Category Archives: Artificial Intelligence

April 18, 2017 07:30 ET | Source: Paysa

PALO ALTO, Calif., April 18, 2017 (GLOBE NEWSWIRE) -- Paysa, the only platform that uses artificial intelligence (AI) to deliver personalized career and hiring recommendations plus real-world salary insights, today announced new findings from a study on the market for artificial intelligence tech talent and associated skills.

According to the study, companies are currently investing in more than $650 million in annual salaries to fuel the AI talent race with more than 10,000 available positions at top employers across the country.

The total annual investment, on average, among the top 20 employers that are looking to hire AI talent is $33,292,647. Yet Amazon is allocating nearly 600 percent more and Google is investing nearly 300 percent more than this figure, indicating that their future success is heavily dependent upon AI technologies, and subsequently, the talent to create them.

According to a 2016 Markets and Markets Report, the artificial intelligence (AI) market is expected to be worth $16.06 Billion by 2022, growing at a CAGR of 62.9% from 2016 to 2022. Several U.S. companies have raised $1 billion dollars or more to fuel artificial intelligence (AI) development.

Companies that are hiring to fill AI jobs are currently seeking tech and engineering talent with deep learning, machine learning, artificial intelligence, computer vision, neural networks and reinforcement learning skills. The top 20 employers who are hiring for these jobs and their current average annual investment allocation, based on average net salary, are as follows.

The findings also show that 40 percent of the open AI positions are available at large enterprises with more than 10,000 employees, 10 percent are available at companies with 1001 10,000 employees and 7 percent are available at companies with 11-50 employees. Companies with 51-100 employees account for 5 percent of the open positions across the market while just 2 percent of the open AI positions are available at companies with 0-10 employees. Another 25 percent of todays open AI positions were reported as being at companies with employee counts that were unknown at the time of the study.

Where are these jobs? The top 15 U.S. cities where companies are hiring the most tech talent with artificial intelligence skills and expertise include:

Total investment by region, considering average salary and number of available jobs is as follows:

From powering the self-driving car to guiding the way we shop, manage our finances and even do routine tasks, AI technology plays an increasing a role in every aspect of life as we know it, so its no surprise that investment in this area is growing at a rapid pace and companies are having a hard time keeping up, said Chris Bolte, CEO of Paysa. This latest research reveals that AI and machine learning skills are in such high-demand that companies across the country at every funding stage are weighing the skills and track-records of individuals even more heavily than years-of-experience or their degree as they staff-up their development teams.

The explosion of AI talent needs is a critical event happening right now nationwide -- engineers with right skills can land a great job at a top tier company in any region of the U.S, added Bolte.

Although a majority or 35 percent of the jobs require a Ph.D, 26 percent require just a masters degree and 18 percent require only a bachelors degree. And not all open jobs require a specific degree level suggesting that for some 21 percent of the jobs, having the right skills are more important than graduating from a specific university or degree program.

The Paysa study also reveals that just five percent of the open jobs are executive level, calling for 10 or more years-of-experience, 28 percent of the available AI jobs are senior level positions, requiring five or more, 27 percent are mid-level, mandating two to five years, and less than two percent are junior level, asking for just one to two-years. Another 39 percent of open AI jobs have experience level requirements that are unspecified.

Finally, employers that are hiring for the majority of the positions (36 percent) are at companies that have been around 20 years or more while 21 percent of todays open AI jobs can be found at companies that are 10 to 20-years-old. Only six percent of the open positions are at start-ups or companies that have been around five years or less.

About Paysa Paysa offers personalized career and hiring recommendations plus real-world salary insights for maximizing opportunity, earning potential and value at all stages of an individuals career. Using proprietary artificial intelligence technology and machine learning algorithms, Paysa analyzes millions of data points including jobs, resumes and compensation information, providing professionals with actionable tools, insights, and research. They can then see and understand their individual worth in the market today, and how to increase their value. Paysa also empowers enterprises with the knowledge they need to be competitive in todays fierce tech hiring market. Employers can learn which skills, real-world company experience and educational background offers the greatest predictor of a candidates or employees future success at their organization.

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U.S. Companies Raising $1 Billion or More to Fuel Artificial ... - GlobeNewswire (press release)

Theres no doubt that customer experience is absolutely essential for brand survival. AI and analytics will increasingly be deployed to support the customer experience, as well as being the principal means to deliver it.

