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Key point: Questions have risen once again about what happened to one of America's most famous union bosses.

On July 30, 1975, Jimmy Hoffa, the former president of the Teamsters Union, disappeared.

Hed gone to a restaurant in suburban Detroit apparently expecting to meet a couple of mafia figures whom he had known for decades. Hed hoped to win their support for his bid to return to the unions presidency. A few customers remembered seeing him in the restaurant parking lot before 3 p.m.

Sometime after that he vanished without a trace.

The FBI has long assumed that Hoffa was the victim of a mob hit. But despite a decades-long investigation, no one has ever been charged with his murder. His body has never been found.

Yet even though his physical remains are missing, Hoffa lives on in our collective cultural consciousness.

Martin Scorseses The Irishman is only the latest film to offer a fictionalized version of Hoffas story. Before that there was Sylvester Stallones F.I.S.T. (1978), Danny DeVitos Hoffa (1992) and the made-for-TV movie Blood Feud (1983).

Hes been the subject of countless true crime books, most famously Charles Brandts I Heard You Paint Houses. He inspired an episode of The Simpsons. And he crops up in tabloids such as the Weekly World News, which claimed to have found him living in Argentina, hiding from the vengeful Kennedys.

Ever since I started researching and writing on the history of the Teamsters, people have asked me where I think Hoffas body is located. His story, Ive learned, is the one aspect of labor history with which nearly every American is familiar.

Hoffas disappearance transformed him from a controversial union leader into a mythic figure. Over time, Ive come to realize that Hoffas resonance in our culture has important political implications for the labor movement today.

The rise and fall of the Teamsters Teamster

Hoffa became a household name in the late 1950s, when Robert F. Kennedy, then serving as chief counsel for the Senate Rackets Committee, publicly grilled him about his mob ties.

While other witnesses avoided answering questions by invoking their Fifth Amendment rights, Hoffa, the newly elected leader of the nations largest and most powerful union, adopted a defiant stance. He never denied having connections with organized crime figures; instead, he claimed these were the kinds of people he sometimes had to work with as he strengthened and grew his union in the face of employer opposition. He angrily dismissed any allegations of corruption and touted the gains his union had won for its membership.

Story continues

The verbal sparring between Kennedy and Hoffa became the most memorable part of the hearings.

To the benefit of big business, it turned Hoffa into a menacing symbol of labor racketeering.

But to his union members, it only enhanced his standing. They were already thrilled by the contracts Hoffa had negotiated that included better pay and working conditions. Now his members hailed him as their embattled champion and wore buttons proclaiming, Hoffa, the Teamsters Teamster.

His membership stayed loyal even as Hoffa became the target of a series of prosecution efforts.

After becoming attorney general in 1961, Kennedy created a unit within the Department of Justice whose attorneys referred to themselves as the Get Hoffa Squad. Their directive was to target Hoffa and his closest associates. The squads efforts culminated in convictions against Hoffa in 1964 for jury tampering and defrauding the unions pension fund. Despite that setback, Hoffas hold on the Teamsters presidency remained firm even after he entered federal prison in 1967.

When he finally did leave office, Hoffa did so voluntarily. He resigned in 1971 as part of a deal to win executive clemency from the Nixon administration. There was one condition written into the presidents grant of clemency: He couldnt run for a position in the union until 1980.

Once free, Hoffa claimed that his ban from union office was illegitimate and began planning to run for the Teamsters presidency. However, he faced resistance not from the government but from organized crime figures, who had found it easier to work with Hoffas successor, Frank Fitzsimmons.

Hoffas meeting at the restaurant on July 30, 1975, was part of his efforts to allay that opposition.

Clearly, things didnt go as planned.

Some theorize that the mafia had him killed in order to ensure that he would not run against Fitzsimmons in the Teamsters upcoming 1976 union election.

But after no arrests and multiple fruitless excavations to try to locate his body, Hoffas case remains, to this day, unresolved.

