-data-as-a-service provider Qubole, agrees that enterprises in 2018 will seek to improve their infrastructure and processes for supporting their machine learning and AI efforts.

"As companies look to innovate and improve with machine learning and artificial intelligence, more specialized tooling and infrastructure will be adopted in the cloud to support specific use cases, like solutions for merging multi-modal sensory inputs for human interaction (think sound, touch, and vision) or solutions for merging satellite imagery with financial data to catapult algorithmic trading capabilities," Margarit says.

"We expect to see an explosion in cloud-based solutions that accelerate the current pace of data collection and further demonstrate the need for frictionless, on-demand compute and storage from managed cloud providers," he adds.

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5 artificial intelligence trends that will dominate 2018 | CIO

In 2017, we published a popular post on artificial intelligence (AI) technologies that would dominate that year, based on Forresters TechRadar report.

Heres the updated version, which includes 9 more technologies to watch out for this year.

We hope they inspire you to join the 62% of companies boosting their enterprises in 2018.

Natural language generation is an AI sub-discipline that converts data into text, enabling computers to communicate ideas with perfect accuracy.

It is used in customer service to generate reports and market summaries and is offered by companies like Attivio, Automated Insights, Cambridge Semantics, Digital Reasoning, Lucidworks, Narrative Science, SAS, and Yseop.

Siri is just one of the systems that can understand you.

Every day, more and more systems are created that can transcribe human language, reaching hundreds of thousands through voice-response interactive systems and mobile apps.

Companies offering speech recognition services include NICE, Nuance Communications, OpenText and Verint Systems.

A virtual agent is nothing more than a computer agent or program capable of interacting with humans.

The most common example of this kind of technology are chatbots.

Virtual agents are currently being used for customer service and support and as smart home managers.

Some of the companies that provide virtual agents include Amazon, Apple, Artificial Solutions, Assist AI, Creative Virtual, Google, IBM, IPsoft, Microsoft and Satisfi.

These days, computers can also easily learn, and they can be incredibly intelligent!

Machine learning (ML) is a subdiscipline of computer science and a branch of AI. Its goal is to develop techniques that allow computers to learn.

By providing algorithms, APIs (application programming interface), development and training tools, big data, applications and other machines, ML platforms are gaining more and more traction every day.

They are currently mainly being used for prediction and classification.

Some of the companies selling ML platforms include Amazon, Fractal Analytics, Google, H2O.ai, Microsoft, SAS, Skytree and Adext.

This last one is particularly interesting for one simple reason: Adext AI is the first and only audience management tool in the world that applies real AI and machine learning to digital advertising to find the most profitable audience or demographic group for any ad. You can learn more about it here.

AI technology makes hardware much friendlier.

How?

Through new graphic and central processing units and processing devices specifically designed and structured to execute AI-oriented tasks.

And if you havent seen them already, expect the imminent appearance and wide acceptance of AI-optimized silicon chips that can be inserted right into your portable devices and elsewhere.

You can get access to this technology through Alluviate, Cray, Google, IBM, Intel, and Nvidia.

Intelligent machines are capable of introducing rules and logic to AI systems so you can use them for initial setup/training, ongoing maintenance, and tuning.

Decision management has already been incorporated into a variety of corporate applications to assist and execute automated decision, making your business as profitable as possible.

Check out Advanced Systems Concepts, Informatica, Maana, Pegasystems, and UiPath for additional options.

Deep learning platforms use a unique form of ML that involves artificial neural circuits with various abstraction layers that can mimic the human brain, processing data and creating patterns for decision making.

It is currently mainly being used to recognize patterns and classify applications that are only compatible with large-scale data sets.

Deep Instinct, Ersatz Labs, Fluid AI, MathWorks, Peltarion, Saffron Technology and Sentient Technologies all have deep learning options worthy of exploring.

This technology can identify, measure and analyze human behavior and physical aspects of the bodys structure and form.

It allows for more natural interactions between humans and machines, including interactions related to touch, image, speech and body language recognition, and is big within the market research field.

3VR, Affectiva, Agnitio, FaceFirst, Sensory, Synqera and Tahzoo are all biometrics companies working hard to develop this area.

Robotic processes automation uses scripts and methods that mimic and automate human tasks to support corporate processes.

It is particularly useful for situations when hiring humans for a specific job or task is too expensive or inefficient.

The good example is Adext AI, a platform that automates digital advertising processes using AI, saving businesses from devoting hours to mechanical and repetitive tasks.

Its a solution that lets you make the most of your human talent and move employees into more strategic and creative positions, so their actions can really make an impact on the company's growth.