That makes trust and transparency every bit as important as technology in achieving success.

So what are the components of customer experience? Personalisation is one key element. But theres been a tendency to see personalisation in terms of the value and advantage brands accrue from exploiting ever-more granular customer data in real time.

Instead, the focus needs to be on what personalisation means for the consumer.

And some more forward-thinking brands are starting to do just that. Theyre looking at their relationships with customers and their data in a new way.

Mindful of the need to earn and retain digital trust, these brands are being more open and transparent with consumers. For example, some organisations are enabling their customers to see all the data they held on them. This allows them to modify and control how their interactions with the brand happen in the future.

A more open and transparent relationship with the customer and the concept of fair value exchange sit at the heart of the customer experience in the digital era. When this is done properly, consumers are willing to share more because they recognise the value that they receive and have a degree of control over how brand interactions take place.

Many of these interactions are now managed by artificial intelligence, machine learning, chatbots and virtual assistants. As more of the consumer experience of a brand is driven by AI, the emphasis on fair value becomes even more important. And its a crucial component underpinning the ability to build living brands that adapt and evolve with every consumer interaction. As such, this becomes a powerful potential source of competitive differentiation.

Some of the leading digital businesses are already securing significant advances in their use of AI for everyday dealings with the consumer.

In only a few years, its likely that most interactions wont require a keyboard. Instead, they will be based on voice, gesture and augmented or virtual-reality interactions. And as screen time declines, the ability to own an interface will become a critical goal and a potential source of disruption.

Of course, using AI interfaces as the primary source of interaction and a key source of data needs to strike a balance between offering cool features consumers value and safeguarding against creepy intrusions that turn customers off.

This reinforces the need to give consumers a degree of control that goes beyond simply setting channel preferences to provide a deeper understanding of how and when communications take place. It means that the right time rather than real time becomes the key attribute consumers appreciate and respond to.

So what do organisations need to think about when integrating AI as their spokesperson and first point of contact with the customer?

The right operating model and governance:

Pervasive use of AI to support the customer experience requires a radically different approach to operating models, processes and governance. Entrusting customer data to analytics, machine learning and AI requires the right kind of robust capabilities and controls.

Evolving the data supply chain:

Having AI, machine learning and analytics as the drivers of the customer experience relies on collecting enormous amounts of data. This data can be internal, external, structured and unstructured from right across the value chain, as well as being augmented from other sources. In addition, overlaid on this is derived data and consumer insight. Making all this work together depends on a sophisticated and evolving data supply chain to feed the AI.

Keeping pace with changes in technology:

The sophistication of analytics, AI and machine learning is increasing exponentially. Techniques are in play today that didnt exist a few months ago. So it is essential to make the right choices regarding technology, and have solutions that can keep pace with a rapid rate of change.

People and machines working in tandem:

Tools and techniques need to be augmented with people. Human intervention and control must support AI and its adoption within these enterprises as it becomes the foundation for the customer experience. Its critical to test, learn and develop technology in ways that keep in step with the lightning-fast pace of change.

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Artificial intelligence is the future of customer experience - Telegraph.co.uk

Artificial intelligence has come a long way in recent years, and algorithms with machine-learning applications are proving skillful at things like playing poker, lip-reading, and, unfortunately, being biased.

Researchers at Princeton proved the point in an experiment involving an algorithm known as GloVe, which has learned about 840 billion words from the internet, notes Wired.

For their study, the researchers adapted a word-pairing test used to gauge bias in humans to do the same for the GloVe system. The upshot? Every single human bias they tested showed up, they report in the journal Science. "It was astonishing to see all the results that were embedded in these models," one researcher tells Live Science.

They found examples of ageism, sexism, racism, and moreeverything from associating men more closely with math and science and women with arts to seeing European-American names as more pleasant than African-American ones.

"We have learned something about how we are passing on prejudices that we didn't even know we were doing," says another researcher. Just as Twitter users taught Microsoft's chatbot Tay to unleash neo-Nazi rants on social media last year, so, too, does this oft-used algorithm learn from our own behaviors, regardless of whether they are good or bad.

(Elon Musk calls AI our "biggest existential threat.")

This article originally appeared on Newser: Machine Learning Has a Weakness: Humans

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Artificial intelligence can be as sexist as humans - Fox News

Artificial intelligence may help prevent heart failure.

