When society uses artificial intelligence (AI) to help build judgments about individuals, fairness and equity are critical considerations. On Nov. 12, Brookings Fellow Nicol Turner-Lee sat down with Solon Barocas of Cornell University, Natasha Duarte of the Center for Democracy & Technology, and Karl Ricanek of the University of North Carolina Wilmington to discuss artificial intelligence in the context of societal bias, technological testing, and the legal system.

Artificial intelligence is an element of many everyday services and applications, including electronic devices, online search engines, and social media platforms. In most cases, AI provides positive utility for consumerssuch as when machines automatically detect credit card fraud or help doctors assess health care risks. However, there is a smaller percentage of cases, such as when AI helps inform decisions on credit limits or mortgage lending, where technology has a higher potential to augment historical biases.

Policing is another area where artificial intelligence has seen heightened debateespecially when facial recognition technologies are employed. When it comes to facial recognition and policing, there are two major points of contention: the accuracy of these technologies and the potential for misuse. The first problem is that facial recognition algorithms could reflect biased input data, which means that their accuracy rates may vary across racial and demographic groups. The second challenge is that individuals can use facial recognition products in ways other than their intended usemeaning that even if these products receive high accuracy ratings in lab testing, any misapplication in real-life police work could wrongly incriminate members of historically marginalized groups.

Technologists have narrowed down this issue by creating a distinction between facial detection and facial analysis. Facial detection describes the act of identifying and matching faces in a databasealong the lines of what is traditionally known as facial recognition. Facial analysis goes further to assess physical features such as nose shape (or facial attributes) and emotions (or affective computing). In particular, facial analysis has raised civil rights and equity concerns: an algorithm may correctly determine that somebody is angry or scared but might incorrectly guess why.

When considering algorithmic bias, an important legal question is whether an AI product causes a disproportional disadvantage, or disparate impact, on protected groups of individuals. However, plaintiffs often face broad challenges in bringing anti-discrimination lawsuits in AI cases. First, disparate impact is difficult to detect; second, it is difficult to prove. Plaintiffs often bear the burden of gathering evidence of discriminationa challenging endeavor for an individual when disparate impact often requires aggregate data from a large pool of people.

Because algorithmic bias is largely untested in court, many legal questions remain about the application of current anti-discrimination laws to AI products. For example, under Title VII of the 1964 Civil Rights Act, private employers can contest disparate impact claims by demonstrating that their practices are a business necessity. However, what constitutes a business necessity in the context of automated software? Should a statistical correlation be enough to assert disparate impact by an automated system? And how, in the context of algorithmic bias, can a plaintiff feasibly identify and prove disparate impact?

Algorithmic bias is a multi-layered problem that requires a multi-layered solution, which may include accountability mechanisms, industry self-regulation, civil rights litigation, or original legislation. Earlier this year, Sen. Ron Wyden (D-OR), Sen. Cory Booker (D-NJ), and Rep. Yvette Clark (D-NY) introduced the Algorithmic Accountability Act, which would require companies to conduct algorithmic risk assessments but allow them to choose whether or not to publicize the results. In addition, Rep. Mark Takano (D-CA) introduced the Justice in Forensic Algorithms Act, which addresses the transparency of algorithms in criminal court cases.

However, this multi-layered solution may require stakeholders to first address a more fundamental question: what is the goal that were trying to solve? For example, to some individuals, the possibility of inaccuracy is the biggest challenge when using AI in criminal justice. But to others, there are certain use cases where AI does not belong, such as in the criminal justice or national security contexts, regardless of whether or not it is accurate. Or, as Barocas describes these competing goals, when the systems work well, theyre Orwellian, and when they work poorly, theyre Kafkaesque.

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Highlights: Addressing fairness in the context of artificial intelligence - Brookings Institution

Getty

By Jeff Mills, Director, Solution Marketing at SAP SuccessFactors

Its no longer a secret that getting past the robot rsum readers to a human let alone land an interview can seem like trying to get in to see the Wizard of Oz. As the rsums of highly qualified applicants are rejected by the initial automated screening, job seekers suddenly find themselves having to learn rsum submission optimization to please the algorithms and beat the bots for a meeting with the Wizard.

Many enterprise businesses use Artificial Intelligence (AI) and machine learning tools to screen rsums when recruiting and hiring new employees.Even small to midsize companies who use recruiting services are using whatever algorithm or search-driven automated rsum screening those services utilize.

Why dont human beings read rsums anymore? Well, they do, but usually later in the process after the initial shortlist by the bots. Unfortunately, desirable soft skills and unquantifiable experience can go unnoticed by the best-trained algorithms. So far, the only solution is human interaction.

Despite the view from outside the organization, HR has good reason for using automated processes for screening rsums. To efficiently manage the hundreds or even thousands of applications submitted for one position alone, companies have adopted automated AI screening tools to not only save time and human effort but also to find qualified and desirable candidates before they move on or someone else gets to them first.

Nobodys ever seen the Great Oz!

The wealth of impressive time-saving and turnover reduction metrics equates to success and big ROI for organizations who automate recruiting and hiring processes. Most tales of headaches and frustration go untold for many thousands of qualified applicants whose rsums somehow failed to tickle the algorithm just right.

This trend is changing, however, as the bias built into AI and machine learning algorithms unintentionally or otherwise becomes more glaringly apparent and undeniable. Sure, any new technology will have its early adopters and zealous promoters and apologists as well as the naysayers and skeptics. But when that technology shows promise to change industry and increase profit, criticism can be drowned out and ignored.

The problem of bias in AI is not a new concern. For several years, scientists and engineers have warned that because AI is created and developed by humans, the likelihood of bias finding its way into the program code is high if not certain. And the time to think about that and address it as much as possible is during the design, development, and testing process. Blind spots are inevitable. Once buy-in is achieved and business ecosystems integrate that technology, the recursive and reciprocal influences of technology, commerce, and society can make changing course slow and/or costly.

Consider the recent trouble Amazon found itself in for some of its hiring practices when it had been determined that their AI recruiting tool was biased against women. AI in itself is not biased and performs only as it is instructed and adapts to new information. Rather, the bias comes from the way human beings program and develop the way machines learn and execute commands. Or if the outputs of the AI are taken at face value and never trained by ongoing human interaction, they can never adapt.