Advanced Systems Concepts, Automation Anywhere, Blue Prism, UiPath, and WorkFusion are other examples of robotic processes automation companies.

This technology uses text analytics to understand the structure of sentences, as well as their meaning and intention, through statistical methods and ML.

Text analytics and NLP are currently being used for security systems and fraud detection.

They are also being used by a vast array of automated assistants and apps to extract unstructured data.

Some of the service providers and suppliers of these technologies include Basis Technology, Coveo, Expert System, Indico, Knime, Lexalytics, Linguamatics, Mindbreeze, Sinequa, Stratifyd, and Synapsify.

A digital twin is a software construct that bridges the gap between physical systems and the digital world.

General Electric (GE), for example, is building an AI workforce to monitor its aircraft engines, locomotives and gas turbines and predict failures with cloud-hosted software models of GEs machines. Their digital twins are mainly lines of software code, but the most elaborate versions look like 3-D computer-aided design drawings full of interactive charts, diagrams, and data points.

Companies using digital twin and AI modeling technologies include VEERUM, in the capital project delivery space; Akselos, which is using it to protect critical infrastructure, and Supply Dynamics, which has developed a SaaS solution to manage raw material sourcing in complex, highly distributed manufacturing environments.

Cyber defense is a computer network defense mechanism that focuses on preventing, detecting and providing timely responses to attacks or threats to infrastructure and information.

AI and ML are now being used to move cyberdefense into a new evolutionary phase in response to an increasingly hostile environment: Breach Level Index detected a total of over 2 billion breached records during 2017. Seventy-six percent of the records in the survey were lost accidentally, and 69% were an identity theft type of breach.

Recurrent neural networks, which are capable of processing sequences of inputs, can be used in combination with ML techniques to create supervised learning technologies, which uncover suspicious user activity and detect up to 85% of all cyber attacks.

Startups such as Darktrace, which pairs behavioral analytics with advanced mathematics to automatically detect abnormal behavior within organizations and Cylance, which applies AI algorithms to stop malware and mitigate damage from zero-day attacks, are both working in the area of AI-powered cyber defense.

DeepInstinct, another cyber defense company, is a deep learning project named Most Disruptive Startup by Nvidias Silicon Valley ceremony, protects enterprises' endpoints, servers, and mobile devices.

Compliance is the certification or confirmation that a person or organization meets the requirements of accepted practices, legislation, rules and regulations, standards or the terms of a contract, and there is a significant industry that upholds it.

We are now seeing the first wave of regulatory compliance solutions that use AI to deliver efficiency through automation and comprehensive risk coverage.

Some examples of AIs use in compliance are showing up across the world. For example, NLP (Natural Language Processing) solutions can scan regulatory text and match its patterns with a cluster of keywords to identify the changes that are relevant to an organization.

Capital stress testing solutions with predictive analytics and scenario builders can help organizations stay compliant with regulatory capital requirements. And the volume of transaction activities flagged as potential examples of money laundering can be reduced as deep learning is used to apply increasingly sophisticated business rules to each one.

Companies working in this area include Compliance.ai, a Retch company that matches regulatory documents to a corresponding business function; Merlon Intelligence, a global compliance technology company that supports the financial services industry to combat financial crimes, and Socure, whose patented predictive analytics platform boosts customer acceptance rates while reducing fraud and manual reviews.

While some are rightfully concerned about AI replacing people in the workplace, lets not forget that AI technology also has the potential to vastly help employees in their work, especially those in knowledge work.

In fact, the automation of knowledge work has been listed as the #2 most disruptive emerging tech trend.

The medical and legal professions, which are heavily reliant on knowledge workers, is where workers will increasingly use AI as a diagnostic tool.

There is an increasing number of companies working on technologies in this area. Kim Technologies, whose aim is to empower knowledge workers who have little to no IT programming experience with the tools to create new workflow and document processes with the help of AI, is one of them. Kyndi is another, whose platform is designed to help knowledge workers process vast amounts of information.

Content creation now includes any material people contribute to the online world, such as videos, ads, blog posts, white papers, infographics and other visual or written assets.

Brands like USA Today, Hearst and CBS, are already using AI to generate their content.

Wibbitz, a SaaS tool that helps publishers create videos from written content in minutes with AI video production technology, is a great example of a solution from this field. Wordsmith is another tool, created by Automated Insights, that applies NLP (Natural Language Processing) to generate news stories based on earnings data.

Peer-to-peer networks, in their purest form, are created when two or more PCs connect and share resources without the data going through a server computer.

But peer-to-peer networks are also used by cryptocurrencies, and have the potential to even solve some of the worlds most challenging problems, by collecting and analyzing large amounts of data, says Ben Hartman, CEO of Bet Capital LLC, to Entrepreneur.

