Tag Archives: Humans

Nvidia is training robots to learn from watching humans

 Nvidia is training robots to learn from watching humans

Nvidia has developed a method to train robots how to carry out actions by first watching human activity. In initial applications, robots learned to pick up and move colored boxes and a toy car in a lab environment using a Baxter robot.

Learnings from such research will be used to retrain robots and create robots that can work safely alongside people in industrial settings and homes.

“In the manufacturing environment, robots are really good at repeatedly executing the same trajectory over and over again, but they don’t adapt to changes in the environment and they don’t learn their tasks,” Nvidia principal research scientist Stan Birchfield told VentureBeat in an interview.”So to repurpose a robot to execute a new task you have to bring in an expert to reprogram the robot at a fairly low level and its an expensive operation. What we’re interested in doing is making it easier for a non-expert user to teach a robot a new task by simply showing it what to do.”

The system has a series of deep neural networks that perform perception, planning, and control and the networks are trained entirely on synthetic data.

“There’s sort of a paradigm shift happening in the robotics community now,” he said. “We’re at the point now where we can use GPUs to generate essentially a limitless amount of pre-labeled data for free essentially for free to develop and test algorithms and this potentially going to allow us to develop these robotics systems that need to learn how to interact with the world around them in ways that scale better and are safer.”

The findings were shared today at the International Conference on Robotics and Automation (ICRA) taking place this week in Brisbane, Australia.

The new AI system was made with help from the Nvidia robotics research lab. First announced late last year, the lab now has six employees and is preparing to open up offices adjacent to the University of Washington in Seattle this summer.

The research lab will continue to work with the robotics community and in-house at Nvidia to explore the use of synthetic datasets for training AI systems, Nvidia head of robotics research Dieter Fox told VentureBeat.

Such knowledge could be used to strengthen the Isaac SDK, a framework for training robots with simulations first introduced in May 2017.

“Nvidia actually has been working in that domain for quite a while in the gaming context for instance where its all about setting up 3D virtual environments that are photo realistic and give you some kind of content modeling, and what we want to do is also work with all these teams that have all this expertise but help them expand it in a way so that it becomes better applicable in a robotics setting,” Fox said.

Research like the kind released today will be central to the creation of the next generation of robots, Fox said.

“We’re talking about robots that have to open doors, open drawers, pick up objects, move them around, even physically interacting with people, helping them, for example elderly people in the home,” he said. “These robots need to be able to recognize people, they need to see what a person wants to do, they need to learn from people, learn from demonstration for example, and they also need to be able to anticipate what a person wants to do in order to help them.”

Nvidia joins a growing number of companies like Google and SRI International interested in the development of AI systems with a kind of environmental awareness, as Google AI chief Jeff Dean put it, more “common sense.”

To read more about the new robotics research, see this Nvidia blog post.

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Big Data – VentureBeat

Textio expands its AI to help humans craft better recruiting messages

 Textio expands its AI to help humans craft better recruiting messages

Textio, maker of AI-powered tools to augment business writing, today announced a new product to help recruiters reach out to job candidates.

Like the company’s first service, which uses AI to help customers write better job descriptions, Textio’s second offering helps companies write recruiting messages by scoring them on a 100-point scale. It also provides writers with information about how they might want to change their text, including suggestions to avoid pressuring candidates, since that can make people less likely to respond.

Zillow Group, an early customer, saw a 16 percent increase in responses to recruiting messages after implementing the product. Johnson & Johnson recruiters reported a 25 percent increase.

This is the Seattle-based startup’s first new service following its flagship text analysis product for job descriptions. Textio cofounder and CEO Kieran Snyder told VentureBeat in an interview that the company made the move because of its customers’ behavior. A user at Atlassian said the tech company was trying to use Textio to write recruiting mail, something the company saw repeated across its other customers.

“Recruiters write about 100 of these for every job post that’s out there,” Snyder said. “And they’re mostly pretty terrible, so if you get those outreaches, mostly you’re probably not answering them. And so we really started thinking about ‘What if we could attach the platform next to that kind of writing?’”

It’s also an opportunity for the startup to prove that its vision of selling augmented writing services to business users has applications beyond the realm of job descriptions.

While recruiting mail and job descriptions are related, Textio’s job description optimization algorithm offers different feedback from the one used for recruiting mail, with each focusing on the factors most likely to affect the desired outcome.