Bias enters in a few ways. One source is rooted in the data sets used to train algorithms for screening candidates. Other sources of bias enter when certain criteria are privileged, such as growing up in a certain area, attending a top university, or certain age preferences. By using the data for existing employees as a model for qualified candidates, the screening process can become a kind of feedback loop of biased criteria.

A few methods and practices can help correct or avoid this problem. One is to use broad swaths of data, including data from outside your company and even your industry. Also, train algorithms on a continual basis, incorporating new data, and monitoring algorithm function and results. Set benchmarks for measuring data quality and have humans screen rsums as well. Management of automated recruiting and screening solutions can go a long way in minimizing bias as well as reducing the number of qualified candidates who get their rsums rejected.

Bell out of order, please knock

As mentioned earlier, change takes time once these processes are in place and embedded. Until widespread acceptance that problems exist, and steps are taken to address them, the best job seekers can do is adapt.

With all of the possible ways that programmers biases influence the bots screening rsums, what can people applying for jobs do to improve their chances of getting past the AI gatekeepers?

The good news is that these moves will not only help eliminate false negatives and keep your rsum out of the abyss, but they are likely to make things easier for the human beings it reaches.

Well, why didnt you say so? Thats a horse of a different color!

So, what are they looking for? How do you beat the bots?

In the big picture, AI is still young, and we are working out the kinks and bugs not only at a basic code and function level, but also on the human level. We are still learning how to navigate and account for our roles and responsibilities in the overall ecosystem of human-computer interaction.

The bottom line is that AI, machine learning, and automation can eliminate bias or reinforce it. That separation may never be pure, but its an ideal that is not only worth striving for, it is absolutely necessary to work toward. The impact and consequences of our choices today will leave long-lasting effects on every area of human life.

And the bright side is that were already beginning to see how those theoretical concerns can play out in the real world, and we have an opportunity to improve a life-changing technological development whose reach and impact we can still only dimly imagine. In the meantime, job seekers looking to beat the bots are not entirely powerless, but can do what human beings have done well for ages: adapt.

Interested in how to deliver a great candidate experience? Read our guide on how to Transform the Candidate Experience.

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How To Get Your Rsum Past The Artificial Intelligence Gatekeepers - Forbes

A new report published this week (18 November 2019) looking at the intersection of AI and journalism has issued a warning to global newsrooms: collaborate with your competitors or face extinction.

The study, 'New powers, new responsibilities. A global survey of journalism and artificial intelligence' is a joint project between Polis, the international journalism think-tank at London School of Economics and Political Science, and the Google News Initiative, who has funded the research.

It surveyed 71 international news organisations on their on use of artificial intelligence for editorial purposes across a seven-month period, showing that just 37 per cent of them have a dedicated AI strategy.

Charlie Beckett, director, Polis, London School of Economics and Political Science, said that newsrooms have between two and five years to develop a meaningful strategy, or risk fading out of the digital landscape.

"This is a marathon, not a sprint - but theyve got to start running now," he said.

"Youve got two years to start running and at least working out your route and if youre not active within five years, youre going to lose the window of opportunity. If you miss that, youll be too late."

Even by the lowest possible trajectory, the rate of which natural language processing, translations, text generations and deep-fakes are developing means that newsrooms cannot afford to drag their heels, as the knowledge gap will only widen.

"Deepfakes have already accelerated in the last six months from a something in lab to something kids in Macedonia can churn out. Im not trying to panic people - the report stresses the positivity of AI - but there is a real sense of urgency here," he explained.

"Its really clear if you look at other industries that AI is shaping customer behaviour. People expect personalisation, be that in retail or housing, for production, supply or content creation. They use AI because of the efficiencies that it generates and how it enhances the services or products it offers.

"So if we, as journalists, are going to be living in that world, journalism is going to look very dumb if it doesnt have those capabilities. If journalism doesnt get its act together, worse than looking antiquated, it won't be looked at at all."

Despite these alarm bells, integrating AI into editorial process can have a range of benefits, including taking the burden out of long-winded tasks and sifting through large databases to produce local stories.

While many global newsrooms like Reuters News are quite advanced in this field, integrating significant cultural and operational changes is challenging for cash-strapped local and regional newsrooms.

The report details that many newsrooms struggle because of those financial limitations, but also because of lack of expertise, managerial strategy and time to prioritise AI, as well as scepticism around the technology. Some of these concerns touch on established arguments around algorithmic bias, filter bubbles and the influence of machine learning over editorial decisions.

The report also offers an eight-step pathway to integrating AI in newsrooms, even for those starting from scratch. It starts with assessing readiness of AI right through to creating task-specific roles with AI resources.

But dialogue and exchanging best practices from those in similar circumstances are also important.

Networking is not just a nice idea though, it can be a commercial arrangement.

"It could be a developing a good machine learning program, saying 'Can we benefit by selling this onto other newsrooms, so others can also benefit?' Or sharing data so you can train data better to develop better newsroom tools.

"When you train natural language processing you need a big dataset of images. One newsroom may not have that - are there opportunities for newsrooms to get together and share these tools and benefit? Its not altruism, its called benign self-interest."

Not co-operating, he argues, will lead to mutual self-destruction. But he is already seeing early signs of co-operation, recognising that competitors face common issues. Only by tackling the problem together can they resume their rivalry.

"To have healthy competition, you need healthy business," said Beckett.

"Get the tech right, then that will allow you to focus much harder on what you do differently and best, and how your editorial product is different to your competitors."

Ultimately, he said that AI will be neither the saviour nor the demise of journalism. But it will at least allow local journalists to leave their newsdesk more often and do more field work.

"If youve ever worked in a local newsroom, you know there are people who cant leave the office because they are churning out stories.

"They are losing the very idea of being local, which is going out into the streets, to meet people and to interact with your community. This is to augment and to power-up journalism, its not about journalism turning into a bland robotic product, its about getting back to distinctive journalism."

Want to know how to use breaking news to grow your audience? Find out how at Newsrewired on 27 November at Reuters, London. Head to newsrewired.com for the full agenda and tickets

If you like our news and feature articles, you can sign up to receive our free daily (Mon-Fri) email newsletter (mobile friendly).

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Newsrooms have five years to embrace artificial intelligence or they risk becoming irrelevant - Journalism.co.uk

LITTLETON, Colo. -- A group of MIT researchers visited Lockheed Martin this month for a chance to talk about the future of artificial intelligence and automation.

