Monthly Archives: August 2017

By Doug Randall, CEO, Protagonist

At a recent meeting with U.S. governors, Elon Musk made some hefty criticisms of artificial intelligence. When an interviewer jokingly asked whether we should be afraid of robots taking our jobs, Musk, not jokingly, replied, AI is a fundamental risk to the existence of human civilization. Those are serious words, from a very influential thinker.

Narratives about AI are buzzing. Some, like Musk, have vocalized concerns over the regulation of AI and its impact on human jobs. The vast majority of industry leaders have been bombastic about the wonders of the technology, while dismissing the criticisms. Eric Schmidt of Alphabet said:

Youd have to convince yourself that a declining workforce and an ever-increasing idle force, the sum of that wont generate more demand. Thats roughly the argument that you have to make. Thats never been truein order to believe its different now, you have to believe that humans are not adaptable, that theyre not creative.

That confidence might not resonate with those who are warier of the threats of AI--a population significant population that is underrepresented in Silicon Valley leadership. Protagonist recently analyzed hundreds of thousands of conversations around AI using our Narrative Analytics platform. Most of what we found was positive techno-friendly Narratives, but there are also very real, deeply held beliefs about AI as a threat to humanity, human jobs and human privacy that need to be addressed.

Most companies in the AI space can readily allow that Narratives like robots will take our jobs or AI is a threat to humanity exist. What they dont know is how much that Narrative rests with their target audience or whether those Narratives are being applied to their own brand. Its often more than they think. Elon Musk is far from alone in his distrust.

As of last year, 10 percent of Americans considered AI a threat to humanity and six percent considered it a threat to jobs, though the former number was declining and the latter was rising. With that in mind, businesses should be aware of the very real risks of being labelled a job-killer. That Narrative might not be as broadly held as some others, but is one of the most emotionally evocative. Fear and anger over outsourced or inaccessible work opportunities played a major role in the last presidential election. Its clearly a topic that resonates with people on a deep level, and if it dictated their vote, it will dictate their feelings about a company.

Our analysis revealed a cautionary finding: the less tech-friendly Narratives about AI have significantly higher levels of engagement than the more positive tropes. That means theyre more likely to spread quickly once they are triggered.

In todays world it doesnt matter whether specific types of AI present a real threat to human jobs or privacy; if theyre perceived to be dangerous the businesses behind them could be in real trouble. Negative affiliations could result in anything from investor slowdown, to active boycotts to slowed adoption during critical growth periods.

In Silicon Valley and tech markets, growth rates are particularly important and AI companies often experience surges of enthusiastic early adopters. When AI companies are strategizing for continued growth and allocating resources, they also need to think about how adoption trends might change when their product reaches the broader market. Negative narratives could create significanteven damningheadwinds if they arent accounted for and addressed directly.

So what can businesses implementing AI do? First, understand the Narrative landscape, then take action by addressing negative beliefs head-on. Of the seven percent of Americans who fear AI is feeding the surveillance machine, most are in finance, marketing or healthcare. So businesses looking to sell into those fields should emphasize privacy in their marketing. Companies worried about being affiliated with job disenfranchisement should advocate ways they create opportunities.

Its okay to relish in the excitement of innovation; 69 percent of the the mentality around artificial intelligence is positive: its rich, its exciting, its transforming business as we know it. AI-using companies can and should participate in that shared glow. They just cant ignore or laugh off those other Narratives as they do so. Especially with people like Musk chiming in.

Dougis Founder & CEO ofProtagonistwhich is a high growth Narrative Analytics company.Protagonistmines beliefs in order to energize brands, win narrative battles, and understand target audiences.

Protagonistuses natural language processing, machine learning, and deep human expertise to identify, measure, and shape narratives.Doughas lectured on a number of topics at the Wharton School, Stanford University, and National Defense University; his articles on future technology trends have appeared in the Financial Times, Wired, and Business 2.0. He was previously a partner at Monitor, founder of Monitor 360 and co-head of the consulting practice at Global Business Network (GBN). Before that, he was a Vice President at Snapfish, a senior consultant at Decision Strategies, Inc., and a senior research fellow at the Wharton School.

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AI Vs. The Narrative of the Robot Job-Stealers - HuffPost

On Aug. 7, semiconductor, sensor,and integrated circuits makerON Semiconductor (NASDAQ: ON) reported second-quarter 2017 results that exceeded management's guidance. Later in the day, I caught up with ON's David Somo -- the company's vice president of corporate strategy and marketing -- to talk about the industry, where things are headed, and what ON is doing to grow its piece of the pie.