For example, the job description algorithm tends to emphasize bullet points, since optimizing those can have a significant impact on how well a post performs. But they don’t matter as much to recruiting mail, where requesting responses from recipients is more important.

Customers can purchase the recruiting mail analysis service separately or as part of the Textio Hire bundle, which includes the company’s job description service. Textio negotiates a price for its software with individual customers, so there’s no one-size-fits-all pricing chart. However, the cost of Textio Hire should be lower than that of purchasing the two services independently.

In the future, Textio plans to tackle other applications — like sales outreach — where its AI writing assistance can provide concrete benefits to business applications.

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When Humans Fumble, HA/DR Recovers

Vision Solutions, which recently merged with Syncsort, has been in the business of high availability and disaster recovery for more than two decades. The article below about the value of HA/DR solutions originally appeared on their blog.

Last year, an IT failure threw a major airline carrier into chaos, as flight cancellations affected hundreds of thousands of customers and caused the airline to suffer an embarrassing PR nightmare. Systems and operations have since recovered, but the lessons learned from it reverberate for the many businesses and organizations that remain vulnerable to the cause of this issue: human error.

Despite technology’s rapid evolution, there still isn’t a software or hardware solution that can protect against a well-intentioned administrator mistyping a command or a trained and talented engineer disconnecting power when not supposed to. And when human error accounts for 22 percent of data center outages—by far the primary cause of downtime—the best protection against these on-the-job missteps is reliable recovery should an event occur.

The goal of any good high availability and disaster recovery program is to keep a replicated, in-sync version of a production server and its data always ready in case of a site or server outage or human error. That way, business can continue as normal with little to no downtime. For the major airline, that would have meant no canceled flights, no piles of lost luggage, and no news stories like this. What would industry-leading HA/DR mean for you? Protected brand reputation? Customer retention? Safeguards against loss of critical data? Cost savings? For many businesses, the answer is all of the above—and more!

Download our 2018 State of Resilience Report to review trends in HA/DR, IT security, migration and more!

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Syncsort + Trillium Software Blog

Is there a future for humans?

Lurking beneath the fear of artificial intelligence and automation threatening people’s jobs lies a deeper, far more profound threat. Do artificial intelligence and automation imperil humanity itself?

Those predicting a dystopian future include Elon Musk, Bill Gates, Stephen Hawking, and many others. For some of them, it’s only a matter of time before the prophecy of Yuval Noah Harari’s great book, Homo Deus: A Brief History of Tomorrow, comes to pass. The bleak vision: a world where a small group of humans control machines, which in turn control the rest of humanity.

Meanwhile, there are others who, even while feeling blindsided by the rapid development of AI, see the potential for a bright future. “The revolution in deep nets has been very profound, it definitely surprised me, even though I was sitting right there,” said Google cofounder Sergey Brin at the World Economic Forum in January. “What can these things do? We don’t really know the limits,” he said. “It has incredible possibilities. I think it’s impossible to forecast accurately.”

And there’s plenty to be optimistic about. Already AI, automation and other digital technologies are helping realize everything from medical breakthroughs to increased economic productivity to self-driving cars. Yet for alarmists, these activities are rendering humans the metaphorical equivalents of frogs inside a pot of water on the stove, unaware that the water is getting warm.

Wading into this controversy are Stephen Wolfram, founder of Wolfram Research, and Irwin Gotlieb, chairman of GroupM. Speaking with VentureBeat editor in chief Blaise Zerega onstage at Collision in New Orleans, the pair voiced carefully reasoned, but very different, approaches to the issue in a session titled “Is there a future for humans?” (Watch video above.)

Wolfram explained that the current kerfuffle around AI is really just a continuation of the way technology helps humans by taking on tasks so that we no longer have to do them. “If there’s one thing that has advanced throughout history, it’s technology,” he said. “The question then is: What is it that humans still have to do?” In his view, we’re rapidly getting to a place where humans will be setting goals and then turning to technology to achieve them, as automatically as possible.

 Is there a future for humans?

Above: Stephen Wolfram, founder of Wolfram Research, asked, “What is it that humans still have to do?”

“When people ask what’s the space left for the humans,” Wolfram said, “the figuring out of what do is the kind of quintessential human piece.”

Gotlieb agreed with Wolfram on this principle — to a point. “I am much more fearful than Stephen,” he countered. “All of a sudden, problems that we thought we had two decades to deal with, we’re going to be facing them much, much more quickly.” He explained that the nerd in him welcomed the advances wrought by AI, but at the same time, “there’s a little voice in the back of my head that’s saying the dystopian outcome is perhaps more likely.”