Nano Vision, a startup that rewards users with cryptocurrency for their molecular data, aims to change the way we approach threats to human health, such as superbugs, infectious diseases, and cancer, among others.

Another player utilizing peer-to-peer networks and AI is Presearch, a decentralized search engine thats powered by the community and rewards members with tokens for a more transparent search system.

This technology allows software to read the emotions on a human face using advanced image processing or audio data processing. We are now at the point where we can capture micro-expressions, or subtle body language cues, and vocal intonation that betrays a persons feelings.

Law enforcers can use this technology to try to detect more information about someone during interrogation. But it also has a wide range of applications for marketers.

There are increasing numbers of startups working in this area. Beyond Verbal analyzes audio inputs to describe a persons character traits, including how positive, how excited, angry or moody they are. nViso uses emotion video analytics to inspire new product ideas, identify upgrades and enhance the consumer experience. And Affectivas Emotion AI is used in the gaming, automotive, robotics, education, healthcare industries, and other fields, to apply facial coding and emotion analytics from face and voice data.

Image recognition is the process of identifying and detecting an object or feature in a digital image or video, and AI is increasingly being stacked on top of this technology to great effect.

AI can search social media platforms for photos and compare them to a wide range of data sets to decide which ones are most relevant during image searches.

Image recognition technology can also be used to detect license plates, diagnose disease, analyze clients and their opinions and verify users based on their face.

Clarifai provides image recognition systems for customers to detect near-duplicates and find similar uncategorized images.

SenseTime is one of the leaders in this industry and develops face recognition technology that can be applied to payment and picture analysis for bank card verification and other applications. And GumGums mission is to unlock the value of images and videos produced across the web using AI technology.

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19 Artificial Intelligence Technologies That Will Dominate In ...

Siri and Alexa are going to become a lot more useful to you in the near future.

November27, 20185 min read

Opinions expressed by Entrepreneur contributors are their own.

Consumers have been skittish about the notion of artificial intelligence (AI) invading their homes:What if robots take over the world?Whos spying on me? Who'slistening to my conversations? But now those same consumers are starting to embracethe new technology in their personal lives and businesses.

Related:10 Artificial Intelligence Trends to Watch in 2018

This in turn has raised the confidence of companies interested in the technology -- so much so that companies have tripled their AI investment since 2013, according to a survey by McKinsey & Company.

AI, in fact, has the potential to completely change the way companies do business; and because of technological developments, more companies, in 2019, will be able to access and implement this life-changing technology. Already, companies like Amazon, Microsoft and Google are leading the way.

So, given those expected future advances for the technology, check out four AI trends to watch for in 2019.

Consumers have been benefiting from having AI assistants in their homes for some time now with the introduction of Apples Siri, Amazons Alexa and other devices. You can ask AI assistants to play you a song, tell you the weather, search out information online, turn off your house lights and much more.

Consumers have been embracing this new AI-powered technology. In fact, in a study from Adobe Analytics, 71 percent of smart-speaker owners reported using them at least daily, while 44 percent said they used them multiple times a day. So, in 2019, expect to see even more advanced AI assistants in your home, at work and in other areas of your life.

As of now, what consumers ask AI assistants to do is pretty basic, like searching for and playing a particular song. But expect to see big changes in the tasks AI assistants canperform in the near future. AI assistants will soon be able to provide even more individualized experiences as they get better at recognizing different users' voices.

Instead of just speaking to your AI home device or your mobile phone, I predict youll soon be able to speak to your car, TV, refrigerator -- even your lamps.

Related:The Impact of Artificial Intelligence on 2018's Top HR Trends

According to a survey from Indeed, 42 percent of employers polled were worried that they wouldn't be able to find the talent they needed. For many businesses, the recruiting process is one of their most time-consuming and stressful tasks, but with advancements in artificial intelligence, AI-powered recruiting tools will be a recruitment trend to watch for in 2019.

For example, Mya, which stands for My Recruiting Assistant,is a chatbot recruiting assistant. It can communicate with candidates via Skype, emailor text. It can pre-qualify candidates for you and even reject a candidate if you decide to pass on his or her application.

Image credit: Hiremya.com

Along with AI-powered screening and candidate-communication tools, a number of emerging artificial intelligence toolsare emerging that will help employers save even more time and find the candidates they need next year.

Because users will soon start using AI-powered assistants in new ways and more often, advanced conversational AI-powered search will be a huge trend. With the introduction of voice search, the way in which consumers search online has changed. Instead of typing in a search query like condos for sale Dallas,consumers will be able to speak their search queries using a more conversational phrase, like, What condos are for sale in Dallas for under $150,000?