From man to myth

In Andrew Lawlers history of the Lost Colony of Roanoke, he writes, To die is tragic, but to go missing is to become a legend, a mystery.

Stories are supposed to have a beginning, a middle and an end. But when people go missing and are never found, Lawler explains, theyll endure as subjects of endless fascination. It allows their legacies to be re-written, over and over.

These new interpretations, Lawler observes, can reveal something fresh about who we were, who we are, and who we want to be.

The myth of Hoffa lives on, even though almost five decades have passed since that afternoon in July 1975.

What shapes has it taken?

To some, he stands for an idealized image of the working class a man whod known hard, manual labor and worked tirelessly to achieve his success. But even after rising to his leadership post, Hoffa lived simply and eschewed pretense.

As a Washington Post article from 1992 put it, He wore white socks, and liked his beef cooked medium well He snored at the opera.

Meanwhile, his feud with the Kennedys pitted a populist tough guy off the loading docks against the professional class, the governing class, the educated experts. The Washington Post piece ties Hoffas story to that of another working-class icon. Watching Hoffa go up against Bobby Kennedy was like watching John Henry go up against a steam hammer it was only a matter of time before he lost.

But Hoffas myth can also serve as a morality tale. The New Republic, for instance, described how Danny DeVitos 1992 film reworks Hoffas life into the story of an embattled champion of the working class who makes a Faustian pact with the underworld.

In the movie, Hoffas Teamsters are caught in hopeless picket line battles with mob goons who the anti-union employers have hired. In order to get those goons to switch sides, Hoffa makes a bargain with mafia leaders. But the mafia ultimately has Hoffa killed when he tries to defy their control, becoming the victim of his own unbridled ambition.

Finally, the underworlds mysterious role in Hoffas death keeps his story compelling for Americans who have a fascination with conspiracy theories. It supports the idea of an invisible cabal that secretly runs everything, and which can make even a famous labor leader disappear without a trace.

Hoffas story is often intertwined with theories about the Kennedy assassination that attribute the presidents murder to an organized crime conspiracy. Both Hoffa and Kennedys murders, in these accounts, highlight the underworlds apparently unlimited power to protect its interests, with tentacles that extend into the government and law enforcement.

Did Hoffa taint the labor movement?

Over two decades after he went missing, a 1997 article in The Los Angeles Times noted that No union in America conjures up more negative images than the Teamsters.

This matters, because for most Americans who lack first-hand knowledge about organized labor, Hoffa is the only labor leaders name they recognize. And as communications scholar William Puette has noted, the Teamsters notoriety is such that for many people in this country the Teamsters Union is the labor movement.

A union widely perceived as mobbed up with a labor leader notorious for his Mafia ties has come, in the minds of some Americans, to represent the entire labor movement. That perception, in turn, bolsters arguments against legislative reforms that would facilitate union organizing efforts.

The other themes in Hoffas myth have similar negative implications for labor. He represents a nostalgic, white, male identity that once existed in a seemingly lost world of manual work. That myth also implies that the unions that emerged in those olden times are no longer necessary.

This depiction doesnt match reality. Todays working class is diverse and employed in a broad spectrum of hard manual labor. Whether youre working as a home health aide or in the gig economy, the need for union protection remains quite real.

But for those working-class Americans who see their society controlled by a hidden cabal of powerful, corrupt forces like the puppet masters who supposedly had JFK and Hoffa killed labor activism can appear quixotic.

For these reasons, the ghost of Jimmy Hoffa continues to haunt the labor movement today.

[ Deep knowledge, daily. Sign up for The Conversations newsletter. ]

David Scott Witwer, Professor of American Studies, Pennsylvania State University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

This article first appeared last month.

Image: Netflix's The Irishman.

Read the original article.

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Does Netflix's The Irishman Reveal What Really Happened To Jimmy Hoffa? - Yahoo News

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Back in Oct. 1950, British techno-visionary Alan Turing published an article called "Computing Machinery and Intelligence," in the journal MIND that raised what at the time must have seemed to many like a science-fiction fantasy.

