BANGALORE, India — Fifteen years ago I came to Bangalore, India’s Silicon Valley, to do a documentary on outsourcing. One of our first stops was a company called 24/7 whose main business was answering customer service calls and selling products, like credit cards, for U.S. companies half a world away.

The beating heart of 24/7 back then was a vast floor of young phone operators, most with only high school degrees, save for a small pool of techies who provided “help desk” advice. These young Indians spoke in the best American English, perfected in a class that we filmed, where everyone had to practice enunciating “Peter Piper picked a peck of pickled peppers” — and make it sound like they were from Kansas not Kolkata.

The operations floor was so noisy from hundreds of simultaneous phone conversations that 24/7 installed a white-noise machine to muffle the din, but even then you could still occasionally hear piercing through the cacophony some techie saying to someone in America, the likes of: “What, Ma’am? Your computer is on fire?”

Well, 24/7’s founders — P.V. Kannan and Shanmugam Nagarajan — invited me back last week for an update. Their company is now called [24] and their shop floor is so quiet that the operators are encouraged to play their own music. The only noise is from the tapping on keyboards, because every query — from customers of U.S. retailers, banks and media companies — is coming in by text messaging from smartphones, tablets, desktops and laptops.

These text queries are usually answered first by a [24] chatbot, or “virtual agent,” powered by A.I. (artificial intelligence) and only get handed over to a person using H.I. (human intelligence) if the chatbot gets stuck and can’t answer. The transformation of [24] from perfecting its accents to perfecting its insights illustrates in miniature how A.I. is transforming the whole work landscape.

In a nutshell, the U.S. and Indian middle classes were built on something called the high-wage, middle-skilled job. In an A.I.-driven world, such jobs are becoming extinct. Now there are mostly high-skilled, high-wage jobs and low-skilled, low-wage jobs, and a dwindling number in between.

Virtually all of the [24] human operators today have college degrees, because they need to be able to text with good grammar in English, understand the interaction between the chatbot and the person calling for service and communicate with expertise and empathy when the chatbot runs out of answers.

At the training class I sat in on last week, Peter Piper was gone. He was replaced by a competition among trainees over who could grasp first exactly when the chatbot — which [24] calls by the woman’s name Aiva, for Artificially Intelligent Virtual Assistant — could no longer understand the “intent” of the customer and what that intent actually was.

It’s at that critical point that the human agent not only has to step in and answer the question that Aiva couldn’t, but also to “tag” the customer’s queries that stumped the bot and feed them to [24]’s data scientists, who then turn them into a new, deeper layer of artificial intelligence that enables Aiva to answer this more complex query the next time. (Kannan is about to publish a book on A.I. called “The Age of Intent.” )

The data scientists who figure out the upgrades for chatbots that handle text are called “digital conversation designers.” For another, small part of the business, data scientists for chatbots that speak in computer-generated natural language are called “voice conversation designers.”

“It’s a cool job,” Santhosh Kumar, a 45-year-old conversation designer, who came up through the 24/7 system, said to me. “You are designing what the chatbot should be saying to the customers.” It is all about “how to make a computer sound like a human.” Banks want their bots to be formal; retailers prefer more conversational bots.

Another new term I learned here was “containment.” That measures how deep into a conversation your chatbot can go without having to hand the customer over to a human agent. A company’s “containment rate” is like its A.I. batting average.

Today, [24]’s containment rate ranges from 20 percent to 50 percent of queries, depending on the company it is serving. Its goal is 80 percent. As the bots grasp more of each customer’s intent, the skilled humans are redeployed to more complex services and sales, and that, said Kannan, “turns into better sales and keeping customer satisfaction high.”

His chatbots, Kannan explained, are built with a “negative sentiment detector” to identify angry customers, so “we auto-generate sympathy when we can,” but for the most part “complexity and empathy” are left to the humans.

Hollywood and Bollywood movies lately “have created a really bad impression that robots are going to take over,” said Irene Clara, a trainer. “I don’t think that fear is justified. I think we grow together. When you’re teaching Aiva, you’re getting skilled yourself, and without that Aiva becomes incompetent.”

So — for now — if you have critical thinking and empathy skills, Aiva is your friend. But I wonder what happened to all those Indian high school grads I met 15 years ago. Because if you don’t have those skills — and just have a high school degree or less, which applies to hundreds of millions of Indians — or you are doing routine tasks that will be easily roboticized, well, Aiva the robotic fruit picker, Aiva the file clerk or Aiva the trucker will not be your friend.

So what will a country like India, with so much unskilled labor, do about this challenge? It’s coming. But so is a possible savior. It’s also called technology and A.I.