Devrimb/iStockphoto

By Matthew HutsonApr. 14, 2017 , 3:30 PM

Doctors have lots of tools for predicting a patients health. Butas even they will tell youtheyre no match for the complexity of the human body. Heart attacks in particular are hard to anticipate. Now, scientists have shown that computers capable of teaching themselves can perform even better than standard medical guidelines, significantly increasing prediction rates. If implemented, the new method could save thousands or even millions of lives a year.

I cant stress enough how important it is, says Elsie Ross, a vascular surgeon at Stanford University in Palo Alto, California, who was not involved with the work, and how much I really hope that doctors start to embrace the use of artificial intelligence to assist us in care of patients.

Each year, nearly 20 million people die from the effects of cardiovascular disease, including heart attacks, strokes, blocked arteries, and other circulatory system malfunctions. In an effort to predict these cases, many doctors use guidelines similar to those of the American College of Cardiology/American Heart Association (ACC/AHA). Those are based on eight risk factorsincluding age, cholesterol level, and blood pressurethat physicians effectively add up.

But thats too simplistic to account for the many medications a patient might be on, or other disease and lifestyle factors. Theres a lot of interaction in biological systems, says Stephen Weng, an epidemiologist at the University of Nottingham in the United Kingdom. Some of those interactions are counterintuitive: A lot of body fat can actually protect against heart disease in some cases. Thats the reality of the human body, Weng says. What computer science allows us to do is to explore those associations.

In the new study, Weng and his colleagues compared use of the ACC/AHA guidelines with four machine-learning algorithms: random forest, logistic regression, gradient boosting, and neural networks. All four techniques analyze lots of data in order to come up with predictive tools without any human instruction. In this case, the data came from the electronic medical records of 378,256 patients in the United Kingdom. The goal was to find patterns in the records that were associated with cardiovascular events.

First, the artificial intelligence (AI) algorithms had to train themselves. They used about 78% of the datasome 295,267 recordsto search for patterns and build their own internal guidelines. They then tested themselves on the remaining records. Using record data available in 2005, they predicted which patients would have their first cardiovascular event over the next 10 years, and checked the guesses against the 2015 records. Unlike the ACC/AHA guidelines, the machine-learning methods were allowed to take into account 22 more data points, including ethnicity, arthritis, and kidney disease.

All four AI methods performed significantly better than the ACC/AHA guidelines. Using a statistic called AUC (in which a score of 1.0 signifies 100% accuracy), the ACC/AHA guidelines hit 0.728. The four new methods ranged from 0.745 to 0.764, Wengs team reports this month in PLOS ONE. The best oneneural networkscorrectly predicted 7.6% more events than the ACC/AHA method, and it raised 1.6% fewer false alarms. In the test sample of about 83,000 records, that amounts to 355 additional patients whose lives could have been saved. Thats because prediction often leads to prevention, Weng says, through cholesterol-lowering medication or changes in diet.

This is high-quality work, says Evangelos Kontopantelis, a data scientist at the University of Manchester in the United Kingdom who works with primary care databases. He says that dedicating more computational power or more training data to the problem could have led to even bigger gains.

Several of the risk factors that the machine-learning algorithms identified as the strongest predictors are not included in the ACC/AHA guidelines, such as severe mental illness and taking oral corticosteroids. Meanwhile, none of the algorithms considered diabetes, which is on the ACC/AHA list, to be among the top 10 predictors. Going forward, Weng hopes to include other lifestyle and genetic factors in computer algorithms to further improve their accuracy.

Kontopantelis notes one limitation to the work: Machine-learning algorithms are like black boxes, in that you can see the data that goin and the decision that comes out, but you cant grasp what happens in between. That makes it difficult for humans to tweak the algorithm, and it thwarts predictions of what it will do in a new scenario.

Will physicians soon adopt similar machine-learning methods in their practices? Doctors really pride themselves on their expertise, Ross says. But I, being part of a newer generation, see that we can be assisted by the computer.

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Self-taught artificial intelligence beats doctors at predicting heart attacks - Science Magazine

Forbes

4 Unique Challenges Of Industrial Artificial Intelligence Forbes Robots are probably the first thing you think of when asked to imagine AI applied to industrials and manufacturing. Indeed many innovative companies like Rodney Brooks' Rethink Robotics have developed friendly-looking robot factory workers who hustle ...

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4 Unique Challenges Of Industrial Artificial Intelligence - Forbes

Throughout the week we post dozens of original stories, connecting the dots across the travel industry, and every weekend we sum it all up. This weekend roundup examines digital trends.