"May not machines carry out something which ought to be described as thinking but which is very different from what a man does?" Turing asked.

Turing thought that they could. Moreover, he believed, it was possible to create software for a digital computer that enabled it to observe its environment and to learn new things, from playing chess to understanding and speaking a human language. And he thought machines eventually could develop the ability to do that on their own, without human guidance. "We may hope that machines will eventually compete with men in all purely intellectual fields," he predicted.

Nearly 70 years later, Turing's seemingly outlandish vision has become a reality. Artificial intelligence, commonly referred to as AI, gives machines the ability to learn from experience and perform cognitive tasks, the sort of stuff that once only the human brain seemed capable of doing.

AI is rapidly spreading throughout civilization, where it has the promise of doing everything from enabling autonomous vehicles to navigate the streets to making more accurate hurricane forecasts. On an everyday level, AI figures out what ads to show you on the web, and powers those friendly chatbots that pop up when you visit an e-commerce website to answer your questions and provide customer service. And AI-powered personal assistants in voice-activated smart home devices perform myriad tasks, from controlling our TVs and doorbells to answering trivia questions and helping us find our favorite songs.

But we're just getting started with it. As AI technology grows more sophisticated and capable, it's expected to massively boost the world's economy, creating about $13 trillion worth of additional activity by 2030, according to a McKinsey Global Institute forecast.

"AI is still early in adoption, but adoption is accelerating and it is being used across all industries," says Sarah Gates, an analytics platform strategist at SAS, a global software and services firm that focuses upon turning data into intelligence for clients.

It's even more amazing, perhaps, that our existence is quietly being transformed by a technology that many of us barely understand, if at all something so complex that even scientists have a tricky time explaining it.

"AI is a family of technologies that perform tasks that are thought to require intelligence if performed by humans," explains Vasant Honavar, a professor and director of the Artificial Intelligence Research Laboratory at Penn State University, in an email interview. "I say 'thought,' because nobody is really quite sure what intelligence is."

Honavar describes two main categories of intelligence. There's narrow intelligence, which is achieving competence in a narrowly defined domain, such as analyzing images from X-rays and MRI scans in radiology. General intelligence, in contrast, is a more human-like ability to learn about anything and to talk about it. "A machine might be good at some diagnoses in radiology, but if you ask it about baseball, it would be clueless," Honavar explains. Humans' intellectual versatility "is still beyond the reach of AI at this point."

According to Honavar, there are two key pieces to AI. One of them is the engineering part that is, building tools that utilize intelligence in some way. The other is the science of intelligence, or rather, how to enable a machine to come up with a result comparable to what a human brain would come up with, even if the machine achieves it through a very different process. To use an analogy, "birds fly and airplanes fly, but they fly in completely different ways," Honavar. "Even so, they both make use of aerodynamics and physics. In the same way, artificial intelligence is based upon the notion that there are general principles about how intelligent systems behave."

AI is "basically the results of our attempting to understand and emulate the way that the brain works and the application of this to giving brain-like functions to otherwise autonomous systems (e.g., drones, robots and agents)," Kurt Cagle, a writer, data scientist and futurist who's the founder of consulting firm Semantical, writes in an email. He's also editor of The Cagle Report, a daily information technology newsletter.

And while humans don't really think like computers, which utilize circuits, semi-conductors and magnetic media instead of biological cells to store information, there are some intriguing parallels. "One thing we're beginning to discover is that graph networks are really interesting when you start talking about billions of nodes, and the brain is essentially a graph network, albeit one where you can control the strengths of processes by varying the resistance of neurons before a capacitive spark fires," Cagle explains. "A single neuron by itself gives you a very limited amount of information, but fire enough neurons of varying strengths together, and you end up with a pattern that gets fired only in response to certain kinds of stimuli, typically modulated electrical signals through the DSPs [that is digital signal processing] that we call our retina and cochlea."

"Most applications of AI have been in domains with large amounts of data," Honavar says. To use the radiology example again, the existence of large databases of X-rays and MRI scans that have been evaluated by human radiologists, makes it possible to train a machine to emulate that activity.