In other words, the way users are provided answers to their queries will become more advanced as well.

Continuing with the condo example, AI-powered search engines will do more than just providing users with a number of listings; they'll also receive more conversational answers. Search engines could follow up with questions to provide more detailed solutions by asking, say:

How many bedrooms do you want?

What neighborhood would you prefer to live in?

Would you prefer a gym and pool on the premises?

Users will then be able to narrow down the solutions and get exactly the search results theyre looking for. Since consumers are changing the way they search, the quality of the results they expect to get is changing, as well, pushing AI to keep up with those expectations next year.

Chatbots have been improving customer service for businesses of all types in recent years; you can even order a pizza through a Facebook chatbot now. In 2019, expect chatbots to become even more advanced and human than before. With natural language programming, you no longer have to have a robotic conversation: Consumers can speak to chatbots just as they would a live chat agent. Beyond simple chatbots, more companies will also be implementing life-like animated virtual agents, too.

Autodesk recently unveiled its virtual agent,Ava. Ava is a digital human who can answer customers' questions, direct them to content and help them check out, as well as respond interactively to emotional signals from those users.

Image credit: Ava digital human image on YouTube

More and more retailers and businesses will be using conversational chatbots and virtual agents to solve customer service issues without having to pass users off to a real-life staffer.

Related:5 Tech Trends Content Creators Need to Pay Attention To

So, now you have four useful and exciting AI trends to look forward to in the new year. As AI advances, your businesswill be able to take advantage of this technology to not only give you more convenience in your personal life, but help you run a more efficient and profitable business.

See the article here:

4 Artificial Intelligence Trends to Watch for in 2019

Last week, Ben Goertzel and his company, Aidyia, turned on a hedge fund that makes all stock trades using artificial intelligenceno human intervention required. "If we all die," says Goertzel, a longtime AI guru and the company's chief scientist, "it would keep trading."

He means this literally. Goertzel and other humans built the system, of course, and they'll continue to modify it as needed. But their creation identifies and executes trades entirely on its own, drawing on multiple forms of AI, including one inspired by genetic evolution and another based on probabilistic logic. Each day, after analyzing everything from market prices and volumes to macroeconomic data and corporate accounting documents, these AI engines make their own market predictions and then "vote" on the best course of action.

If we all die, it would keep trading.

Ben Goertzel, Aidyia

Though Aidyia is based in Hong Kong, this automated system trades in US equities, and on its first day, according to Goertzel, it generated a 2 percent return on an undisclosed pool of money. That's not exactly impressive, or statistically relevant. But it represents a notable shift in the world of finance. Backed by $143 million in funding, San Francisco startup Sentient Technologies has been quietly trading with a similar system since last year. Data-centric hedge funds like Two Sigma and Renaissance Technologies have said they rely on AI. And according to reports, two othersBridgewater Associates and Point72 Asset Management, run by big Wall Street names Ray Dalio and Steven A. Cohenare moving in the same direction.

Hedge funds have long relied on computers to help make trades. According to market research firm Preqin, some 1,360 hedge funds make a majority of their trades with help from computer modelsroughly 9 percent of all fundsand they manage about $197 billion in total. But this typically involves data scientistsor "quants," in Wall Street lingousing machines to build large statistical models. These models are complex, but they're also somewhat static. As the market changes, they may not work as well as they worked in the past. And according to Preqin's research, the typical systematic fund doesn't always perform as well as funds operated by human managers (see chart below)

Preqin/WIRED

In recent years, however, funds have moved toward true machine learning, where artificially intelligent systems can analyze large amounts of data at speed and improve themselves through such analysis. The New York company Rebellion Research, founded by the grandson of baseball Hall of Famer Hank Greenberg, among others, relies upon a form of machine learning called Bayesian networks, using a handful of machines to predict market trends and pinpoint particular trades. Meanwhile, outfits such as Aidyia and Sentient are leaning on AI that runs across hundreds or even thousands of machines. This includes techniques such as evolutionary computation, which is inspired by genetics, and deep learning, a technology now used to recognize images, identify spoken words, and perform other tasks inside Internet companies like Google and Microsoft.

The hope is that such systems can automatically recognize changes in the market and adapt in ways that quant models can't. "They're trying to see things before they develop," says Ben Carlson, the author of A Wealth of Common Sense: Why Simplicity Trumps Complexity in Any Investment Plan, who spent a decade with an endowment fund that invested in a wide range of money managers.

This kind of AI-driven fund management shouldn't be confused with high-frequency trading. It isn't looking to front-run trades or otherwise make money from speed of action. It's looking for the best trades in the longer termhours, days, weeks, even months into the future. And more to the point, machinesnot humansare choosing the strategy.