While technology taketh it also giveth. India’s newest high-speed mobile network, Jio, in just the past couple years dramatically slashed the price of cellphone connectivity. This has taken smartphone diffusion much deeper into Indian society than ever before, connecting those making only a few dollars a day to the mobile network, and creating a vast new tool kit to lift them from poverty.

In Mumbai, for example, I met with Sagar Defense Engineering, founded by Nikunj Parashar, which is using technology spun off from the defense industry to create a simple vessel, connected to satellites, that rag pickers, the poorest of the poor here, can be quickly trained on to target and collect the pools of waste that float atop so many Indian rivers and lakes — and get paid for it by the ton.

I also met with LeanAgri, founded by Siddharth Dialani and Sai Gole. It is using A.I. to create a simple cellphone-based app to make poor Indian farmers more successful. The app creates a “dynamic calendar” that tells each farmer which and how much seed and fertilizer to use, the quantity of water to apply and at what time based on changing climate conditions. LeanAgri’s pilot has been serving 3,000 farmers in three Indian states, where some have already seen tenfold increases in their incomes, the company said.

In Bangalore, I visited the EkStep Foundation. It was started by Nandan Nilekani, a co-founder of Infosys; his wife, Rohini; and the social entrepreneur Shankar Maruwada. EkStep (“One Step” in Hindi) argued that if India’s current youth bubble gets left behind by globalization and technology, India’s future will be tied to a giant ball and chain for the rest of the century.

EkStep has created a free, open-source digital infrastructure called Sunbird for making personal learning platforms. The Indian government leveraged it to create a national teachers’ platform, Diksha, which enables different states to put QR codes linked to all kinds of topics in their millions of old paper textbooks.

Now, all that a student or teacher or parent has to do is point a cellphone at the paper QR code and it opens up a universe of interactive content — lesson plans for teachers and study guides for students and parents — giving India a chance to improve numeracy and literacy at a whole new speed and scale.

So don’t write the conclusion of this story yet. Thanks to A.I., Peter Piper just might be able to pick a lot more than a peck of pickled peppers — so many more that not only the top of India’s society will rise, but also the bottom.

Read Source Article: NY Times

In collaboration with HuntertechGlobal


Just think for a moment about how much online searching you do. Need to find a nearby Thai restaurant? Just type your query into the search engine and presto: You receive page after page of results listing eateries in your area offering Pad Thai. Need to know the forecast in Austin? Again, punch in your query and you will receive no shortage of pages offering three-day forecasts, five-days forecasts, even year-round averages.

This is the world we now inhabit. Even people who grew up before the internet have come to accept what cultural historian and media scholar Siva Vaidhyanathan has termed “The Googlization of Everything” — the idea that anything we need to know can be accessible if we type in our query and wade through page after page of results.

But here’s a question that may not have occurred to you: Why must you search at all? After all, querying is a recent phenomenon, and as know, we are living through an unprecedented historical period in which technology is evolving so fast that once cutting-edge innovations introduced just a few years ago now appear laughably quaint. Personal Digital Assistants, anyone?


Surprisingly, bafflingly, the one area of technology central to our lives — search — somehow has not evolved to keep up with the times. Until now.

“This is the first time since 1994 when the search paradigm has changed, says David Seuss, CEO of Northern Light, a Boston-based strategic  research portal provider I consult with that offers a cloud-based SaaS to global enterprises. “In 1994, you went to a search box, filled in a query, hit the search button, and received a list of documents. You manually reviewed these, picking the most relevant item to download. Fast forward to 2019 and it’s stillthe same thing. Find me one other part of the tech landscape that has not changed since the ’90s, whether it be broadband, wireless, mobile cloud computing, artificial intelligence — everything has changed. Everything except search.”

Seuss attributes the dearth of innovation to a lack of imagination even though research has shown users feel frustrated with the status quo search model. Overwhelmed by the sheer amount of entries per query, “the average user won’t go past the first five listings on a search engine results page (SERP),” writes Madeline Jacobson for Leverage Marketing. Why? There is too much available content insufficiently organized, leading users to often accept initial results, even if they’re not ideal. “Most people will click on one of the first few results because they’ve found what they’re looking for, don’t want to scroll further, are short on time, or some combination of the three,” writes Jacobson.

Neil Patel, named one of the top 10 marketers by Forbes, brings home the present reality with a joke: “Where should you bury something that you don’t want people to find? Answer: On the second page of Google.” Though Patel cites the fact that 75 percent of users never scroll past the first results page, this problem is no laughing matter. Even if relying on the first two pages of results has come to be expected, especially when pages number in the hundreds, this modus operandi is detrimental for the information field. Too much important research is getting shelved into obscurity just because the field of search hasn’t kept up with the times. If anything, it’s gotten harder to get the information we need because results tend to skew toward paid advertisers and companies who game searching through SEO maneuvers.