Liz Reynolds is the executive director of the MIT Task Force on the Work of the Future and says her job is to focus on the relationship between new technologies and how they will affect jobs.

Colorado is at the forefront of thinking about these things, Reynolds said. All jobs will be affected by this technology.

Earlier this year, U.S. Sen. Michael Bennet, D-Colo., created an artificial intelligence strategy group to take a closer look at how AI is being used in the state and how that will change in the future.

We need a national strategy on AI that galvanizes innovation, plans for the changes to our workforce, and is clear-eyed about the challenges ahead. And while were seeing progress, workers and employers cant wait on Washington, said Sen. Bennet in a statement. Colorado is well-positioned to shape those efforts, which is why weve made it a priority to bring together Colorado leaders in education, business, nonprofits, labor, and government to think through how we can best support and train workers across Colorado so they are better prepared for a changing economy."

MIT recently released a 60-page report detailing some of the possibilities and challenges with AI and automation.

One of the major challenges the group is considering is how the technology will affect vulnerable workers, particularly people who do not have a four-year degree.

The MIT team is looking for ways to train those workers to better prepare them for the changes.

Were not trying to replace a human, thats not something youre ever going to do with eldercare. For example, youre going be looking for ways to use this technology to help, Reynolds said.

Despite recent advances in AI, Reynolds believes the changes to the workforce will happen over a matter of decades, not years.

We think its going to be a slower process and its going to give us time to make the changes that we need institutionally, she said.

Beyond that, projections suggest that, with an aging workforce, there will be a scarcity of people to employ in future and technology can help fill some of those gaps.

The bigger question is how to ensure that workers can get a quality job that results in economic security for their families.

I think theres really an opportunity for us to see technology not as a threat but really, as a tool, Reynold said. If we can use the right policies and institutions to support workers in this transition then we could really be working toward something that works for everyone.

Lockheed Martin has been using artificial intelligence and automation in its space program for years. The companys scientists rely on automation to manage and operate spacecrafts that are on missions.

However, the technology is also being applied closer to home. The AI Lockheed Martin has created is already being applied to peoples day-to-day lives, from GPS navigation to banking. Now, the company is looking for more ways to make use of it.

Even though its been around for some time, we want to think about how we can use it in different, emerging ways and apply it to other parts of our business as well, said Whitley Poyser, the business transformation acting director for Lockheed Martin Space.

One of the areas in particular Lockheed Martin is looking to apply the technology is in its manufacturing, not only to streamline processes but to use data the machines are already collecting to predict potential issues and better prepare for them.

Poyser understands that there are some fears about this technology taking over jobs, but she doesnt believe thats the case.

Its not taking the job away, its just allowing our employees to think differently and think about elevating their skills and their current jobs, Poyser said. Its actually less of a fear to us and more of an opportunity.

The true potential of artificial intelligence is only beginning to be unleashed for companies like Lockheed Martin. Reynolds is hoping that predicting for the possibilities and challenges now will help the country better prepare for the changes in the decades to come.

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Colorado at the forefront of AI and what it means for jobs of the future - The Denver Channel

For many, just mentioning artificial intelligence brings up mental images of sentient robots at war with mankind and mans struggle to avoid the endangered species list. While this may one day be a real scenario for when (perhaps a big if?) mankind ever creates an artificial general intelligence (AGI), the more pressing matter is whether embedded software developers should be embracing or fearing the use of artificial intelligence in their systems. Here are five reasons why you may want to include machine learning in your next project.

Reason #1 Marketing Buzz

From an engineering perspective, including a technology or methodology in a design simply because it has marketing buzz is something that every engineer should fight. The fact though is that if there is a buzz around something, odds are it will in the end help to sell the product better. Technology marketing seems to come in cycles, but there are always underlying themes that are driving those cycles that at the end of the day do turn out to be real.

Artificial intelligence has progressed through the years, with deep learning on the way. (Image source: Oracle)

Machine learning has a ton of buzz around it right now. Im finding this year that had industry events, machine learning typically makes up at least 25% of the event talks. Ive had several clients tell me that they need machine learning in their product and when I ask them their use case and why they need it, the answer is just that they need it. Ive heard this same story from dozens of colleagues, but the push for machine learning seems relentless right now. The driver is not necessarily engineering, but simply leveraging industry buzz to sell product.

Reason #2 The Hardware Can Support It

Its truly amazing how much microcontroller and application processors have changed in just the last few years. Microcontrollers which I have always considered to be resource constrained devices are now supporting megabytes of flash and RAM, having on-board cache and reaching system clock rates of 1 GHz and beyond! These little controllers are now even supporting DSP instructions which means that they can efficiently execute inferences.

With the amount of computing power available on these processors, it may not require much additional cost on the BOM to be able to support machine learning. If theres no added cost, and the marketing department is pushing for it, then leveraging machine learning might make sense simply because the hardware can support it!

Reason #3 It May Simplify Development

Machine learning has risen on the buzz charts for a reason. It has become a nearly indispensable tool for the IoT and the cloud. Machine learning can dramatically simplify software development. For example, have you ever tried to code up an application that can recognize gestures, handwriting or classify objects? These are really simple problems for a human brain to solve, but extremely difficult to write a program for. In certain program domains such as voice recognition, image classification and predictive maintenance, machine learning can dramatically simplify the development process and speed-up development.

With an ever expanding IoT and more data than one could ever hope for, its becoming far easier to classify large datasets and then train a model to use that information to generate the desired outcome for the system. In the past, developers may have had configuration values or acceptable operation bars that were constantly checked during runtime. These often involved lots of testing and a fair amount guessing. Through machine learning this can all be avoided by providing the data, developing a model and then deploying the inference on an embedded systems.

Reason #4 To Expand Your Solution Toolbox

One aspect of engineering that I absolutely love is that the tools and technologies that we use to solve problems and development products is always changing. Just look at how you developed an embedded one, three and five years ago! While some of your approaches have undoubtedly stayed constant, there should have been considerable improvements and additions to your processes that have improved your efficiency and the way that you solve problems.