Though the company has not openly marketed its fund, Sentient CEO Antoine Blondeau says it has been making official trades since last year using money from private investors (after a longer period of test trades). According to a report from Bloomberg, the company has worked with the hedge fund business inside JP Morgan Chase in developing AI trading technology, but Blondeau declines to discuss its partnerships. He does say, however, that its fund operates entirely through artificial intelligence.

The whole idea is to do something no other humanand no other machineis doing.

The system allows the company to adjust certain risk settings, says chief science officer Babak Hodjat, who was part of the team that built Siri before the digital assistant was acquired by Apple. But otherwise, it operates without human help. "It automatically authors a strategy, and it gives us commands," Hodjat says. "It says: 'Buy this much now, with this instrument, using this particular order type.' It also tells us when to exit, reduce exposure, and that kind of stuff."

According to Hodjat, the system grabs unused computer power from "millions" of computer processors inside data centers, Internet cafes, and computer gaming centers operated by various companies in Asia and elsewhere. Its software engine, meanwhile, is based on evolutionary computationthe same genetics-inspired technique that plays into Aidyia's system.

In the simplest terms, this means it creates a large and random collection of digital stock traders and tests their performance on historical stock data. After picking the best performers, it then uses their "genes" to create a new set of superior traders. And the process repeats. Eventually, the system homes in on a digital trader that can successfully operate on its own. "Over thousands of generations, trillions and trillions of 'beings' compete and thrive or die," Blondeau says, "and eventually, you get a population of smart traders you can actually deploy."

Though evolutionary computation drives the system today, Hodjat also sees promise in deep learning algorithmsalgorithms that have already proven enormously adept at identify images, recognizing spoken words, and even understanding the natural way we humans speak. Just as deep learning can pinpoint particular features that show up in a photo of a cat, he explains, it could identify particular features of a stock that can make you some money.

Goertzelwho also oversees the OpenCog Foundation, an effort to build an open source framework for general artificial intelligencedisagrees. This is partly because deep learning algorithms have become a commodity. "If everyone is using something, it's predictions will be priced into the market," he says. "You have to be doing something weird." He also points out that, although deep learning is suited to analyzing data defined by a very particular set of patterns, such as photos and words, these kinds of patterns don't necessarily show up in the financial markets. And if they do, they aren't that usefulagain, because anyone can find them.

For Hodjat, however, the task is to improve on today's deep learning. And this may involve combining the technology with evolutionary computation. As he explains it, you could use evolutionary computation to build better deep learning algorithms. This is called neuroevolution. "You can evolve the weights that operate on the deep learner," Hodjat says. "But you can also evolve the architecture of the deep learner itself." Microsoft and other outfits are already building deep learning systems through a kind of natural selection, though they may not be using evolutionary computation per se.

Whatever methods are used, some question whether AI can really succeed on Wall Street. Even if one fund achieves success with AI, the risk is that others will duplicate the system and thus undermine its success. If a large portion of the market behaves in the same way, it changes the market. "I'm a bit skeptical that AI can truly figure this out," Carlson says. "If someone finds a trick that works, not only will other funds latch on to it but other investors will pour money into. It's really hard to envision a situation where it doesn't just get arbitraged away."

Goertzel sees this risk. That's why Aidyia is using not just evolutionary computation but a wide range of technologies. And if others imitate the company's methods, it will embrace other types of machine learning. The whole idea is to do something no other humanand no other machineis doing. "Finance is a domain where you benefit not just from being smart," Goertzel says, "but from being smart in a different way from others."

See the original post here:

The Rise of the Artificially Intelligent Hedge Fund | WIRED

When someone mentions artificial intelligence(AI), what is the first thing that comes to your mind?

Most of us imagine an army of human-like robots rebelling against humanity, while a fewfolks from a more positive thinking communityare envisioning a bright future where AI serves them in all possible ways from walking a dog early in the morning when the weather is unpleasant to peeling tangerines at Christmas.

While the second scenario is certainly fun, it is a utopia per se. A reality with robots rising up against people, however, is a much more probable event.

If you believe AI will soon become the greatest existential threat to humanity, weve got some good news for you. Elon Musk and Stephen Hawking share your point of view. (What a way to boost your self-esteem, right?)

Jokes aside, the debate within the global tech community is not centered on the impact of human-like AIas the general public thinksbut rather on the possibility of ever achieving this technology outright. Professionals are absorbed in discussions about how to define human-like and intelligence. These definitions may seem trivial to outsiders but understanding the human mind and intelligence are, in fact, critical to determining the timeline of milestones for AI. Experts are still not certain how this kind of intelligence will manifest or how soon day X will come, but it is clear that we are moving towards this reality with increasing speed.