David Somo, ON Semiconductor's senior vice president of corporate strategy and marketing. Image source: ON Semiconductor.

ON Semiconductor is often lumped in with what has been dubbed the Internet of Things (IoT) movement, an oft-used term that has become almost generic. What is it exactly?

At the highest level, Somo defines it as "a way to bridge the physical and digital worlds with intelligent technology." Digging a little deeper, this essentially refers to machines and systems that are aware of their environment and deliver data to help improve our lives.

For ON, the rubber meets the road with the devices themselves, like the companies IoT Development Kit -- a hardware package with supporting software designed to help engineers quickly develop a range of devices from smart-home to industrial applications. The "out-of-the-box ready-to-deploy" system recently won the IoT Evolution Product of the Year award from IoT Evolution magazine.

With so many devices coming online, the industry is all about making development and testing of connected systems a quick process. The easier the kit is to use and the more comprehensive its coverage of the connected industry overall, the more likely an engineer is to do business with ON. Speaking to the importance of this, Somo had this to say:

Asthe market environment and application needs changed, we've evolved our business model to go from components, to modules, to more platforms. We are into systems enablement with the components and modules we offer. As devices become intelligent and need to be connected ... semiconductor companies are stepping up in applications capabilities, and ON Semiconductor is certainly trying to lead in this area to offer an out-of-the-box type of solution for customers to jump-start their development.

ON has been aggressively moving into connected automobiles and other industrial applications in recent years, building out a portfolio of differentiated products to capture more share of client's end systems.

To that end, artificial intelligence (AI) has begun to enter the equation for ON. Another buzzword in the tech industry, Somo defines it simply as a device gathering environmental data, looking for patterns in that data, and continuously learning from those patterns.

Perhaps the most visible part of the AI movement is with digital assistants like the Amazon (NASDAQ: AMZN) Echo, but the technology is getting applied mostly outside of consumer markets. Data center management, industrial robotics, and autonomous vehicles are some notable examples, Somo said.

AI is typically the realm of tech companies like Intel (NASDAQ: INTC) and NVIDIA (NASDAQ: NVDA), which provide the processing horsepower, but ON has found that the massive amounts of data being sent to the brain of a system like an autonomous-driving car or an industrial robot affects efficiency.

For example, in an autonomous car, the processor has to continuously parse through and make decisions on information coming from a dozen different sensors, including radar (object detection using radio waves) and lidar (object detection using light imaging) sensors. All that data transfer bogs down not just the main processor, but also the connections in the car carrying the data.

To help speed things up, Somo said, ON is building a single processor unit into its sensors to do some front-end decision making before sending relevant data to the main brain. That helps free up processing power, but more importantly it reduces the amount of bandwidth being consumed in the internal system of the car itself. That helps reduce the lag time as information is sent back and forth from the peripheral device to the brain of the car, a critical process for safety of the vehicle and its passengers.

Image source: Getty Images.

Estimates for the number of connected devices in operation by the end of this decade are all over the board, with some saying as many as 50 billion devices compared to about 8 billion now. Somo said ON tends to think in the 25 billion-to 30 billion-connection range by 2020, but it's almost impossible to say for sure.

The one thing that is for certain is that the opportunity is huge and there is plenty of new business to go around. ON provides a lot of detail on what industries are paying the bills, and currently the automotive and industrial sectors make up just shy of 60% of revenues. I asked Somo if that number will be consistent in the years ahead, or if ON will expand its presence into other areas.

I think automotive and industrial both have long legs under them, thinking about the megatrends that are there. Take automotive, there are four key megatrends, two of which we play in strongly and one we have some play in. The first is autonomous vehicles where you're building higher levels of autonomy ... we have a couple decades of runway to get there in capabilities.

Electric vehicles, we're hearing a lot of noise from companies like Volvo or Subaru, or like Audi and BMW all talking about a healthy percentage of their vehicle fleets are going to be electric motors by 2025.

The third megatrend is connected vehicles. We participate inside the vehicles, so connecting everything inside the vehicle, whether it's wired or wireless like Bluetooth. But there's also outside the vehicle as well, vehicle-to-vehicle, or vehicle-to-grid, and that's going to be more 5G-technology enabled.

The final area is in mobility services around autonomous vehicles. We really aren't quite at that level from an ecosystem vantage point. There is significant competition taking place there ... between the traditional automotive manufacturers andthe ride sharing services like Uber and Lyft, that are competing for how that is going to work over time.