Leveraging machine learning is yet another tool to add to the toolbox that in time, will prove to be an indispensable tool for developing embedded systems. However, that tool will never be sharpened if developers dont start to learn about, evaluate and use that tool. While it may not make sense to deploy a machine learning solution for a product today or even next year, understanding how it applies to your product and customers, the advantages and disadvantages can help to ensure that when the technology is more mature, that it will be easier to leverage for product development.

Real Value Will Follow the Marketing Buzz

There are a lot of reasons to start using machine learning in your next design cycle. While I believe marketing buzz is one of the biggest driving forces for tinyML right now, I also believe that real applications are not far behind and that developers need to start experimenting today if they are going to be successful tomorrow. While machine learning for embedded holds great promise, there are several issues that I think should strike a little bit of fear into the cautious developer such as:

These are concerns for a later time though, once weve mastered just getting our new tool to work the way that we expect it to.

Jacob Beningo is an embedded software consultant who currently works with clients in more than a dozen countries to dramatically transform their businesses by improving product quality, cost and time to market. He has published more than 200 articles on embedded software development techniques, is a sought-after speaker and technical trainer, and holds three degrees which include a Masters of Engineering from the University of Michigan. Feel free to contact him at [emailprotected], at his website, and sign-up for his monthly Embedded Bytes Newsletter.

January 28-30:North America's largest chip, board, and systems event,DesignCon, returns to Silicon Valleyfor its 25th year!The premier educational conference and technology exhibition, this three-day event brings together the brightest minds across the high-speed communications and semiconductor industries, who are looking to engineer the technology of tomorrow. DesignCon is your rocket to the future. Ready to come aboard?Register to attend!

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4 Reasons to Use Artificial Intelligence in Your Next Embedded Design - DesignNews

2020 is going to be a big year for artificial intelligence, that is.

At least, that was the message LivePerson CEO Robert LoCascio delivered to CNBC's Jim Cramer on Friday.

"When we think about 2020, I really think it's the start of everyone having [AI]," LoCascio said on "Mad Money." "AI is now becoming something that's not just out there. It's something that we use to drive everyday life."

LivePerson, based in New York City, provides the mobile and online messaging technology that companies use to interact with customers.

Shares of LivePerson closed up just more than 5% on Friday, at $38.32. While it sits below its 52-week high of $42.85, it is up more than 100% for the year.

It reported earnings last week, with total revenue at $75.2 million for the third quarter, which is up 17% compared with the same quarter in 2018.

More than 18,000 companies use LivePerson, including four of the five largest airlines, LoCascio said. Around 60 million digital conversations happen through LivePerson each month, he said.

"You can buy shoes with AI on our platform. You can do airlines. You can do T-Mobile, change your subscription with T-Mobile," he said. "That's the stuff in everyday life."

The world has entered a point where technology has transformed all aspects of communication, LoCascio said.

"Message your brand like you message your friends and family," he said, predicting a day where few people want to pick up the phone and call a company to ask questions. "We're powering all that ... for some of the biggest brands in the world."

LoCascio said LivePerson, which he founded in 1995, now uses AI to power about 50% of the conversations on its platform.

"We're one of the few companies where it's not a piece of the puzzle. It's the entire puzzle," he said.

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Artificial intelligence has become a driving force in everyday life, says LivePerson CEO - CNBC

Technology has found a way to seep into every nook and cranny of the human experience. Throughout this digital shift, there has been one universal language that moves society toward an upward trajectory and lets humans live in an easier manner: coding.

Computer science is an increasingly important area of study, as the digital and physical world blends seamlessly together, like a beautifully choreographed dance. But like all things that humans touch, the mathematical sequences that create our digital existence are flawed by the systems of inequality that thrive within the physical world.

Each person, regardless of their background, has inherently adopted a set of subconscious biases. The prejudice that each person holds can come across in both superficial and significant ways, and when an engineer is creating an algorithm for a new system of artificial intelligence, those biases will undoubtedly affect the outcome.

This is not to say that humans are intentionally or deliberately ingraining their biases into these systems, but traces of their preferences break through from the data that is fed into these new creations.

Take, for instance, the AI software ImageNet.

In September, thousands of people uploaded their photos to a website called ImageNet Roulette, which used the AI software to analyze a persons face and describe what it saw.

This seemingly amusing game churned out a plethora of responses from nerd to nonsmoker. However, when Tabong Kima, a 24-year-old African American man, uploaded his smiling photo, the software analyzed him as an offender and wrongdoer, according to the New York Times.

To add insult to injury, the software also analyzed the man with him, another person of color, a spree killer.

At first glance, some may write this flawed social media trend as unimportant in the grand scheme of things, but that is far from the truth.

ImageNet is one of many data sets that have been extensively used by tech giants, start-ups and academic labs when they train new forms of artificial intelligence. This means that any flaws in this one data set, such as the racist labelling in Kimas case, have already spread far and wide throughout the digital realm of existence.

As engineers increasingly drift toward the production of AI software, with the goal of lifting tedious responsibilities from the shoulders of busy individuals, it is important to ensure the footprint of systemic inequality that has historically permeated throughout the world does not find a way into the powerful realm of software.

Software has progressively impacted each persons ability to thrive in the world. Whether thats through applying for credit cards or jobs, there continues to be less of a hands-on approach when these applications are reviewed.

This past week, Apple came under fire for their credit cards alleged sexist algorithms.

The conversation first surfaced when David Hansson, a prominent software engineer, tweeted about the issues he and his wife were having with Apples credit card.

The @AppleCard is such a f sexist program. My wife and I filed joint tax returns, live in a community-property state, and have been married for a long time. Yet Apples black box algorithm thinks I deserve 20x the credit limit she does. No appeals work, Hansson tweeted.

Not long after, Apples co-founder Steve Wozniak weighed in on the issue, stating that he and his wife experienced a similar issue with the Apple credit card.

To explain simply, a black box algorithm is a system where the inputs and outputs can be viewed by an observer, but without any knowledge of the internal system works. Meaning that although theres an output of data, no one knows how the system created that information.

In response to the deeply unsettling prospect of Apple limiting users in a way which hints at sexist black box practices, Sen. Ron Wyden (D-Oregon) tweeted, The risks of unaccountable, black box algorithms shouldnt be underestimated. As companies increasingly rely on algorithms to handle life-changing decisions and outcomes, federal regulators must do their part to stamp out discrimination before its written into code.