AI works by combining large amounts of data with intelligent algorithms series of instructions that allow the software to learn from patterns and features of the data, as this SAS primer on artificial intelligence explains.

In simulating the way a brain works, AI utilizes a bunch of different subfields, as the SAS primer notes.

The concept of AI dates back to the 1940s, and the term "artificial intelligence" was introduced at a 1956 conference at Dartmouth College. Over the next two decades, researchers developed programs that played games and did simple pattern recognition and machine learning. Cornell University scientist Frank Rosenblatt developed the Perceptron, the first artificial neural network, which ran on a 5-ton (4.5-metric ton), room-sized IBM computer that was fed punch cards.

But it wasn't until the mid-1980s that a second wave of more complex, multilayer neural networks were developed to tackle higher-level tasks, according to Honavar. In the early 1990s, another breakthrough enabled AI to generalize beyond the training experience.

In the 1990s and 2000s, other technological innovations the web and increasingly powerful computers helped accelerate the development of AI. "With the advent of the web, large amounts of data became available in digital form," Honavar says. "Genome sequencing and other projects started generating massive amounts of data, and advances in computing made it possible to store and access this data. We could train the machines to do more complex tasks. You couldn't have had a deep learning model 30 years ago, because you didn't have the data and the computing power."

AI is different from, but related to, robotics, in which machines sense their environment, perform calculations and do physical tasks either by themselves or under the direction of people, from factory work and cooking to landing on other planets. Honavar says that the two fields intersect in many ways.

"You can imagine robotics without much intelligence, purely mechanical devices like automated looms," Honavar says. "There are examples of robots that are not intelligent in a significant way." Conversely, there's robotics where intelligence is an integral part, such as guiding an autonomous vehicle around streets full of human-driven cars and pedestrians.

"It's a reasonable argument that to realize general intelligence, you would need robotics to some degree, because interaction with the world, to some degree, is an important part of intelligence," according to Honavar. "To understand what it means to throw a ball, you have to be able to throw a ball."

AI quietly has become so ubiquitous that it's already found in many consumer products.

"A huge number of devices that fall within the Internet of Things (IoT) space readily use some kind of self-reinforcing AI, albeit very specialized AI," Cagle says. "Cruise control was an early AI and is far more sophisticated when it works than most people realize. Noise dampening headphones. Anything that has a speech recognition capability, such as most contemporary television remotes. Social media filters. Spam filters. If you expand AI to cover machine learning, this would also include spell checkers, text-recommendation systems, really any recommendation system, washers and dryers, microwaves, dishwashers, really most home electronics produced after 2017, speakers, televisions, anti-lock braking systems, any electric vehicle, modern CCTV cameras. Most games use AI networks at many different levels."

AI already can outperform humans in some narrow domains, just as "airplanes can fly longer distances, and carry more people than a bird could," Honavar says. AI, for example, is capable of processing millions of social media network interactions and gaining insights that can influence users' behavior an ability that the AI expert worries may have "not so good consequences."

It's particularly good at making sense of massive amounts of information that would overwhelm a human brain. That capability enables internet companies, for example, to analyze the mountains of data that they collect about users and employ the insights in various ways to influence our behavior.

But AI hasn't made as much progress so far in replicating human creativity, Honavar notes, though the technology already is being utilized to compose music and write news articles based on data from financial reports and election returns.

Given AI's potential to do tasks that used to require humans, it's easy to fear that its spread could put most of us out of work. But some experts envision that while the combination of AI and robotics could eliminate some positions, it will create even more new jobs for tech-savvy workers.

"Those most at risk are those doing routine and repetitive tasks in retail, finance and manufacturing," Darrell West, a vice president and founding director of the Center for Technology Innovation at the Brookings Institution, a Washington-based public policy organization, explains in an email. "But white-collar jobs in health care will also be affected and there will be an increase in job churn with people moving more frequently from job to job. New jobs will be created but many people will not have the skills needed for those positions. So the risk is a job mismatch that leaves people behind in the transition to a digital economy. Countries will have to invest more money in job retraining and workforce development as technology spreads. There will need to be lifelong learning so that people regularly can upgrade their job skills."