The answer in short isthe company is always looking for new growth opportunities, but likes its current breakdown. Somo sees much connected device growth happening in theindustrial economy for years to come, providing plenty of opportunity.

Even though connected devices are booming, Somo said in ON's view the industry is well-balanced from a supply and demand perspective. Often fraught with periods of boom and bust as supply and demand changes, the company sees things as being neither hot nor cold but just right.Persisting balance betweensupply and demand means steady growth without substantial risk of an industry crash. That's good for all IoT companies, and is a good situation for ON as it continues to expand on its role as a device and connected systems supplier.

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What ON Semiconductor Thinks About the IoT, AI, and the Future of Tech Development - Madison.com

Beware the hyperbole surrounding artificial intelligence and how far it has progressed.

Great claims are being made for artificial intelligence, or AI, these days.

Amazon's Alexa, Google's assistant, Apple's Siri, Microsoft's Cortana: these are all cited as examples of AI. Yet speech recognition is hardly new: we have seen steady improvements in commercial software like Dragon for 20 years.

Recently we have seen a series of claims that AI, with new breakthroughs like "deep learning", could displace 2 million or more Australian workers from their jobs by 2030.

Similar claims have been made before.

I was fortunate to discuss AI with a philosopher, Julius Kovesi, in the 1970s as I led the team that eventually developed sheep-shearing robots. With great insight, he argued that robots, in essence, were built on similar principles to common toilet cisterns and were nothing more than simple automatons.

"Show me a robot that deliberately tells you a lie to manipulate your behaviour, and then I will accept you have artificial intelligence!" he exclaimed.

That's the last thing we wanted in a sheep-shearing robot, of course.

To understand future prospects, it's helpful to see AI as just another way of programming digital computers. That's all it is, for the time being.

We have been learning to live with computers for many decades. Gradually, we are all becoming more dependent on them and they are getting easier to use. Smartphones are a good example.

Our jobs have changed as a result, and will continue to change.

Smartphones can also disrupt sleep and social lives, but so can many other things too. Therefore, claims that we are now at "a convergence" where AI is going to fundamentally change everything are hard to accept.

We have seen several surges in AI hyperbole. In the 1960s, machine translation of natural language was "just two or three years away". And we still have a long way to go with that one. In the late 1970s and early 1980s, many believed forecasts that 95 per cent of factory jobs would be eliminated by the mid-1990s. And we still have a long way to go with that one too. The "dot com, dot gone" boom of 2001 saw another surge. Disappointment followed each time as claims faded in the light of reality. And it will happen again.

Self-driving cars will soon be on our streets, thanks to decades of painstaking advances in sensor technology, computer hardware and software engineering. They will drive rather slowly at first, but will steadily improve with time. You can call this AI if you like, but it does not change anything fundamental.

The real casualty in all this hysteria is our appreciation of human intelligences ... plural. For artificial intelligence has only replicated performances like masterful game playing and mathematical theorem proving, or even legal and medical deduction. These are performances we associate with intelligent people.

Consider performances easily mastered by people we think of as the least intelligent, like figuring out what is and is not safe to sit on, or telling jokes. Cognitive scientists are still struggling to comprehend how we could begin to replicate these performances.

Even animal intelligence defies us, as we realised when MIT scientists perfected an artificial dog's nose sensitive enough to detect TNT vapour from buried landmines. When tested in a real minefield, this device detected TNT everywhere and the readings appeared to be unrelated to the actual locations of the mines. Yet trained mine detection dogs could locate the mines in a matter of minutes.

To appreciate this in a more familiar setting, imagine a party in a crowded room. One person lights up a cigarette and, to avoid being ostracised, keeps it hidden in an ashtray under a chair. Everyone in the room soon smells the cigarette smoke but no one can sense where it's coming from. Yet a trained dog would find it in seconds.

There is speculation that quantum computers might one day provide a real breakthrough in AI. At the moment, however, experiments with quantum computers are at much the same stage as Alan Turing was when he started tinkering with relays in the 1920s. There's still a long way to go before we will know whether these machines will tell deliberate lies.

In the meantime it might be worth asking whether the current surge of interest in AI is being promoted by companies like Google and Facebook in a deliberate attempt to seduce investors. Then again, it might just be another instance of self-deception group-think.

James Trevelyan is emeritus professor in the School of Mechanical and Chemical Engineering at the University of Western Australia.

Read more from the original source:

When robots learn to lie, then we worry about AI - The Australian Financial Review