Goldman Sachs, the financial company that handles the credit limit for Apple Cards, has denied the use of black box algorithms, an even more important issue has surfaced from their response.

Even if black box algorithms are not in place, and a credit card application is not explicitly asking for the applicants gender, these refined machine-learning algorithms can still analyze the information its being fed and describe what gender they are analyzing. This is then applied to determine credit limits.

For instance, the machines could learn that applicants who have credit cards open at a particular womens clothing store are a bad financial risk. It could then provide lower credit limits for those who carry these cards, which results in women receiving lower credit limits than men, according to Forbes Magazine.

Another instance of gender-biased algorithms occurred in 2018, after Amazon engineers created an AI engine with the sole purpose of vetting through over 100 resumes to help choose the top candidates to be hired.

The tech giant realized that the engine was not rating women software engineer applicants in a fair way, because the resume patterns that the engine had been taught to replicate illustrated the stark gender gap within the tech industry.

In a male-dominated industry, Amazons system taught itself that male applicants were preferred over women.

Even after the engineers reprogrammed the system to ignore explicitly gendered words, like womens, the system still picked up on implicitly gendered words and used that to rate its applicants.

As these new systems are trained to learn from historical decisions made by humans, it must come as no surprise that the race and gender-based inequality that has plagued society for so long has now found a new home within the digital realm.

Discrimination is entangled in our private lives, thats just the truth. The tango of privilege continues to strut across all facets of human existence, and as this experience dives deeper within the world of artificial intelligence, engineers must ensure that they are not dipping further into the discrimination that minority communities have historically been shown.

Were all beginning to understand better that algorithms are only as good as the data that gets packed into them, said Sen. Elizabeth Warren (D-MA) in an interview with Bloomberg News. And if a lot of discriminatory data gets packed in, in other words, if thats how the world works, and the algorithm is doing nothing but sucking out information about how the world works, then the discrimination is perpetuated.

Theres no easy fix to this problem. The way in which bias affects the livelihood of individuals, and how to combat that in a fair manner, has long been a question for social scientists and philosophers. Expanding that issue into technology, where concepts have to be defined in mathematical terms, illustrates the hard work that must be done to create a truly fair digital environment.

While fixing these computing errors will rely on an extreme amount of trial and error, its the responsibility of software engineers to ensure that these new technologies will not cause more harm and discrimination toward people.

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Artificial intelligence engines have implicit biases - The Daily Titan

Have you ever commiserated with your colleagues that your boss acts like an automaton?

This soon might be more than just a figure of speech and some employees don't necessarily think that would be a bad thing.

By 2030, up to 800 million workers around the world could be replaced by machines. The fear of rampaging robots isnt just restricted to jobs. Leaders in emerging technology, such as Elon Musk, have suggested artificial intelligence (AI) is a fundamental risk to the existence of civilization.

But a new survey shows some workers have much friendlier views toward AI. Oracle and Future Workplace found 82% of workers believe robot managers are better at certain tasks such as maintaining work schedules and providing unbiased information than their human counterparts.

And almost two-thirds (64%) of workers worldwide say they would trust a robot more than their human manager. In China and India, that figure rises to almost 90%.

Almost two-thirds of workers worldwide trust a robot manager more than a human manager.

Image: Oracle/Future Workplace

The use of robotics in Asia is growing rapidly. Sales of industrial robots in India jumped by 39% in a year, while China is aiming to become one of the worlds most automated nations by 2020.

The implementation of AI technology, including robots, is expected to add as much as $15.7 trillion to the global economy by 2030. Automating routine tasks and administration will free employees up to focus on more complex work, while product development will become more agile as machines learn rapidly about what customers want.

The research recognizes that robots can bring complementary skills to the workplace. More than half of those surveyed by Oracle/Future Workplace say they're excited about having robot co-workers. Millennials are particularly enthusiastic.

There is growing enthusiasm for human-robot work partnerships.

Image: International Federation of Robotics

Our workplaces are changing and not necessarily for the worse. A World Economic Forum report on the future of work suggests that while 75 million jobs may be lost to automation by 2022, another 133 million additional new roles will be created.

The World Economic Forum was the first to draw the worlds attention to the Fourth Industrial Revolution, the current period of unprecedented change driven by rapid technological advances. Policies, norms and regulations have not been able to keep up with the pace of innovation, creating a growing need to fill this gap.

The Forum established the Centre for the Fourth Industrial Revolution Network in 2017 to ensure that new and emerging technologies will helpnot harmhumanity in the future. Headquartered in San Francisco, the network launched centres in China, India and Japan in 2018 and is rapidly establishing locally-run Affiliate Centres in many countries around the world.

The global network is working closely with partners from government, business, academia and civil society to co-design and pilot agile frameworks for governing new and emerging technologies, including artificial intelligence (AI), autonomous vehicles, blockchain, data policy, digital trade, drones, internet of things (IoT), precision medicine and environmental innovations.

Learn more about the groundbreaking work that the Centre for the Fourth Industrial Revolution Network is doing to prepare us for the future.

Want to help us shape the Fourth Industrial Revolution? Contact us to find out how you can become a member or partner.

Those new roles as well as stable occupations such as human resources specialists and university lecturers are likely to play on our creativity and ability to empathize with colleagues.

AI is redefining not only the relationship between worker and manager, but also the role of a manager in an AI-driven workplace, says Dan Schawbel, Research Director at Future Workplace.

Managers will remain relevant in the future if they focus on being human and using their soft skills, while leaving the technical skills and routine tasks to robots.

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Two-thirds of employees would trust a robot boss more than a real one - World Economic Forum

Project Cortex will let partners build knowledge networks for customers.

Microsoft intends to significantly simplify how people find organizational information through internal knowledge networks. Its via new technology set to appear next year, revealed as Project Cortex.

If Project Cortex works as Microsoft envisions, it will streamline, or in many cases eliminate, the need for individuals to interrupt their work to search for internal information. Project Cortex, introduced at this months Microsoft Ignite conference in Orlando, Florida, was a stealth effort in the works for more than two years.

Project Cortex also promises to give Microsofts vast partner ecosystem much to digest going into 2020, particularly as customers look to utilize it to enhance their collaboration and employee productivity efforts and Microsoft begins offering training and enablement for partners.