And instead of replacing human workers, AI may be used to enhance their intellectual capabilities. Inventor and futurist Ray Kurzweil has predicted that by the 2030s, AI have achieved human levels of intelligence, and that it will be possible to have AI that goes inside the human brain to boost memory, turning users into human-machine hybrids. As Kurzweil has described it, "We're going to expand our minds and exemplify these artistic qualities that we value."

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How Artificial Intelligence Is Totally Changing Everything - HowStuffWorks

Individually, the Internet of Things (IoT) and Artificial Intelligence (AI) are powerful technologies. When you combine AI and IoT, you get AIoTthe artificial intelligence of things. You can think of internet of things devices as the digital nervous system while artificial intelligence is the brain of a system.

What Is The Artificial Intelligence Of Things? When AI Meets IoT

What is AIoT?

To fully understand AIoT, you must start with the internet of things. When things such as wearable devices, refrigerators, digital assistants, sensors and other equipment are connected to the internet, can be recognized by other devices and collect and process data, you have the internet of things. Artificial intelligence is when a system can complete a set of tasks or learn from data in a way that seems intelligent. Therefore, when artificial intelligence is added to the internet of things it means that those devices can analyze data and make decisions and act on that data without involvement by humans.

These are "smart" devices, and they help drive efficiency and effectiveness. The intelligence of AIoT enables data analytics that is then used to optimize a system and generate higher performance and business insights and create data that helps to make better decisions and that the system can learn from.

Practical Examples of AIoT

The combo of internet of things and smart systems makes AIoT a powerful and important tool for many applications. Here are a few:

Smart Retail

In a smart retail environment, a camera system equipped with computer vision capabilities can use facial recognition to identify customers when they walk through the door. The system gathers intel about customers, including their gender, product preferences, traffic flow and more, analyzes the data to accurately predict consumer behavior and then uses that information to make decisions about store operations from marketing to product placement and other decisions. For example, if the system detects that the majority of customers walking into the store are Millennials, it can push out product advertisements or in-store specials that appeal to that demographic, therefore driving up sales. Smart cameras could identify shoppers and allow them to skip the checkout like what happens in the Amazon Go store.

Drone Traffic Monitoring

In a smart city, there are several practical uses of AIoT, including traffic monitoring by drones. If traffic can be monitored in real-time and adjustments to the traffic flow can be made, congestion can be reduced. When drones are deployed to monitor a large area, they can transmit traffic data, and then AI can analyze the data and make decisions about how to best alleviate traffic congestion with adjustments to speed limits and timing of traffic lights without human involvement.

The ET City Brain, a product of Alibaba Cloud, optimizes the use of urban resources by using AIoT. This system can detect accidents, illegal parking, and can change traffic lights to help ambulances get to patients who need assistance faster.

Office Buildings

Another area where artificial intelligence and the internet of things intersect is in smart office buildings. Some companies choose to install a network of smart environmental sensors in their office building. These sensors can detect what personnel are present and adjust temperatures and lighting accordingly to improve energy efficiency. In another use case, a smart building can control building access through facial recognition technology. The combination of connected cameras and artificial intelligence that can compare images taken in real-time against a database to determine who should be granted access to a building is AIoT at work. In a similar way, employees wouldn't need to clock in, or attendance for mandatory meetings wouldn't have to be completed, since the AIoT system takes care of it.

Fleet Management and Autonomous Vehicles

AIoT is used to in fleet management today to help monitor a fleet's vehicles, reduce fuel costs, track vehicle maintenance, and to identify unsafe driver behavior. Through IoT devices such as GPS and other sensors and an artificial intelligence system, companies are able to manage their fleet better thanks to AIoT.

Another way AIoT is used today is with autonomous vehicles such as Tesla's autopilot systems that use radars, sonars, GPS, and cameras to gather data about driving conditions and then an AI system to make decisions about the data the internet of things devices are gathering.