 Is there a future for humans?

Above: Irwin Gotlieb, chairman of GroupM, said, “There’s a little voice in the back of my head that’s saying the dystopian outcome is perhaps more likely.”

The two discussed ways rapid technological advances were accelerating income inequality, societal changes, and job losses, as well as the need for collective action to better understand and even regulate the ways AI might be used in future. For instance, science fiction writer Isaac Asimov’s three rules of robotics have held up so far, and more recently, Wolfram has described the need for an AI Constitution.

And yet when the conversation took a turn towards ethics and humanity’s general quest for meaning, the importance of human judgment rose to the surface. Gotlieb raised the scenario of one AI-car carrying one passenger and another carrying several passengers; if only one vehicle could be saved, how would an AI system determine a response?

“At the moment there isn’t one solution for the world, and different parties will put different rule sets against it, with different objectives,” Gotlieb said.

“This question of ‘Can we invent one perfect set of mathematical principles that will determine the AIs for all eternity?’ — the answer, I think, is no,” Wolfram said. “In the longer future, we’re being asked to look at ourselves and ask: What is the essence of humanity? If we could define an AI Constitution, what would we want it to say?”

On this they agreed: The future for humans is up to, well, us humans.

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AI Weekly: Microsoft chases Amazon, Toyota taps Nvidia, humans brace for dystopia

Here’s this week’s newsletter:

This week, Amazon and Microsoft launched new attacks in the intelligent assistant wars.

On Tuesday, Amazon added a touchscreen to its Echo device and introduced calls and messaging. (This Sunday, don’t forget to say, “Alexa, call Mom.”)

And yesterday at the Build conference, Microsoft upped its ante by releasing a Cortana Skills Kit for developers and launching 26 new voice apps. Despite these salvos, as our Khari Johnson writes, Google Assistant has more than 230 actions from third-party developers. Amazon, which opened its Alexa Skills Kit to developers back in 2015, passed 10,000 skills three months ago.

Microsoft has some catching up to do.

Meanwhile, those who fear an AI-powered future may see these developments as more evidence that tech companies are like children playing catch with knives. Stephen Wolfram of Wolfram Research and Irwin Gotlieb of GroupM confronted the utopian and dystopian views of this issue at Collision 2017. Even as he welcomes technological advancements, Gotlieb warned, “There’s a little voice in the back of my head that’s saying the dystopian outcome is perhaps more likely.” (Watch the video below.)

For AI coverage, send news tips to Khari Johnson and guest post submissions to John Brandon. Please be sure to visit our AI Channel.

Thanks for reading,
Blaise Zerega
Editor in Chief

P.S. Please enjoy this video from Collision, “Is there a future for humans?”

 AI Weekly: Microsoft chases Amazon, Toyota taps Nvidia, humans brace for dystopia

Is there a future for humans?

Lurking beneath the fear of artificial intelligence and automation threatening people’s jobs lies a deeper, far more profound threat. Do artificial intelligence and automation imperil humanity itself? Those predicting a dystopian future include Elon Musk, Bill Gates, Stephen Hawking, and many others. For some of them, it’s only a matter of time before the prophecy of Yuval Noah Harari’s […]

Read the full story

 AI Weekly: Microsoft chases Amazon, Toyota taps Nvidia, humans brace for dystopia

Toyota is using Nvidia’s car supercomputer for its autonomous vehicles

Nvidia CEO Jen-Hsun Huang announced that Toyota will use Nvidia’s Drive PX supercomputers for autonomous vehicles. Those cars will debut in the market in the next few years, Huang said. The Drive PX uses a new processor dubbed Xavier, which can do 30 trillion operations per second in a deep learning application while burning just […]

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 AI Weekly: Microsoft chases Amazon, Toyota taps Nvidia, humans brace for dystopia

Microsoft releases new custom AI services for businesses

Four customizable artificial intelligence services were made available today from Microsoft Cognitive Services to enable businesses and developers to create AI. New AI from Microsoft includes: Custom Search to make search with your own terms Custom Vision Service to let developers create computer vision with just a few dozen of their own images Custom Decision Service to focus on A/B […]

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 AI Weekly: Microsoft chases Amazon, Toyota taps Nvidia, humans brace for dystopia