Company officials described Project Cortex as the first major new productivity and collaboration service planned for Microsoft 365 since the launch of Microsoft Teams three years ago. Microsoft CEO Satya Nadella underscored the notion of bringing more seamless access to knowledge in context of whatever application a user is working in is a critical next step forward for the Microsoft 365 platform.

Project Cortex takes what is data today inside of your organization and converts it into knowledge, Nadella said during his Ignite keynote session.

Under development by the Microsoft SharePoint team alongside adjacent Office groups including Teams, Yammer and Outlook, Project Cortex uses the companys AI, advances in Microsoft Search and its Graph APIs to create organizational knowledge networks that will enable customers to automatically surface collective information.

Project Cortex is scheduled to appear in Office, Dynamics and other connected applications in the first half of 2020. Its not a product itself; rather, Project Cortex will apply its advances in artificial intelligence (AI) and machine learning, drawing on the Microsoft Graph APIs.

A topic card with resources appears when a user hovers over an acronym.

Using the Microsoft Graph, individuals will be able to find organizational information contextually, without switching applications. For example, Microsoft demonstrated at Ignite how a user can scroll over an acronym in any Office app to get a definition of what that acronym means through a topic card that appears without interrupting the readers flow, which inevitably occurs when searching for it in some other application.

You now have AI-driven topics, so essentially you have AI creating topic wikis inside the enterprise, Nadella said. You have that ability now, in the context of any document or any email. More interestingly, you can find the experts who know something about that acronym, and you can really integrate and interact with them.

Project Cortex will automatically use metadata that resides in distributed repositories in what Microsofts SharePoint team described as the next generation of core enterprise content management (ECM) services.

Chris McNulty, Microsofts senior product manager for Project Cortex, who works on the SharePoint team, underscored that Project Cortex is not a new platform, but rather building on the existing Microsoft 365 and SharePoint platforms.

Were able to reach left to right across the suite to be able to invoke the best on top of our platform [by] doing things with security, doing things with compliance [and] doing things with search, McNulty said during an Ignite session that provided an overview of Project Cortex.

McNulty noted that all of these capabilities will bring together and invoke the metadata within those specific services. For instance, with Microsoft Information Protection, Project Cortex will extract key metadata that can attach and invoke policies based on the value of a contract, he said.

Microsofts Seth Patton earlier this month at Ignite 2019.

Seth Patton, general manager of Microsofts Office 365 marketing group, said during a briefing at Ignite that the company spent time with 25 customers in the development of Project Cortex, and 13 organizations are testing it a private technical preview. Patton said Project Cortex is centered around a common problem facing organizations knowledge drain the consequence of those with expertise leaving an organization.

That knowledge is walking out the door, Patton said.

While acknowledging thats a problem that has long faced all organizations, Patton said the need for making internal knowledge more readily available with the appropriate controls to protect it is becoming more critical. Whats driving that acceleration, he noted, is

Read more:

Microsoft Aims to Bring AI to Mainstream Collaboration with Project Cortex - Channel Futures

Artificial intelligence has helped archaeologists uncover an ancient lost work of art.

The Nazca Lines in Peru are ancient geoglyphs, images carved into the landscape. First formally studied in 1926, they depict people, animals, plants, and geometric shapes. The formations vary in size, with some of the biggest running up to 30 miles long. Their exact purpose is unknown, although some archaeologists think they may have had religious or spiritual significance. Local guides believe the lines relate to sources of water.

Some Nazca lines span miles of Peruvian countryside. Flickr/Christian Haugen

New geoglyphs are still being discovered and can be hard to spot due to changes in the landscape, with natural erosion and urbanization breaking them up.

A research team from Yamagata University recently announced it had discovered 142 new Nazca formations, including images of birds, monkeys, fish, snakes, and foxes.

The team partnered with IBM to try and train its deep-learning platform Watson to look for hard-to-find geoglyphs.

They fed the AI with aerial images to see if it could spot any more Nazca outlines. Watson threw up a few candidates, from which the researchers picked the most promising. Sure enough, their field work confirmed the AI had found an ancient Nazca artwork.

The find was a relatively small depiction of a humanoid figure spanning just 16 feet. The researchers estimate the figure dates from roughly 100 BC to AD 100, making it at roughly 2,000 years old.

The project's success has prompted Yamagata University to announce a more prolonged partnership with IBM, and will create a full location map of the geoglyphs to help future archaeologists.

Read more from the original source:

Scientists used IBM Watson to discover an ancient humanoid stick figure - Business Insider

Artificial intelligence (AI) is more than just cyborgs in movies trying to destroy humanity. It has actual real-world applications that can make our lives better and our businesses stronger and more profitable. In retail, artificial intelligence is being adopted rapidly - between 2016 and 2018 there was a 600% increase in adoption. Unfortunately the adoption rate is still relatively low, ranging from 26% for home improvement stores to 33% for apparel and footwear. If AI can make such a big difference, why isnt everyone adopting it?

Currently, only 15% of companies are saying they are spearheading AI adoption. Only 25% of large retailers are investing up to 10% of their capital in artificial intelligence systems, while all other size retailers are spending just 7%. Implementation of AI is costly, but those who use it are finding the benefits far outweigh the costs.

Customer service is one of the most successful use-cases for artificial intelligence yet. Customer-facing AI can improve customer satisfaction by 9%, reduce customer complaints by 8%, and can lower customer churn by 5%, all in the early stages. As this technology improves, it can help even more with customer satisfaction. Currently, voice technology is being programmed that can emulate and mirror human emotions better, mimicking empathy and deescalating tense customer service situations. But even just getting people to the right customer service representative when they call a call center can help get customers the right remedy to their situation.

Chatbots and virtual assistants are an extension of this technology that are used to assist customers who arent having a problem. They can help assist customers in finding the right item to buy, finding new items to try, and learning more about what they are buying. In fashion, AI and chatbots are being used to help customers find new clothing looks based on what they like. In the spirits business, Brown-Forman has a Whiskey Whisperer that can help customers learn more about whiskey, find new products, and find cocktail recipes to try.

AI is also helping to streamline the shopping experience. In Amazon Go stores, cameras and AI mean that customers can take items off the shelves, bag them and walk out of the store, saving time in the checkout line, as the system rings things up as you go and automatically charges you as you walk out the door.