Autonomous Delivery Robots

Similar to how AIoT is used with autonomous vehicles, autonomous delivery robots are another example of AIoT in action. Robots have sensors that gather information about the environment the robot is traversing and then make moment-to-moment decisions about how to respond through its onboard AI platform.

Continued here:

What Is The Artificial Intelligence Of Things? When AI Meets IoT - Forbes

AI, Artificial Intelligence concept,3d rendering,conceptual image.

As I covered in a recent post for Forbes.com, this year has seen notable breakthroughs in AI (Artificial Intelligence).They have included innovations about algorithmslike GANs or Generative Adversarial Networksas well as advances in categories like NLP (Natural Language Processing), just to name a few.

Then what can we expect in 2020?Well, it seems likely that the innovations will continue at a rapid pace.

So heres a look at what we may see:

Anand Rao, the Global and US Artificial Intelligence Leader at PwC:

2020 will be the year of practical AI: using cool technology to solve boring problems. Business leaders are recalibrating their ambitions, with just 4% intending to scale AI across the organization. Instead, many are focusing on functional areas like finance, compliance, HR, and tax and universal pain points like extracting data from forms. In our survey, executives ranked using AI to operate more efficiently and increase productivity as the top-two benefits they expect from AI in the coming year.

Sanjeev Katariya, the VP/Chief Architect of eBay AI & Platforms:

From an ecommerce lens, AI will continue to grow, building adaptive and highly personalized markets and bridging borders while extending itself to places on the planet that need to see explosive growthwho in 2020, will gladly join the ecommerce revolution.

Michael Kopp, the Head of Data Science at HERE Technologies:

Deep Learning goes industrial. Dedicated DL chipsets are accelerating trial and error opportunities across industries, allowing diverse fields to build critical new models and AI components that solve real-world data problems.

Bryan Friehauf, the Executive Vice President and General Manager of Enterprise Software, ABB:

In 2020, AI will be the mainstream recommendation engine for the industrial sector. In energy management in particular, there is a huge opportunity. AI can provide facility managers with accurate power consumption predictions, which enables them to take timely action to reduce unplanned consumption spikes through rescheduling or switching off non-critical loads. AI will be the technology that takes simulations to the next level, helping to locate unstable areas of the grid and increase safety for workers in the field.

Steve Grobman, the Chief Technology Officer at McAfee:

In general, adversaries are going to use the best technology to accomplish their goals, so if we think about nation-state actors attempting to manipulate an election, using deepfake video to manipulate an audience makes a lot of sense. Adversaries will try to create wedges and divides in society.

Jake Saper, a Partner at Emergence Capital:

"In 2020, we will see the tech industry shift its focus away from using AI to drive automation and move it towards employing AI for augmentation. We'll realize that human-to-human jobs, which most often include dynamic input and feedback, are at their core still best performed by humans. In those cases, AI is ideally suited to augment, and not replace, human jobs."

Andy Ellis, the Chief Security Officer at Akamai:

What well see in many spaces is folks starting to understand the limitations of algorithmic solutions, especially where those create, amplify, or ossify bias in the world; and companies buying technologies will really need to start understanding how that bias impacts their operations.

Steve Wood, the Chief Product Officer at Boomi, a Dell Technologies business:

Overzealous data analyses have brought many companies face to face with privacy lawsuits from consumers and governments alike, which in turn has led to even stricter data governance laws. Understandably concerned about making similar mistakes, businesses will begin turning to metadata for insights in 2020, rather than analyzing actual data.

Jay Gurudevan, the Principal Product Manager of AI/ML at Twilio:

Well see more enterprises and businesses leverage AI tools and automated communication to better understand the entire customer journey. As consumers become more comfortable interacting with AI agents, Natural Language Processing will become more accurate and advanced and implementation will expand.