This means it is high time to finally understand what AI is all about.

First things first. Before digging deeper into the topic of AI, lets briefly discuss what artificial intelligence is and how it works.

The term artificial intelligence dates back to 1956 and belongs to a Stanford researcher John McCarthy, who coined the term and defined the key mission of AI as a sub-field of computer science.

Basically, artificial intelligence (AI) is the ability of a machine or a computer program to think and learn. The concept of AI is based on the idea of building machines capable of thinking, acting, and learning like humans.

A more nuanced definition is that artificial Intelligence is an interdisciplinary concept that studies the possibility of creating machines capable of interacting with their environment and acting upon the received data in the a manner considered intelligent.

While some people falsely consider AI a technology, the more accurate approach would be seeing it as a broad concept in which machines are able to deal with tasks in a way we would call intelligent or smart.

There are certain things a machine/computer program must be capable of to be considered AI.

First, it should be able to mimic human thought process and behavior. Second, it should act in a human-like wayintelligent, rational, and ethical.

It is worth mentioning that the AI concept relates both to Weak AI and General AI that has cognitive functions. Stanford has outlined a helpful AI FAQ on these topics.

Not really. Although the two terms are often used interchangeably, they are not the same.

Artificial intelligence is a broader concept, while machine learning is the most common application of AI.

We should understand machine learning as a current application of AI that is focused on development of computer programs that can access data and learn from it automatically, without human assistance or intervention. The entire machine learning concept is based on the assumption that we should give machines access to information and let them learn from it themselves.

Artificial intelligence, in its turn, is a bunch of technologies that include machine learning and some other technologies like natural language processing, inference algorithms, neural networks, etc.

Many people associate AI with the distant future. They incorrectly believe that despite all the buzz around artificial intelligence, the technology is not likely to become a part of their lives anytime soon. Little do they know how many aspects of their lives are already affected by AI.

Take Siri or Alexapersonal assistants that have already become the new normal for thousands of people around the globe. These and similar intelligent gadgets are able to recognize our speech (read: understand what we want or need), analyze the information they have access to, and provide an answer or solution. What is remarkable (and a little scary) about such assistants is that they continuously learn about their users until the point at which they are able to accurately anticipate users needs.

Spotify, Pandora, and Apple Music are some other touching points between AI and you. These services are capable of recommending music based on your interests. These apps monitor the choices you make, insert them into a learning algorithm, and suggest music you are most likely to enjoy. This particular use of AI is probably one of the simplest among all, but it does a good job helping us discover new songs and artists.

AI is making headway in areas you might least expect it. For example, when you come across short news stories on the Associated Press or Yahoo!, chances are good they were written by AI. The current state of artificial intelligence already allows for some basic robot writing. It might be not yet ready to compose in-depth articles or creative stories, but does a pretty good job writing short and simple articles like sport recaps and financial summaries.

Other examples of artificial intelligence in use today include smart home devices like Googles NEST, self-driving cars like those produced by Tesla, and online games like Alien: Isolation.

Here at StopAd, we rely on artificial intelligence, too.

Thanks to the AI weve developed, our ad blocker is able to detect ads just like a human does. This means identifying and blocking ads regardless of their placement, size, type, and format. StopAd is even capable of identifying native advertisingads designed to mimic the structure and layout of the website they appear on. Furthermore, we sometimes use AI to conduct our own investigations.

Some people claim that AI is still in its infancy. Others assure us that we are only a few years away from AI gaining control over humanity. The truth, however, lies somewhere in between.

According to the most trustworthy forecasts out there, AI will outsmart humans at virtually everything in the following 45 years. Obviously, this wont happen overnight. Industries will be falling under AIs spell one-by-one.

Experts predict that within the next decade AI will outperform humans in relatively simple tasks such as translating languages, writing school essays, and driving trucks. More complicated tasks like writing a bestselling book or working as a surgeon, however, will take machines much more time to learn. AI is expected to master these two skills by 2049 and 2053 accordingly.

It is obviously too soon to talk about AI-powered creatures like those from Westworld or Ex Machina stealing our jobs or, worse yet, rising against humanity, but we are certainly moving in that direction. Meanwhile, top tech professionals and scientists are getting increasingly concerned about our future and encourage further research on the potential impact of AI.

It looks like those who understand the full potential of AI are more scared of it than those who only know the basics. A recent scandal between Googles executives and employees may serve as a proof. In April, employees of Google demanded the company to stop working on a so-called Pentagon Project as they were afraid of being involved in the business of war. The project officially known as Project Maven is meant to use AI to make it easier to classify images of people and objects shot by drones. The potential danger is that the life-or-death decisions of what needs to be bombarded and what doesnt will be made without humans involvement.