Facebook is using AI to hide spammy posts from your News Feed

Facebook has announced plans to reduce the number of “low-quality web page experiences” from users’ News Feeds. More specifically, the target of the company’s latest effort to prevent users from ditching the social network are “misleading, sensational and spammy” posts that encourage people to click, only to disappoint through offering little in the way of […]

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 AI Weekly: Microsoft chases Amazon, Toyota taps Nvidia, humans brace for dystopiaAmazon unveils $ 230 touchscreen Echo Show

Amazon’s latest smart speaker is a touchscreen device that looks like a modern cross between Apple’s Twentieth Anniversary Macintosh and a ’90s kitchen TV. Amazon is calling it the Echo Show, and it’ll cost you $ 230 when it launches on June 28. Amazon has boasted about its Echo smart speakers for years, though the company won’t actually say how […]

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 AI Weekly: Microsoft chases Amazon, Toyota taps Nvidia, humans brace for dystopia

AI that detects sarcasm and irony? Perfect  AI Weekly: Microsoft chases Amazon, Toyota taps Nvidia, humans brace for dystopia

Increasingly, companies are turning to artificial intelligence to understand what people say about their products and services on Twitter or Facebook. The goal is to react more quickly to complainers and perhaps sell more stuff to happy customers. But with sarcasm, there is a big gap between what people say and what they mean. And, […]

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Beyond VB

The meaning of life in a world without work

As technology renders jobs obsolete, what will keep us busy? Sapiens author Yuval Noah Harari examines ‘the useless class’ and a new quest for purpose (via The Guardian)

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This Robot Completes a 2-Hour Brain Surgery Procedure in Just 2.5 Minutes

Researchers believe their surgery-assisting robot is capable of performing complex brain surgeries. The machine can reduce the time of surgeries by cutting down the time it takes to cut into the skull from two hours to two and a half minutes. (via Futurism)

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IBM’s Watson ‘is a joke,’ says Social Capital CEO Palihapitiya

IBM isn’t at the forefront of artificial intelligence, Social Capital CEO and founder Chamath Palihapitiya told CNBC on Monday, and he certainly isn’t a fan of IBM’s Watson. “Watson is a joke, just to be completely honest,” he said in an interview with “Closing Bell” on the sidelines of the Sohn Investment Conference in New York. (via CNBC)

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AI Is the Future of Cybersecurity, for Better and for Worse

In the near future, as artificial intelligence (AI) systems become more capable, we will begin to see more automated and increasingly sophisticated social engineering attacks. The rise of AI-enabled cyberattacks is expected to cause an explosion of network penetrations, personal data thefts, and an epidemic-level spread of intelligent computer viruses. Ironically, our best hope to defend against AI-enabled hacking is by using AI. But this is very likely to lead to an AI arms race, the consequences of which may be very troubling in the long term, especially as big government actors join the cyber wars. (via Harvard Business Review)

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Big Data – VentureBeat

Hungry hungry humans



 Hungry hungry humans

About Krisgo

I’m a mom, that has worn many different hats in this life; from scout leader, camp craft teacher, parents group president, colorguard coach, member of the community band, stay-at-home-mom to full time worker, I’ve done it all– almost! I still love learning new things, especially creating and cooking. Most of all I love to laugh! Thanks for visiting – come back soon icon smile Hungry hungry humans

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Deep Fried Bits

How machine learning can make humans better managers

shutterstock 501705940 780x780 How machine learning can make humans better managers

Machine learning is rapidly infiltrating today’s workplace, in businesses of all shapes, sizes and industries — and it’s here to stay. In fact, so far in 2016 over 200 AI-focused companies have raised nearly $ 1.5 billion in funding, and equity deals to startups in AI increased 6x from roughly 70 in 2011 to almost 400 in 2015.

Large companies like Google, Microsoft, and Amazon have already begun to build their machine learning capabilities to handle large data sets and recognize patterns. But an area that machine learning hasn’t yet fully infiltrated — and one that it has the ability to transform — is people management. In fact, 55 percent of organizations still report being weak at using HR data to predict workforce performance and improvement.

Especially as more companies begin to ride this new wave of machine learning, it won’t be long before they’re leveraging their machine learning capabilities to transform ineffective people management processes. The benefits are twofold: First, machine learning has the ability to eliminate inherent workplace biases; and second, it can help prompt managers to provide the right feedback and recognition to the right employees, helping maintain a positive culture and retain good employees.