For all of our weekend roundups, go here.

>>While European Union competition authorities might be happy with the results of their crackdowns, it doesnt look like much has changed. The market still makes it difficult for both smaller online travel agencies and the hotels themselves to compete:Hotels Still Afraid to Take on Expedia and Booking.com Despite Rule Changes

>>Travel is more exciting than a well composed and prop-heavy Instagram shot would let on. We think its time to start letting go:Its Time to Rethink the Travel Instagram Aesthetic

>>Can a legacy brand like Flight Centre really transform itself and get more digital and global?Flight Centre Invests $7 Million for Stake in Leading Argentinian Booking Site

>>Microsoft thinks that voice-powered internet gives it a shot via voice assistant Cortana at upending todays travel search funnel, which is dominated by Googles search results. The theorys plausible, but Microsoft needs to move faster to win:Microsoft Bets on Artificial Intelligence to Help It Succeed Again in Travel

>>American Express Global Business Travel is investing in technology to give travelers better digital tools and a more streamlined booking experience:CEO Interview: How American Express GBT Tackles Innovation

>>Big corporate travel players are finding solutions to improve the experience for their travelers. American Express Global Business Travels acquisition of booking tech company KDS last year shows one way forward:The Digital Battle to Improve Traveler Experience Skift Corporate Travel Innovation Report

>>Technology-driven innovation has done wonders for airline industry stock prices. But is that innovation also doing wonders for passengers? Right now the answer seems to be no:The Airline Industrys Tech Problem Digital Marketing News This Week

>>Startups raise funding for booking platforms for serviced apartments, event spaces, and branded budget hotels, while one Indian booking site gets a new investor owner:HotelPlanner Picks up TravelTicker: Travel Startup Funding This Week

See full article

Photo Credit: Microsoft's Cortana voice assistant lets users search the web for answers via voice. A new Harman Karman device is expected to bring Cortana into more homes soon. Microsoft

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Microsoft Bets on Artificial Intelligence and 7 Other Digital Trends This Week - Skift

For over a decade now, the biggest selling points of premium smartphones have been all about physical design.

But in the coming years, the consumer electronics market is poised to experience a radical shift one that every investor should start paying attention to today.

When Steve Jobs officially unveiled the first iPhone to the world in 2007, there was enormous hype surrounding its groundbreaking touch interface and for good reason. By blending input and display into a single place, Apple managed to redefine the mobile device entirely.

In the years that followed, Apple introduced a great number of incremental improvements, and it was always the physical redesigns that stood out and that have spurred sales the most.

The iPhone 4 (2010) was the first major redesign for Apple. It had a sleek, modern look, and it was the first iPhone to feature a high-pixel count Retina display. It also introduced the world to the front-facing camera, which has become a staple of todays premium smartphones.

The following year, Apple managed to nearly double its iPhone to 93 million units.

The changes to the iPhone 5 (2012) were less radical, but there were meaningful physical improvements nonetheless. The phone featured an extra-tall screen, which allowed for a bottom row of applications. It also introduced Apples Lightning connector, which could be easily inserted face up or face down.

Not long after, the iPhone 5S (2013) was introduced with Touch ID, enabled by a bold new fingerprint sensor.

And thenthere was the iPhone 6 (2014), Apples latest major redesign. This model introduced NFC for Apple Pay and extended its screen size by reducing the bezel footprint.

But in 2015 something weird happened...

That year, Apple released the iPhone 6S, and it was visually identical to its predecessor. If you held the two in your hand, youd have a difficult time telling the difference.

Then in 2016, it was more of the same: The iPhone7 was nearly identical from a design standpoint. The only notable addition was actually the removal of its headphone jack.

For acute investors, this three-year lull in physical redesign exposed a notable chink in Apples armor: the company, which was once adding new and meaningful physical features to its phones as often as once a year, had begun to run out of ideas.

That is, Apple was running into a very unusual problem most companies never face: design perfection.

When it comes to physical design, theres really not much to argue about: Apple has a reputation for being the best in the business.

But as the years have gone by, Apples design improvements have drastically reduced in significance.

And as a byproduct, its competitors have caught up. In certain cases, they have even surpassed Apple in terms of quality of design.

Samsungs flagship Galaxy S line, for one, has had wireless charging for years. Apple may or may not feature wireless charging in the iPhone 8.