Avon Puri, the CIO of Rubrik:

An ecosystem of technologies will emerge that leverage intelligence, such as RPA technologies, and will provide new efficiencies in business processes that werent possible before. Next year is when new intelligent technologies will really take off, and RPA will lead automated intelligence in the enterprise.

Umesh Sachdev, the CEO and co-founder of Uniphore:

Speech analytics tools were an important bridge to support automation, and the same AI aiding humans behind the scenes will aid bots and enable the era of platforms. In 2020, heres where were going to see the most progress: anticipating intent by layering emotion and sincerity with historical data in real time. We'll be able to determine things like the likelihood of person paying their past-due bill.

Rama Sekhar, a Venture Partner at Norwest:

2020 will usher in the year of AI in the Enterprise. AI will get an upgrade from being an ingredient to a first class citizen as CIOs will introduce AI-first initiatives, just as they adopted cloud-first initiatives five years ago. Companies will have to justify why theyre not using AI in their own software, processes, and workflows in 2020.

Stefan Nandzik, the Vice President of Product & Brand Marketing at Signifyd:

In 2020, well see a spate of lawsuits filed by aggrieved consumers who have been wrongly barred from returning goods to retailers, or buying goods from ecommerce merchants, or renting home shares, or benefiting from Uber rides by algorithmically driven screening schemes. And well see the first significant pieces of legislation codifying consumers rights when it comes to AIcreating demand for liable machines.

Dr. Hossein Rahnama, the CEO of Flybits:

Startups are realizing that no matter how good their algorithm is, big companies aren't comfortable just handing over their sensitive datasets and core assets. So as the industry continues to mature over the next year, AI entrepreneurs will recognize that they have to shed their grad school mindset of give me the data and Ill do my work because that is no longer the case. This realization will force AI entrepreneurs to focus on more than just algorithms and shift their attention toward solidifying a data strategy that includes governance, management, encryption and tokenization. Because at the end of the day, without a strong data strategy, your AI strategy means nothing.

Chris Nicholson, the CEO of Pathmind:

One of the most promising areas of AI applications in 2020 will combine different, powerful forms of AI. Deep learning is used in a lot of perceptive tasks that answer the question: what am I looking at? For example, deep learning could recognize a grizzly bear in a photograph. Reinforcement learning is used in a lot of strategic tasks that answer the question: what should I do? For example, should I run away, stand in place or play dead? If you combine the two, then you get a powerful sequence of machine learning decisions you can combine. In this example: Given that I see a grizzly bear ahead of me, I should play dead. (Pro tip: grizzlies can run 35 miles per hour, but they do not eat carrion.) So those combinations of smart perceptions combined with smart actions vastly extend the value of AI. We move beyond simple classification into much higher ROI tasks that have implications for businesses, robotics, self-driving cars and video games.

Dr. Alex Liu, the Chief Data Scientist for IBM and the founder of RMDS Lab:

There will be more exploration of causality, which is the next generation of data analysis. It will be going from what to why. This will be crucial in improving the success rate of AI, which is still fairly low.

Tom (@ttaulli) is the author of the book,Artificial Intelligence Basics: A Non-Technical Introduction.

See the article here:

AI (Artificial Intelligence): What We Can Expect In The New Year - Forbes

Artificial Intelligence (AI) is creating a new frontline in information security. Systems that independently learn, reason and act will increasingly replicate human behavior. Like humans, they will be flawed, but also capable of achieving great things.

AI poses new information risks and makes some existing ones more dangerous. However, it can also be used for good and should become a key part of every organizations defensive arsenal. Business and information security leaders alike must understand both the risks and opportunities before embracing technologies that will soon become a critically important part of everyday business.

Already, AI is finding its way into many mainstream business use cases. Organizations use variations of AI to support processes in areas including customer service, human resources, and bank fraud detection. However, the hype can lead to confusion and skepticism over what AI actually is and what it really means for business and security. It is difficult to separate wishful thinking from reality.