The military explains that their only intent is to reduce the current workload and minimize the number of tedious tasks performed by humanssomething AI is extremely well-suited for.

Given that lives of people might be at stake, however, can these tasks even be called tedious? And theres another critical question. In a world like this, who will bear the blame of killing innocent people?

It is a widespread point of view that one day not only will AI exceed human performance but it will also extend beyond human control. With so many fearful articles out there, questions like is artificial intelligence safe? or is artificial intelligence bad for people? should come as no surprise. AI is obviously exciting but simultaneously warrants caution.

Given the innate advantage AI machines have over us humans (accuracy, speed, etc.) an AI rebellion scenario is something we should not completely dismiss. Time will show us whether AI is our greatest existential threat or a tech blessing that will improve our quality of life in many different ways.

So far, one thing remains perfectly clear: creating AI is one of the most remarkable events for humankind. After all, AI is considered a major component of 4th Industrial Revolution, and its potential socioeconomic impact is believed to be as huge as the invention of electricity once had.

In light of this, the smartest approach would be keeping an eye on how the technology evolves, taking advantage of the improvements it brings to our lives, and not getting too nervous at the thought of machine takeover.

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What is Artificial Intelligence? AI Basics Explained | StopAd

Carnegie Mellon has led the world in artificial intelligence education and innovation since the field was created. It's only natural, then, that the School of Computer Science would offer the nation's first bachelor's degree in artificial intelligence, which we introduced in fall 2018.

The BSAI program gives you the in-depth knowledge you need to transform large amounts of data into actionable decisions. The program and its curriculum focus on how complex inputs such as vision, language and huge databases can be used to make decisions or enhance human capabilities. The curriculum includes coursework in computer science, math, statistics, computational modeling, machine learning and symbolic computation. Because CMU is devoted to AI for social good, you'll also take courses in ethics and social responsibility, with the option to participate in independent study projects that change the world for the better in areas like healthcare, transportation and education.

Just as AI unites disciplines from machine learning to natural language processing, instruction in the BSAI program includes faculty members from the school's Computer Science Department, Human-Computer Interaction Institute, Institute for Software Research, Language Technologies Institute, Machine Learning Department and Robotics Institute.

When you graduate with a B.S. in AI from SCS, you'll have the computer science savvy and skills our students are known for, with the added expertise in machine learning and automated reasoning that you'll need to build the AI of tomorrow.

See the Curriculum

The BSAI program is reserved for current and future SCS students only, so you need to be accepted into the School of Computer Science first. Once you're at Carnegie Mellon and enrolled in SCS, you can declare a BSAI major in the spring of your first year. Initially, the program will accommodate roughly 100 students total, or about 3035 from each class.

Learn More About Admissions

If you're an SCS student interested in applying for the BSAI program, send us an email.

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B.S. in Artificial Intelligence | Carnegie Mellon School of ...

National technology investment strategies are hard to define let alone pass through complicated legislative bodies, like the US Congress, even when theres a declared war that threatens a country's financial and economic competitiveness.The war for global leadership in artificial intelligence and machine learning is well underway, and the US is poised to lose perhaps the most important technology war in its history.

Is the AI war well-understood?Not even close, at least not by the leaders who develop national strategies or by the citizens of the United States who all need to spend some time on https://willrobotstakemyjob.com.While I searched and searched, I could not find a single political candidate in the recent US mid-term elections who discussed AI, the AI war, or how the US will likely lose the war unless a massive strategic pivot occurs immediately.Since theyre mostly unaware of the war, US leaders have no strategies to prevent an historic loss: imagine the implications of electing politicians who have no idea a deadly war is underway.

The Threat

So whats going on?

AI/machine learning/deep learning (lets call it all AI) are the new digital weapons which, by the way, the US Department of Defense discovered decades ago.While we could certainly examine the importance of AI in global military and economic warfare, no one can argue that AI is unimportant.In fact, its at least a 9 or any imaginable 10-point scale.I give it an easy 10.So do lots of others who research technology trends and technology adoption, especially those who track indicators ofnational success.

The Chinese have a very public, very-deep, extremely well-funded commitment to AI.Air Force General VeraLinn Jamieson says it plainly:"We estimate the total spending on artificial intelligence systems in China in 2017 was $12 billion. We also estimate that it will grow to at least $70 billion by 2020."According to the Obama White House Report in 2016, China publishes more journal articles on deep learning than the US and has increased its number of AI patents by 200%.China is determined to be the world leader in AI by 2030.