Throughout the supply chain, AI can be used to streamline operations. In production, AI can be used to forecast orders, schedule workers optimally, and even create a schedule that will utilize electricity more efficiently. In shipping, AI can help ensure trucks are completely full so empty space isnt being shipped. It can also optimize shipping routes to save on time and fuel. In retail, AI can be used to optimize ordering so that valuable inventory isnt sitting around collecting dust.

Artificial intelligence is often portrayed as something that is going to destroy mankind in the movies, but in reality most of the real-life applications are pretty mundane and most are actually beneficial. As AI is adopted in more aspects of the retail landscape, there will certainly be bumps in the road. But the end product will be a stronger, more agile sector of the economy that serves both customers and businesses better. Learn more about the future of retail with AI from the infographic below!

Read next: From Science Fiction To Reality With Artificial intelligence (infographic)

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The Future Of Artificial Intelligence In Retail - Digital Information World

Although AIs are entering new areas every day, a handful of AI laboratories that still focus on artificial intelligence are still consuming large amounts of cash and have made not much progress on AI.

According to the documents submitted to the UK Companies Registry in August, only the Alphabet-owned AGI Lab DeepMind lost $570 million in 2018 alone. Another AI Lab, OpenAI, which aims to create AGI, had to abandon its non-profit organisation to find investors in its expensive research. Both labs have achieved extraordinary success, including the creation of robots that can play complex board games and video games. But they are still far from creating artificial intelligence.

A survey by MIT Sloan Management Review and BCG showed that 90 per cent of companies are currently investing in artificial intelligence technology. But less than 40 per cent of companies were able to sense and calculate the economic benefits of implementing AI, while the rest noted that new technologies did not affect the business, or the impact was minimal. According to experts, not all enterprises have learned how to correctly use AI in their business areas, it is also incorrect to consider AI as a means of reducing costs. Successful companies integrate AI into the overall business development strategy; for them, this is one of the tools to increase revenue, rather than save.

Georgy Lagoda, the Deputy CEO of Software Product Group, In his report at CNews Forum 2019 answered the question of why companies continue to invest and develop artificial intelligence technologies, and also spoke about the application of the latest AI trends in various Russian government and business fields.

According to George, the use of AI is necessary for the field of Big Data. Over the past 2 years, about 95 per cent of all world data was generated, while 90 per cent of the data are not structured and fragmented. In this regard, in the era of the digital economy, there is a need to implement AI-based solutions, without which the processing of a huge amount of data becomes impossible.

In 2019, based on business needs, the Hype Gartner cycle included 8 new and previously known technologies in the artificial intelligence class: cloud AI services, AutoML, augmented intelligence, Explainable AI, smart devices, reinforcement learning, quantum computers, marketplaces with AI. One of the most sought-after trends of this year is speech recognition and synthesis. George cites Amazon Alexa and Yandex Alice as an example, as well as Vera Voice technology, which allows synthesising celebrity speech.

Furthermore, George mentioned the agreement between Gazprom Neft and Saudi Aramco. The companies will now work together to create and use AI and neural networks to refine the hydrodynamic models of the fields.

In Russian banking sector, there is also a widespread introduction of AI: the appearance of chatbots, robot operators in call centres. The largest bank in Russia - Sberbank, plans to introduce smart machines to help operators with coin counting, loading operations, etc.

In the field of Russian education at the moment there are several problems. For example, in each class, there are laggards and those to whom the material seems fairly easy. It is assumed that the use of adaptive technologies will help in solving this issue. AI will monitor the progress of each student and inform the teacher about the levels of assimilation of various materials. The speaker of the Software Product also spoke about another application of the latest technologies in the field of education - the introduction of AI in testing USE tests. AI worked over a million-student work and, based on handwriting analysis, found tests written by different people.

George also spoke about the use of AI in the field of the Internet of things and announced the economic effect of the introduction of the SOVA PAK for writing fines and controlling parking lots. For the first year of work in one of the regions of Russia, the complex allowed to significantly reduce labour costs, saving about 500 million rubles. from the budget.

According to experts, technological capabilities are widely used in the Russian industrial market, especially in the field of instrumentation, engineering and aircraft manufacturing. Georgy cites an example of a system for automatic photo and video recording of violations and neglect of industrial safety. The solution allows you to increase the safety of production processes by instantly responding to dangerous situations and informing workers.

Then he drew the attention of listeners to the use of AI in the field of Russian government information security. Recently, cybercriminals have begun to use new technologies in the development of malware, which makes it increasingly difficult for anti-virus systems to detect threats. To combat cybercrime, IT developers began to implement the machine learning function in software products for the most in-depth analysis of malware. George cited Wallarm as an example, which develops solutions with a machine learning feature for firewalling. Classical security systems use a common set of static signatures, as a result of which, during the intrusion, a huge number of IP addresses are blocked. Unlike them, the Wallarm platform analyses traffic using AI and then blocks individual packets, i.e. specific attacker.

As for AI in the public sector, the speaker cited the Moscow Information Technology Department as an example, in which a huge number of information security events are generated every minute. When parsing such a data stream by a person, errors and omissions of dangerous incidents are not ruled out. Therefore, for the efficiency of processing such data, software solutions are introduced that, with the help of machine learning, can analyse and segment events, as well as respond to threats with high speed.

There is a risk in everything but the fact that AI is progressing continuously, and that AI is the future of the Internet, computing, etc. AI will strengthen the Internet of Things and IoT is expected to increase exponentially over the next 2 years. Simply put, there are currently about 23 billion connected devices, and this number will increase to over 75 billion, which is more than 200 per cent by 2025. All adds up to a YES, it is worth investing in AI.

However, there remains an important question of what part of AI to invest in? Then wait for our upcoming articles.

Amin Haqshanas, Sophomore in Mechatronics Engineering, Web Designer/Developer, Zinniagroup

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Is it worth investing in artificial intelligence? - ITProPortal

Speaking at BlockShow Asia 2019, Todalarity CEO Toufi Saliba posed a hypothetical question to the audience: How many people would take a pill that made you smarter, knowing they can be controlled by a social entity?

No one raised their hand, and he was unsurprised.

Thats the response that I get, zero percent of you, he continued. Now imagine at the same time the pill has autonomous decentralized governance so that no one can control or repurpose that pill but the host yourself.