Googles new flagship Pixel phone is another example. Its camera is arguably the best available on the market.

Of course, Apple is by no means lagging far behind its competitors, but the reality is that other manufacturers have reached design parity. Unless Apple has some radical new design up its sleeve, the company will continue to offer little more than a brand name above its competition.

What this likely means for Apple is further reduction in market share because consumers now have equivalent options. This is just a simple law of economics, and its why Googles Android OS snagged a record market share with nearly nine in every 10 smartphones late last year.

Its also why on Wednesday, TrendForce revealed that Samsung has already regained its top market share position following last years exploding Galaxy Note fiasco. The firm owned 26.1% of the smartphone market worldwide in the first quarter, versus Apples 16.9%.

Thats a stark contrast to the fourth quarter of fiscal 2016, where Apple stood at 20.3% market share and Samsung at 18.5%.

This is as much proof as you need that Apple has lost any true technological edge.

And it could be getting a whole lot worse for the major handset maker in the years to come.

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The only thing more troubling for Apple than product parity is product obsolescence.

In terms of design, Apple is at minimal threat from competing handset OEMs. With the smartphone near design perfection, theres little left to differentiate any single premium from the other.

But where Apple faces serious competition within the smartphone market is at the level of software and ecosystems.

Specifically, Apple is coming in at a heavy disadvantage when it comes to artificially intelligent digital assistants.

While its true that Apple was the first to introduce a digital assistant with Siri, its ability to leverage AI to its advantage has been negligible compared to the competition. Both Google and Amazon have begun making significant strides with Amazon Alexa and Google Assistant over the last year.

Google Assistant is a particular threat to Apple for two reasons...

First, Android penetrates close to 90% of the global smartphone market. This means Google can show Apple up with a single software update the moment it develops a fully conversational AI. And make no mistake, this is Googles objective.

Second, and this is also tied to Android market share, Google has access to significantly more consumer data than Apple and its able to parse it all out. This is whats referred to in the industry as a knowledge base, or as Google calls it, a Knowledge Graph.

According to various sources, Googles Knowledge Graph contains 18 billion statements about 570 million entities, with a schema of 1,500 entity types and 35,000 relation types.

All told, the knowledge base is armed with 70 billion facts. This means you can ask Google Assistant a question, and instead of directing you to a website, it can answer you directly in 70 billion different scenarios.

For years, Google has been learning about us. It knows what questions we ask and the answers were looking for. Its been collecting a vast web of knowledge. This unprecedented knowledge base is the heart of the companys long-term strategy for absolute technological dominance.

Apple, on the other hand, can only dominate on the hardware front, and will face an enormous challenge breaking out of its vertical ecosystem. Apples isolation, which once served the company quite well, has suddenly become a major detriment.

Ben Rickard, head of mobile at MEC global solutions and EMEA, puts it this way:

What is becoming increasingly clear is that visual and branding identity will become less relevant in a world that is operated by voice and Natural Language Processing (NLP).

In other words, were going to soon stop looking at our phones and start talking to them instead as digital assistants become more conversational. Eventually, the form factor of mobile devices as we know them today will start to fade away.

Gartner, for one, projects that by 2018, 30% of our everyday contact with technology will be conversational.

And ComScore estimates that 50% of all web searches will be through conversation by 2020.

Thats great news for software- and AI-focused companies like Google. Its terrible news for hardware-focused companies like Apple, Inc.

But more than any other business niche, its a winning formula for the makers of embedded microphones. No matter what the form factor of our future AI assistant, theyre always going to need a way to listen.

Until next time,

Jason Stutman

@JasonStutman on Twitter

Jason Stutman is Wealth Daily's senior technology analyst and editor of investment advisory newsletters Technology and Opportunity and The Cutting Edge. His strategy for building winning portfolios is simple: Buy the disruptor, sell the disrupted.

Covering the broad sector of technology and occasionally dabbling in the political sphere, Jason has written hundreds of articles spanning topics from consumer electronics and development stage biotechnology to political forecasting and social commentary.

Outside the office Jason is a lover of science fiction and the outdoors, and an amateur squash player at best. He writes through the lens of a futurist, free market advocate, and fiscal conservative. Jason currently hails from Baltimore, Maryland, with roots in the great state of New York.