Problems with people management today

More than half of executives today believe that their current performance management approach is not effective in driving employee engagement or high performance. Additionally, managers account for at least 70 percent of the variance in employee engagement scores across business units. Only 30 percent of U.S. workers are engaged, demonstrating a clear link between poor managing and a nation of “checked out” employees.

In addition to those challenges, inherent biases linked to gender, age, well-liked employees, and more continue to be a huge problem in the workplace. Earlier this year, it was reported that companies need to make retention of female employees a priority, especially with 56 percent of women in computing jobs leaving their positions at the “mid-level” point. Additionally, women are 20 percent less likely than men to say they get management feedback that helps them improve their performance.

Making humans better managers

With the use of machine learning, companies can ensure that these biases in the workplace, whether inherent or on purpose, are eliminated. Machine learning already has the opportunity to make an impact on people management. Companies like Accenture, SAP, and Deloitte are trading in their traditional performance management ratings and rankings systems for technologies that bring transparency to data around the work employees do. This creates huge opportunities to leverage data to provide the right assessment of employees and get a holistic picture of what’s driving work. As this data surfaces, so does the ability to apply machine learning to turn managers into effective coaches.

In today’s work environment, managers seldom focus their energies on coaching employees continuously. Yet feedback and recognition are most effective when they’re given instantly with appropriate context and specificity. Waiting until the end of the year introduces different biases focusing on recent wins or only well-liked employees. Instead of basing these things on the personal biases of a manager, a machine learning tool that collects data surrounding an employee’s actual work prompts managers to give that constructive criticism or praise when it’s relevant and warranted.

Machine learning can help humans become better managers by removing any biases a manager might have. With machine learning, employee performance is backed up by raw, inarguable data that shows how employees are actually performing. By taking advantage of this rich repository of data, managers can better recognize which employees are achieving important goals. In turn, they can provide appropriate feedback without relying on their personal opinions.

With its ability to eliminate bias and prompt a data-driven approach to feedback and recognition from managers, machine learning can completely transform the workplace by making coming to work an engaging experience for every employee — no matter their age, race or gender. Employees shouldn’t have to worry about the personal biases of their managers. Instead, they should focus on progressing toward their goals and improving their work. On the flip side, managers should focus on giving better feedback and recognition to guide employees toward success. People management needs to keep up with today’s fast-paced digital age, and with help from machine learning technology to make humans better managers, it won’t get left behind.

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Merging Humans with Enterprise AI and Machine Learning Systems

  1. In Defense of the Human Experience in a Digital World
  2. Profits that Kill in the Age of Digital Transformation
  3. Competing in Future Time and Digital Transformation
  4. Digital Hope and Redemption in the Digital Age
  5. Digital Transformation and the Role of Faster
  6. Digital Transformation and the Law of Thermodynamics
  7. Jettison the Heavy Baggage and Digitally Tranform
  8. Digital Transformation – The Dark Side
  9. Business is Not as Usual in Digital Transformation
  10. 15 Rules for Winning in Digital Transformation
  11. The End Goal of Digital Transformation
  12. Digital Transformation and the Ignorance Penalty
  13. Surviving the Three Ages of Digital Transformation
  14. From Digital to Hyper-Transformation
  15. Believers, Non-Believers and Digital Transformation
  16. Forces Driving the Digital Transformation Era
  17. Digital Transformation Requires Agility and Energy Measurement
  18. A Doctrine for Digital Transformation is Required
  19. The Advantages of Advantage in Digital Transformation
  20. Digital Transformation and Its Role in Mobility and Competition
  21. Digital Transformation – A Revolution in Precision Through IoT, Analytics and Mobility
  22. Competing in Digital Transformation and Mobility
  23. Ambiguity and Digital Transformation
  24. Digital Transformation and Mobility – Macro-Forces and Timing
  25. Mobile and IoT Technologies are Inside the Curve of Human Time


Kevin Benedict
Senior Analyst, Center for the Future of Work, Cognizant Writer, Speaker and World Traveler
View my profile on LinkedIn
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Subscribe to Kevin’sYouTube Channel
Join the Linkedin Group Strategic Enterprise Mobility
Join the Google+ Community Mobile Enterprise Strategies

***Full Disclosure: These are my personal opinions. No company is silly enough to claim them. I am a mobility and digital transformation analyst, consultant and writer. I work with and have worked with many of the companies mentioned in my articles.