Listen to what Tristan Greene writing in TNW concludes about the USs commitment to AI:Unfortunately, despite congressional efforts to get the conversation started at the national level in the US, the White Houses current leadership doesnt appear interested in coming up with a strategy tokeep upwith China. It gets worse:China has allocated billions of dollars towards infrastructure to house hundreds of AI businesses in dedicated industrial parks.It has specific companies, the Chinese counterparts to US operations like Google and Amazon, working on different problems in the field of AI. And itsregulating education so that the nation produces more STEM workers. But perhaps most importantly, China makes it compulsory for businesses and private citizens to share their data with the government something far more valuable than money in the world of AI.

Greenes scary bottom line?Meanwhile, in the US, the Trump administration has shown little interest in discussing its own countrys AI yet,may soon have to talk to Chinas.

More data?According to Iris Deng, China ranks first in the quantity and citation of research papers, and holds the most AI patents, edging out the US and Japan (and) China has not been shy about its ambitions for AI dominance, with the State Council releasing a road map in July 2017 with a goal of creating a domestic industry worth 1 trillion yuan and becoming a global AI powerhouse by 2030.

It's obvious:Without more leadership from Congress and the President, the U.S. is in serious danger of losing the economic and military rewards of artificial intelligence (AI) to China. Thats the somber conclusion of a report published ... by the House Oversight and Reform IT subcommittee.

Jerry Bowles also says it clearly:The U.S. has traditionally led the world in the development and application of AI-driven technologies, due in part to the governments commitment to investing heavily in research and development. That has, in turn, helped support AIs growth and development. In 2015, the United States led the world in total gross domestic R&D expenditures, spending $497 billion.But, since then, neither Congress nor the Trump administration has paid much attention to AI and government R&D investment has been essentially flat.Meanwhile, China has made AI a key part of its formal economic plans for the future.

The Response

The US House of Representatives Subcommittee on Information Technology Committee on Oversight & Government Reform summarizes itbut notdefinitively:

There is a pressing need for conscious, direct, and spirited leadership from the Trump Administration.The 2016 reports put out by the Obama Administrations National Science and Technology Council and the recent actions of the Trump Administration are steps in the right direction. However, given the actions taken by other countries especially China Congress and the Administration will need to increase the time, attention, and level of resources the federal government devotes to AI research and development, as well as push for agencies to further build their capacities for adapting to advanced technologies.

The government has an essential role to play in securing American leadership in AI.Fulfilling this role will require balancing the creative energy of innovative Americans whose knowledge and entrepreneurial spirit have driven the development of this technology with regulatory frameworks that protect consumers. To ensure the appropriate balance is met, it is vital Congress and the Executive Branch continue to educate themselves about AI, increase the expenditures of R&D funds, help set the agenda for public debate, and, where appropriate, define the role of AI in the future of this nation.

Clearly, a coordinated, heavily-funded American response is way overdue.Here are somespecific steps:

These steps represent a good start to turn the tide of the AI war a war the US simply cannot afford to lose.

The rest is here:

Artificial Intelligence, China And The U.S. - How The U.S. Is ...

This book explains how it is possible for computers to reason and perceive, thus introducing the field called artificial intelligence. From the book, you learn why the field is important, both as a branch of engineering and as a science.

If you are a computer scientist or an engineer, you will enjoy the book, because it provides a cornucopia of new ideas for representing knowledge, using knowledge, and building practical systems. If you are a psychologist, biologist, linguist, or philosopher, you will enjoy the book because it provides an exciting computational perspective on the mystery of intelligence.

This completely rewritten and updated edition of Artificial Intelligence reflects the revolutionary progress made since the previous edition was published.

Part I is about representing knowledge and about reasoning methods that make use of knowledge. The material covered includes the semantic-net family of representations, describe and match, generate and test, means-ends analysis, problem reduction, basic search, optimal search, adversarial search, rule chaining, the rete algorithm, frame inheritance, topological sorting, constraint propagation, logic, truth maintenance, planning, and cognitive modeling.

Part II is about learning, the sine qua non of intelligence. Some methods involve much reasoning; others just extract regularity from data. The material covered includes near-miss analysis, explanation-based learning, knowledge repair, case recording, version-space convergence, identification-tree construction, neural-net training, perceptron convergence, approximation-net construction, and simulated evolution.

Part III is about visual perception and language understanding. You learn not only about perception and language, but also about ideas that have been a major source of inspiration for people working in other subfields of artificial intelligence. The material covered includes object identification, stereo vision, shape from shading, a glimpse of modern linguistic theory, and transition-tree methods for building practical natural-language interfaces.

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Artificial Intelligence (3rd Edition): Winston: 9780201533774 ...