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Artificial Intelligence: The Single Biggest Threat to Apple Inc. (NASDAQ: AAPL) - Wealth Daily

The days of artificial intelligence are upon us all and its unbelievable, to say the least. Just take the time to remember back, not that long ago, when there was so much science fiction. Now today that same science fiction is science fact. Thats the same way that artificial intelligence has come to us, but it is not based on fact. Now some people will tell you that you have to be afraid of artificial intelligence. Thats not the truth. You do not have to fear it at all. It has been proven that artificial intelligence is not as intelligent as it wants to make you believe it is. It is not as smart as it thinks it is. Artificial intelligence has told itself that it knows whats best for the American people, even if the people dont believe it. But you see, artificial intelligence does not care what the people think is the right thing for them and their lives. This artificial intelligence wants to shove down your and my throat what it believes in, because the people cant think for themselves.

There are those who will tell you that artificial intelligence is the best thing that could ever happen to America. Those who believe this are the same ones who think the past eight years were the best that this country has ever seen. This past eight years that I write about was the gift from those who were in power. These people of the past eight years are the artificial intelligence that I write about. They were not really as smart as what they told you that they were, now were they? They told you and themselves that they were the brightest bulb in the box. It did not turn out that way, now did it?

It seems to me that if they were the best thing since sliced bread, then they would not have lost nationwide more than 1,000 seats of office. The best Ive ever seen with A.I. was Harry Reid. It seems Dirty Harry was somewhat of a storyteller. Some would just say he could tell a lie if it would put him and his party in the lead, and he did. No time for detail, but one of his best lies came during the election when Mitt Romney was running against Obama. Just look it up and see for yourself. But it did not stop there with Harry Reid and his A.I. just look to the past. During the 113th Congress, Reid triggered the nuclear option. That was in 2013. Democrats in the Senate changed the rules on the filibuster, lowering the number of senators needed to confirm presidential nominees from 60 to a simple majority of 51. You see what A.I. got the Democrats? What were they thinking? Did they really think that they would never be in the minority? Now, the worm has turned. The shoe is on the other foot and it will be used against the Democrats. That still does not mean that it is going to be easy on the Republicans to get things done under Trumps administration.

In the long term, the changing of the rule represents a mighty power shift in a chamber that, for over two centuries, has given more rights to the minority party than any other legislative body from around the world. Now Trumps party holds the majority in the Senate and is assured of having his nominees approved with less opportunity for Democrats to obstruct his nominees. Karma has a way of getting on your back like a monkey when you least expect it, and its hard to shake off at times. The Democrats are reaping what they have sown in the past. The Republicans need not think this will not be a vicious circle in short order.

Another problem for the Democrats is they will be unable, on their own, to stop any of the 127 lower-court judicial appointments that Trump will make this year, because they are either vacant or will become vacant soon. The only help Democratic senators will have depends or their Republican counterparts respect for Senate tradition about the appointment of judges in their home states. Some feel with the nature of Washington these days, that tradition might well change.

Republicans in office are feeling the same way as the citizens are feeling about the Democrats. For the most part, the people see the Democrats as not willing to work with anybody who has different viewpoints other than their own. At times, all in Washington have to wonder what Reid was planning when he invoked the nuclear option in the first place. One thing that he wanted it to do was to confirm a head of the Consumer Financial Protection Board, which had been created during 2013, or there about. Republicans had opposed Obamas nominations to that post on the principal that the agency had been set up to be unaccountable to elected officials and unconstitutional. In time it was proven that this was the fact of the matter.

It has also been proven that under this last administration that unbelievable amounts of your tax dollars are unaccountable. It was used without authorization. But no one seems to know how the money was appropriated or why its just gone. Thats a joke and we all know it. It went into the pockets of Washingtons elite and their slush funds that went to pay their family members for jobs that they never went to. Again, for most of the hard-working people of America, its not hard to believe where the money went. It did not just sprout legs and run off. Now the A.I. would expect you to believe that it did.

Reid wanted to fill as many vacancies on the D.C. Circuit Court of Appeals as he could. Three years after the nuclear option, it helped Obama establish a liberal majority on the D.C. Circuit Court. This would help Obama give to the world the Internet and more. That brings us to today and the new fight that is taking place in Washington. It appears that we have an oh my moment in the Democratic party. In 2006, Neil Gorsuch only weeks after his nomination was confirmed by the Senate to the U.S. Court of Appeals for the 10th Circuit. We now know that he has made it to the Supreme Court. He has always been the type of judge that is approachable and popular with all parties. The American Bar Association has stated that he was unanimously well qualified for that posting. I myself have taken the time to watch interviews and read about this man. I do believe that he will be the type of Supreme Court Judge who will look beyond all the parties and will see the law as it was written. I see this man as of the people not someone who thinks he is above us and the laws of the land. Also, as I write these words, I know why the Left wished to block Judge Gorsuch.