Read More: How AI and Automation Are Joining Forces to Transform ITSM

As AI systems are adopted by organizations, they will become increasingly critical to day-to-day business operations. Some organizations already have, or will have, business models entirely dependent on AI technology. No matter the function for which an organization uses AI, such systems and the information that supports them have inherent vulnerabilities and are at risk from both accidental and adversarial threats. Compromised AI systems make poor decisions and produce unexpected outcomes.

Simultaneously, organizations are beginning to face sophisticated AI-enabled attacks which have the potential to compromise information and cause severe business impact at a greater speed and scale than ever before. Taking steps both to secure internal AI systems and defend against external AI-enabled threats will become vitally important in reducing information risk.

While AI systems adopted by organizations present a tempting target, adversarial attackers are also beginning to use AI for their own purposes. AI is a powerful tool that can be used to enhance attack techniques or even create entirely new ones. Organizations must be ready to adapt their defenses in order to cope with the scale and sophistication of AI-enabled cyberattacks.

Security practitioners are always fighting to keep up with the methods used by attackers, and AI systems can provide at least a short-term boost by significantly enhancing a variety of defensive mechanisms. AI can automate numerous tasks, helping understaffed security departments to bridge the specialist skills gap and improve the efficiency of their human practitioners. Protecting against many existing threats, AI can put defenders a step ahead. However, adversaries are not standing still as AI-enabled threats become more sophisticated, security practitioners will need to use AI-supported defenses simply to keep up.

The benefit of AI in terms of response to threats is that it can act independently, taking responsive measures without the need for human oversight and at a much greater speed than a human could. Given the presence of malware that can compromise whole systems almost instantaneously, this is a highly valuable capability.

The number of ways in which defensive mechanisms can be significantly enhanced by AI provide grounds for optimism, but as with any new type of technology, it is not a miracle cure. Security practitioners should be aware of the practical challenges involved when deploying defensive AI.

Questions and considerations before deploying defensive AI systems have narrow intelligence and are designed to fulfill one type of task. They require sufficient data and inputs in order to complete that task. One single defensive AI system will not be able to enhance all the defensive mechanisms outlined previously an organization is likely to adopt multiple systems. Before purchasing and deploying defensive AI, security leaders should consider whether an AI system is required to solve the problem, or whether more conventional options would do a similar or better job.

Read More: Artificial Intelligence in Restaurant Business

Questions to ask include:

Security leaders also need to consider issues of governance around defensive AI, such as:

AI will not replace the need for skilled security practitioners with technical expertise and an intuitive nose for risk. These security practitioners need to balance the need for human oversight with the confidence to allow AI-supported controls to act autonomously and effectively. Such confidence will take time to develop, especially as stories continue to emerge of AI proving unreliable or making poor or unexpected decisions.

AI systems will make mistakes a beneficial aspect of human oversight is that human practitioners can provide feedback when things go wrong and incorporate it into the AIs decision-making process. Of course, humans make mistakes too organizations that adopt defensive AI need to devote time, training and support to help security practitioners learn to work with intelligent systems.

Given time to develop and learn together, the combination of Human and Artificial Intelligence should become a valuable component of an organizations cyber defenses.

Computer systems that can independently learn, reason and act herald a new technological era, full of both risk and opportunity. The advances already on display are only the tip of the iceberg there is a lot more to come. The speed and scale at which AI systems think will be increased by growing access to big data, greater computing power and continuous refinement of programming techniques. Such power will have the potential to both make and destroy a business.

AI tools and techniques that can be used in defense are also available to malicious actors including criminals, hacktivists and state-sponsored groups. Sooner rather than later these adversaries will find ways to use AI to create completely new threats such as intelligent malware and at that point, defensive AI will not just be a nice to have. It will be a necessity. Security practitioners using traditional controls will not be able to cope with the speed, volume, and sophistication of attacks.

To thrive in the new era, organizations need to reduce the risks posed by AI and make the most of the opportunities it offers. That means securing their own intelligent systems and deploying their own intelligent defenses. AI is no longer a vision of the distant future: the time to start preparing is now.

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Deciphering Artificial Intelligence in the Future of Information Security - AiThority