Aviso CEO K.V. Rao: Humans, Machines and Magic

K.V. Rao is CEO of Aviso.

In this exclusive interview, CRM Buyer discusses with Rao the future of predictive intelligence.

83994 300x300 Aviso CEO K.V. Rao: Humans, Machines and Magic

Aviso CEO K.V. Rao

CRM Buyer: How does predictive analytics help with both sales and CRM?

Rao: The most important thing with predictive analytics is to not look at sales, but other areas. CRM is a mature industry, but it’s fundamentally not moving the needle for the user, for the subscriber — the sales rep or manager. It’s fundamentally recording your activities, but it doesn’t really let you commit with confidence to drive the right outcomes.

That’s what predictive analytics can do. It can help you find the right content faster. That’s what the state-of-the art is for sales. You’re trying to understand what deals to work on to meet your target and make money, and that’s what predictive analytics can do.

CRM Buyer: How do you see the relationship between sales and CRM?

Rao: We need data, and we are data junkies. The data comes from CRM and email and social media, and what predictive analytics does is to discern patterns in data — and that’s where it’s very powerful. If you’re selling more, you’re better relating with your customers.

It’s not just sales. It’s the relationships that you’re building. Those are far more powerful, whether it’s cutting revenue streams, cross-selling, building longer relationships, or driving more efficient revenue growth. Predictive analytics is helpful for new business, but the same principle applies for finding which customers are not happy. You can rank and look for patterns.

CRM Buyer: Is there still a need for people in this world of predictive analytics and machine learning?

Rao: The magic happens with humans. With all the work going in AI, humans are still the ones that make magic happen. You want to use machines to do more magic, and machines help you to put your emotions in check. Emotions can be powerful, but they might let you go astray, so predictive analytics strengthens the positive aspects of the emotions and dampens the negative bias.

If it’s used right, it will reinforce your intuition and judgment and expertise — tell you that yes, you are on the right track. Or it can temper your response and ask, “Are you sure you want to go down that path?”

CRM Buyer: What is the secret to analyzing data accurately?

Rao: How do you get adoption? You need to have confidence in the tools. How do you build confidence? Confidence comes when you can verify what the machine is telling you is indeed reliable. That is one part of making data science useful. If Netflix keeps recommending horror movies and you hate horror movies, you’ll lose confidence.

The other side that is different from consumer applications is that organizations are all about well-established business processes. You need to make it part of the workflow — not something different or new or alien. A key part of success is not making it exotic or new. You don’t want it to be a shiny object that’s put on a shelf. You want it to be key to the enterprise workflow.

CRM Buyer: Why are what-if scenarios important?

Rao: The thing that makes sales more interesting and challenging than any other field is the uncertainty. Everything is not in your control. There are competitors and prospects. Given those uncertainties, you can’t just say I know my plan, and I’ll stick to it.

You need to deal with these uncertainties in time in a sales environment. What are my backup deals that I can work on to make up that gap? You’re always doing contingency planning, and that’s an important capability.

CRM Buyer: What is the secret to identifying and prioritizing key deals?

Rao: Having an unbiased view into the risks of the deals. We call them “uncertainties.” How can you measure those? You can measure the risks and opportunities against another. If you have an unbiased view, then you can make a smart decision.

CRM Buyer: What’s in the future for predictive intelligence? Are there limits to what can and will be predictable?

Rao: It is moving things to a higher level of performance in organizations. Everything is getting compressed in our space, and to be competitive and to move at faster speeds, predictive analytics is becoming a must-have in the enterprise.

CRM Buyer: How does all of this ultimately tie back to CRM?

Rao: With customers, you can start giving alerts when you think a customer is at risk. You’re trying to build a relationship and maintain it. The squeaky wheel always gets the most attention. If this customer is complaining a lot, maybe that customer is at risk for attrition.

The machine might come and say this customer is actually a very demanding customer, but they’re not really at risk. You don’t have to give a discount. You can maintain your margins and also retain your customer.

The unbiased view is key. That’s all machines can do. The value is that emotions can have bias. Machines can remove that bias and help drive better performance.

CRM Buyer: So it’s like Spock.

Rao: Exactly. Captain Kirk is amazing, but without Spock things can be catastrophic. end enn Aviso CEO K.V. Rao: Humans, Machines and Magic

Freelance writer Vivian Wagner has wide-ranging interests, from technology and business to music and motorcycles. She writes features regularly for ECT News Network, and her work has also appeared in American Profile, Bluegrass Unlimited, and many other publications. For more about her, visit her website. You can also connect with Vivian on Google+.