It goes back to the last months of Obamas administration and the nomination of his pick, Judge Merrick Garland, to fill the vacancy of Judge Antonin Scalia. If Garland had filled Scalias post on the court, that would have made it possible that Obamas choice could tilt the balance of the court away from conservatives for years. The GOP knew that if Garland were allowed to take the seat, conservative grassroots voters would revolt and for the most part, they did. That is the very reason we now have Trump in office, like it or not. It was the people, not the GOP, that did not want Garland.

So now we see the nuclear option coming back to haunt the Democrats and their artificial intelligence. I dont want A.I. in office I want real intelligence.

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The Left's artificial intelligence - Richmond County Daily Journal

When Microsoft released an artificially intelligent chatbot named Tay on Twitter last March, things took a predictably disastrous turn. Within 24 hours, the bot was spewing racist, neo-Nazi rants, much of which it picked up by incorporating the language of Twitter users who interacted with it.

Unfortunately, new research finds that Twitter trolls aren't the only way that AI devices can learn racist language. In fact, any artificial intelligence that learns from human language is likely to come away biased in the same ways that humans are, according to the scientists.

The researchers experimented with a widely used machine-learning system called the Global Vectors for Word Representation (GloVe) and found that every sort of human bias they tested showed up in the artificial system. [Super-Intelligent Machines: 7 Robotic Futures]

"It was astonishing to see all the results that were embedded in these models," said Aylin Caliskan, a postdoctoral researcher in computer science at Princeton University. Even AI devices that are "trained" on supposedly neutral texts like Wikipedia or news articles came to reflect common human biases, she told Live Science.

GloVe is a tool used to extract associations from texts in this case, a standard corpus of language pulled from the World Wide Web.

Psychologists have long known that the human brain makes associations between words based on their underlying meanings. A tool called the Implicit Association Test uses reaction times to demonstrate these associations: People see a word like "daffodil" alongside pleasant or unpleasant concepts like "pain" or "beauty" and have to quickly associate the terms using a key press. Unsurprisingly, flowers are more quickly associated with positive concepts; while weapons, for example, are more quickly associated with negative concepts.

The IAT can be used to reveal unconscious associations people make about social or demographic groups, as well. For example, some IATs that are available on the Project Implicit website find that people are more likely to automatically associate weapons with black Americans and harmless objects with white Americans.

There are debates about what these results mean, researchers have said. Do people make these associations because they hold personal, deep-seated social biases they aren't aware of, or do they absorb them from language that is statistically more likely to put negative words in close conjunction with ethnic minorities, the elderly and other marginalized groups?

Caliskan and her colleagues developed an IAT for computers, which they dubbed the WEAT, for Word-Embedding Association Test. This test measured the strength of associations between words as represented by GloVe, much as the IAT measures the strength of word associations in the human brain.

For every association and stereotype tested, the WEAT returned the same results as the IAT. The machine-learning tool reproduced human associations between flowers and pleasant words; insects and unpleasant words; musical instruments and pleasant words; and weapons and unpleasant words. In a more troubling finding, it saw European-American names as more pleasant than African-American names. It also associated male names more readily with career words, and female names more readily with family words. Men were more closely associated with math and science, and women with the arts. Names associated with old people were more unpleasant than names associated with young people.

"We were quite surprised that we were able to replicate every single IAT that was performed in the past by millions," Caliskan said.

Using a second method that was similar, the researchers also found that the machine-learning tool was able to accurately represent facts about the world from its semantic associations. Comparing the GloVe word-embedding results with real U.S. Bureau of Labor Statistics data on the percentage of women in occupations, Caliskan found a 90 percent correlation between professions that the GloVe saw as "female" and the actual percentage of women in those professions.

In other words, programs that learn from human language do get "a very accurate representation of the world and culture," Caliskan said, even if that culture like stereotypes and prejudice is problematic. The AI is also bad at understanding context that humans grasp easily. For example, an article about Martin Luther King Jr. being jailed for civil rights protests in Birmingham, Alabama, in 1963 would likely associate a lot of negative words with African-Americans. A human would reasonably interpret the story as one of righteous protest by an American hero; a computer would add another tally to its "black=jail" category.