This time hands were raised in abundance. Decentralized governance represents a necessary step for the tech community to build up a trust in digital developments related to securely managing big data.

Economics and ethics can go together thanks to decentralization, commented SingularityNET CEO Ben Goertzel.

But does the decentralized governance represent a step forward from centralization, or it is just an illusion of evolution? Cole Sirucek, co-founder of DocDoc, shared his vision:

It is when we are at a point of centralizing data that you can begin to think about decentralization. For example, electronic medical records: in five years the data will be centralized. After that, you can decentralize it.

Goertzal didnt fully agree: I dont think it is intrinsic. The reason centralized systems are simpler to come by is how institutions are built right now. There is nothing natural about centralization of data. He elaborated on the mutual dependence of two important technologies:

Blockchain is not as complex as AI, but it is a necessary component of the future. Without BTC, you dont have means of decentralized governance. AI enables the future, blockchain secures it.

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Artificial Intelligence Will Enable the Future, Blockchain Will Secure It - Cointelegraph

Rong Luo, CFO of TAL Education Group, Doranda Doo, SVP of iFLYTEK Co. Ltd. and Song Zhang, Managing Director of Thoughtworks China on Day 2 of CNBC East Tech West at LN Garden Hotel Nansha Guangzhou on November 19, 2019 in Nansha, Guangzhou, China.

Zhong Zhi | Getty Images News | Getty Images

The technology industry and policymakers need to address public concerns about artificial intelligence (AI) which are "not the fault of AI" itself, a tech executive said Tuesday.

"It is the fault of developers, so we need to solve this problem," said Song Zhang, managing director for China at global software consultancy, ThoughtWorks.

Consumer worries relating to AI include concerns about personal privacy and how the systems may get out of control, said Zhang during a panel discussion discussing the "Future of AI" at CNBC's East Tech West conference in the Nansha district of Guangzhou, China.

It is the duty of the tech industry and policymakers to focus on, discuss and solve such problems, said Zhang in Mandarin, according to a CNBC translation. Indeed, while consumers are curious about AI when they first come into contact with the technology, their mindset changes over time, said Rong Luo, chief financial officer of TAL Education Group.

"The first phase is everyone finds it refreshing, they like something new, they want to give it a try," said Luo.

But "in phase two, people start to care a lot about their privacy, their security," Luo added.

And finally, after "one to two years of adjustments, we (have) now entered phase three, we have a more objective view of the technology. We do not put (it) on the pedestal nor do we demonize it," said Luo.

Panelists at the session acknowledged the potential of AI in various fields such as language translation and education.

"Technology is here to assist them, empower them. We want to free them from those repetitive and meaningless work (tasks) so they have more energy and time for other more creative jobs," said Doranda Doo, senior vice president of Chinese artificial intelligence firm iFlytek.

"So I think what's the most powerful is not AI itself, but people who are empowered by AI," Doo said.

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Public fears about artificial intelligence are 'not the fault of A.I.' itself, tech exec says - CNBC

Recently, Hisham Daoud and Magdy Bayoumi of the University of Louisiana at Lafayette have introduced a completely newArtificial Intelligence (AI) system that predicts epilepsy seizures. According to the World Health Organizations reports, around 50 million people around the world are suffering from epilepsy and 70% of those patients can control the seizures through medications.

The new AI technology shows 99.6% accurate results, and the best thing about it is that it predicts the attacks an hour before it happens. In this way, the patient can gear up for it and take medications that can prevent its occurrence. Having enough time to control the attack is what a patient needs.

Some might be thinking that its the perfect cure and prediction strategy for the epilepsy patients, but its not; however, it is definitely something big in the market. Right now, there are many other options as well that does the work, for instance, electroencephalogram (EEG) tests to evaluate the brain activity to predict the condition.

This new AI technology performs both EEG tests and applies predictive models simultaneously to predict seizures. The deep learning algorithms in the technology allows predicting the electrical channels lightning that happens only during a seizure.

Currently, this technology is not available worldwide and there is some time before it does so. Right now, the team of this technology is busy working on a custom chip that can make the process further easier.

It is definitely good news for epilepsy patients and more improvement in the technology will only bring good outcomes for them.

Source: IEEE spectrum / IEEE.

Read next: Effects of Social Media on the Mental Health of Young People!

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Epileptic Seizure Prediction becomes much easier with the new Artificial Intelligence technology - Digital Information World

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Three Legal Areas to Think About When Using Artificial Intelligence in the Workplace - JD Supra

Intel Corporation(NASDAQ: INTC) revealed its plans for expanding its role in high-performance computing (HPC) artificial intelligence (AI) with new additions to its data-centric silicon portfolio and new software initiative that represents a paradigm shift from the current single-architecture, single-vendor programming models.

HPC and AI workloads demand diverse architectures, ranging from CPUs, general-purpose GPUs and FPGAs, to more specialized deep-learning NNPs, which Intel demonstrated earlier this month, said Raja Koduri, senior vice president, chief architect, and general manager of architecture, graphics and software at Intel. Simplifying our customers ability to harness the power of diverse computing environments is paramount, and Intel is committed to taking a software-first approach that delivers a unified and scalable abstraction for heterogeneous architectures.

oneAPI: A Developer-Centric Approach to Heterogeneous Computing

Intels silicon portfolio has a diverse mix of architectures deployed across various silicon platforms. The foundation of Intels data centric strategy is the Intel Xeon Scalable processor, which today powers over 90 percent of the worlds Top500 supercomputers. At Supercomputing 2019 conference, Intel unveiled a new category of general-purpose GPUs based on Intels Xearchitecture. Code-named Ponte Vecchio, this new high-performance, highly flexible discrete general-purpose GPU is architected for HPC modeling and simulation workloads and AI training.

Intels Data-Centric Strategy Delivers the Foundation for AI/HPC Convergence

Intels data-centric silicon portfolio and oneAPI initiative lays the foundation for the convergence of HPC and AI workloads at exascale within the Aurora system at Argonne National Laboratory.

Aurora will be the first U.S. exascale system to leverage the full breadth of Intels data-centric technology portfolio, building upon the Intel Xeon Scalable platform and using Xearchitecture-based GPUs, as well as Intel Optane DC persistent memory and connectivity technologies.

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Intel to Expand Role in High-Performance Computing and Artificial Intelligence - Financialbuzz.com