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CRM Buyer

Machines are becoming more creative than humans

Can machines be creative? Recent successes in AI have shown that machines can now perform at human levels in many tasks that, just a few years ago, were considered to be decades away, like driving cars, understanding spoken language, and recognizing objects. But these are all tasks where we know what needs to be done, and the machine is just imitating us. What about tasks where the right answers are not known? Can machines be programmed to find solutions on their own, and perhaps even come up with creative solutions that humans would find difficult?

The answer is a definite yes! There are branches of AI focused precisely on this challenge, including evolutionary computation and reinforcement learning. Like the popular deep learning methods, which are responsible for many of the recent AI successes, these branches of AI have benefitted from the million-fold increase in computing power we’ve seen over the last two decades. There are now antennas in spacecraft so complex they could only be designed through computational evolution. There are game playing agents in Othello, Backgammon, and most recently in Go that have learned to play at the level of the best humans, and in the case of AlphaGo, even beyond the ability of the best humans. There are non-player characters in Unreal Tournament that have evolved to be indistinguishable from humans, thereby passing the Turing test— at least for game bots. And in finance, there are computational traders in the stock market evolved to make real money.

Passing the Turing test for video games: The AI is indistinguishable from human players.

These AI agents are different from those commonly seen in robotics, vision, and speech processing in that they were not taught to perform specific actions. Instead, they learned the best behaviors on their own by exploring possible behaviors and determining which ones lead to the best outcomes. Many such methods are modeled after similar adaptation in biology. For instance, evolutionary computation takes concepts from biological evolution. The idea is to encode candidate solutions (such as videogame players) in such a way that it is possible to recombine and mutate them to get new solutions. Then, given a large population of candidates with enough variation, a parallel search method is run to find a candidate that actually solves the problem. The most promising candidates are selected for mutation and recombination in order to construct even better candidates as offspring. In this manner, only an extremely tiny fraction of the entire group of possible candidates needs to be searched to find one that actually solves the problem, e.g. plays the game really well.

We can apply the same approach to many domains where it is possible to evaluate the quality of candidates computationally. It applies to many design domains, including the design of the space antenna mentioned above, the design of a control system for a finless rocket, or the design of a multilegged, walking robot. Often evolution comes up with solutions that are truly unexpected but still effective — in other words, creative. For instance, when working on a controller that would navigate a robotic arm around obstacles, we accidentally disabled its main motor. It could no longer reach targets far away, because it could not turn around its vertical axis. What the controller evolved to do instead was slowly turn the arm away from the target, using its remaining motors, and then swing it back really hard, turning the whole robot towards the target through inertia!

The most recent and, in my opinion, the most exciting research in this field focuses on computational design creativity head on. One idea that has emerged, again modeled after biology, is that evolutionary computation should not be set to optimize a particular design objective but instead should be set to simply discover solutions that are novel. Many difficult problems are deceptive — if you try to solve them by making incremental improvements, you will get stuck. Novelty search instead discovers stepping stones, such as candidates that may not perform well but exhibit a highly unique approach. Often a truly creative solution can be found by combining the novel features of several candidates into a single solution that works. For example, it is possible to evolve a fast walking gait for a bipedal robot not by trying to incrementally walk faster and faster but by allowing it to fall on its face as fast and hard as possible and then evolving a way to postpone the fall by taking steps.

Many new applications have suddenly come within our reach thanks to computational creativity — even though most of us do not realize it yet. If you are facing a design problem where potential solutions can be tested automatically, chances are you could evolve those solutions automatically as well. In areas where computers are already used to draft designs, the natural next step is to harness evolutionary search. This will allow human designers to gain more traction for their ideas, such as machine parts that are easier to manufacture, stock portfolios that minimize risk, or websites that result in more conversions. In other areas, it may take some engineering effort to define the design problem for the computer, but the effort may be rewarded by truly novel designs, such as finless rockets, new video game genres, personalized preventive medicine, and safer and more efficient traffic.

And with all that time saved, we humans will have more time for creative pursuits of our own.

Risto Miikkulainen is a Professor of Computer Science and Neuroscience at the University of Texas at Austin and a neuroevolution pioneer. He is also a fellow at AI startup Sentient Technologies.

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