Retaining accuracy while getting AI tools to understand fairness is a big challenge, Caliskan said. [A Brief History of Artificial Intelligence]

"We don't think that removing bias would necessarily solve these problems, because it's probably going to break the accurate representation of the world," she said.

The new study, published online today (April 12) in the journal Science, is not surprising, said Sorelle Friedler, a computer scientist at Haverford College who was not involved in the research. It is, however, important, she said.

"This is using a standard underlying method that many systems are then built off of," Friedler told Live Science. In other words, biases are likely to infiltrate any AI that uses GloVe, or that learns from human language in general.

Friedler is involved in an emerging field of research called Fairness, Accountability and Transparency in Machine Learning. There are no easy ways to solve these problems, she said. In some cases, programmers might be able to explicitly tell the system to automatically disregard specific stereotypes, she said. In any case involving nuance, humans may need to be looped in to make sure the machine doesn't run amok. The solutions will likely vary, depending on what the AI is designed to do, Caliskan said are they for search applications, for decision making or for something else?

In humans, implicit attitudes actually don't correlate very strongly with explicit attitudes about social groups. Psychologists have argued about why this is: Are people just keeping mum about their prejudices to avoid stigma? Does the IAT not actually measure prejudice that well? But, it appears that people at least have the ability to reason about right and wrong, with their biased associations, Caliskan said. She and her colleagues think humans will need to be involved and programming code will need to be transparent so that people can make value judgments about the fairness of machines.

"In a biased situation, we know how to make the right decision," Caliskan said, "but unfortunately, machines are not self-aware."

Original article on Live Science.

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Bad News: Artificial Intelligence Is Racist, Too - Live Science

Forrester Research says intelligent agents (cognitive marketing, artificial intelligence, machine learning, chatbots, etc.) will eliminate 6% of U.S. jobs by 2021. But machine learning and artificial intelligence (AI) are already making inroads in marketing areas usually reserved for humans.

Is it time to push the panic button? Will marketer jobs as we know them today become obsolete in five, 10 or 15 years?

No on the panic button, but some marketing roles will clearly be at risk in the future.

Artificial intelligence and machine learning will bring significant changes to marketing, but those changes will most likely come through automating repetitive tasks that machines can do more efficiently.

This will free up marketers to work on activities where the human touch is more reliable, such as planning, strategy, context and correction.

What AI means for marketers

You've seen all the hype over self-driving cars, right? Marketing AI is similar. Both autonomous-driving technology for cars and marketing AI will handle the most routine, repeatable tasks -- but humans, at least for the near future, will set and correct the destination and keep their hands on or near the wheel.

A couple of definitions:

Machine learning is moving into the email technology mainstream, as you might have read about in industry publications like MediaPost -- or heard when you attend conferences where early adopter companies and technology vendors have been talking about it for a couple of years.

Subject lines are an excellent proving ground for AI and machine learning, because computers can figure out which combination of words, phrases and images in a subject line works best to meet the goals you set for a specific kind of email.

Machine learning can select content assets in emails for hyper-personalization, such as choosing which offers to send to different segments based on behavior and other data. More than dynamic content, these assets represent what works best for the brand.

Time to start sending out resumes?

Again, not yet.

AI/machine learning will likely take over jobs with these characteristics:

See why subject-line writing is a natural fit? Now, the jobs most at risk:

Even if your job is on this list, you don't need to panic unless it's the only thing you do. But for most marketers, these tasks take up most of their time but contribute the least to job satisfaction.

How much more could you get done if AI took over some or all of those tasks? Maybe you could finally have more time to develop new strategic plans or programs, integrate email with other channels, and more.

Embrace it now; profit from it later

One of my favorite quotes is from German economist Rudi Dornbusch: "In economics, things take longer to happen than you think they will, and then they happen faster than you thought they could."

In other words, these vast changes will probably take longer than we think to change the marketing landscape, but they are coming, and when they take hold -- as with the transformation to mobile-first digital marketing -- it will happen quickly.

Take time to understand the possibilities and get ahead of the changes, and look at your own work to see where you might need to upgrade your own skills so you don't get replaced by a robot.

Until next time, take it up a notch!

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Will Artificial Intelligence Make Email Marketers Obsolete? - MediaPost Communications