Based on these types of frustrations it’s understandable that millennials, now comprising more than 35 percent of the workforce, have begun to push back on the way we search. Instead of relying on the manual querying model with all of its time-sucking and less-than-effective research implications, the new generation is paving the way for what’s being dubbed a “browse to content” model emphasizing information gathering and acquiring insights from numerous trusted, curated sources.

“Millennials are different than Boomers and Gen-Xers in how they approach information gathering,” says Seuss. “For Boomers and Gen-Xers, search was the radical change in their professional lives. They went from having to go to a corporate library and browse magazines on shelves to being able to go online and find information instantly. As a result, Boomers and Gen-Xers are personal research-oriented and search-oriented. Millennials, on the other hand, grab and move on. Speed is the primary ingredient in successful information delivery to Millennials. The truth is they are extremely efficient information gatherers and extremely effective at acquiring relevant insights when the tools are right and designed for their cognitive style.”

Though millennials may be the most adept at this new browse-to-content model, savvy business executives already have begun implementing new search modalities. It makes sense these two seemingly disparate groups would be pioneers of this search tactic; after all, they share key personal characteristics. Both are forever rushing around, feeling starved for time. Both also resent the antiquated hunting, pecking, downloading rinse and repeat search model of yesteryear, favoring instead the efficacious curated or “storytelling” model leading to advancements in competitive intelligence (CI.)

So what is this new storytelling research model? And how is possible to finally achieve technological breakthroughs in the field of search, a sector that has resisted evolution for so long? We need look no further than machine learning for the secret sauce. With the help of A.I., tasks once relegated to flesh and blood researchers can be now accomplished by computers. Drawing on the latter’s pattern-forming and predictive abilities, it can observe users’ actions, discerning their interests based on what they download, share, comment on or bookmark. Informed by this knowledge, an A.I. can proactively — and without manual prompting — recommend relevant content to users. Disrupting the traditional search model to its page ranking core, content can seek out the user instead of the other way around.

But machine learning promises even more dramatic search improvements. Anyone who receives RSS feeds or email alerts is probably aware of the benefits of collating desired content in so-called strategic dashboards. After all, publications such as The New York Times, have found tremendous benefit in being top-of-mind with readership by offering daily news briefings. What’s unique about what Northern Light offers through its A.I.-based platform is an up-to-the-second summarization of crucial content. “Instead of a user having to manually scroll through a search result list and individual documents to glean answers to a research question, the search engine reads all of the documents and summarizes the significance of the search result,” explains Seuss.

Such a distillation of actionable information can be pivotal to a company’s ability to stay current in an increasingly competitive business landscape where knowledge equals power — or at least leverage. Possessing the latest intel can be particularly invaluable to organizations in the IT space where lightning-fast developments and narrowing product life cycles can render today’s information useless tomorrow. Likewise, too many companies suffer from a deluge of information. They know they need to keep up with the latest information, but often find their efforts stymied by the sheer amount of data available from so many sources. Given the reality that research is constantly changing and quickly becoming obsolete, the smarter play can involve investing in a platform for aggregating and centralizing key content.

Seuss’ SinglePoint knowledge portal offers this type of service to its customers in various industries, including pharmaceutical, manufacturing, logistics, IT, and hospitality. Using a personalized approach for each client, it harvests business-relevant content from an array of content partners, such as Forrester, IDC, and Informa. Offering text analytics with extensive industry and business strategy taxonomies, and custom aggregation from syndicated secondary, primary internal, news, government, and web sources, it serves up content on demand for companies who live and die based on access to the latest competitive intelligence.

Beyond harvesting needed information to a central hub, Northern Light seeks to tackle another major frustration related to traditional searching: gleaning the most relevant, usable information in a timely manner. Most everyone is familiar with the annoying experience of manually sifting through result after result to find the desired content. To combat this universal pain point Northern Light has introduced its Insight Report. Powered by machine-learning, it automatically summarizes a document’s key ideas in its search results.

Importantly, the A.I. doesn’t rewrite the articles for SinglePoint’s clientele. Instead, using a proprietary algorithm, it extracts and presents the important “summary-worthy sentences,” defined as those declarative sentences making a statement and expressing a pithy idea. The “intelligence” in artificial intelligence comes into play once you realize the computer is almost instantaneously graphing all of the sentences in all of the documents onto the search results page. It does this to determine the relationships between them to synthesize the most important items a user needs to know. “The machine orders the report sentences as they appear in the documents first and also orders the document sentence grouping in the order the documents are on the search result,” explains Seuss.

Using this type of machine learning to reduce the strain on companies and individuals struggling to keep up with the staggering amount of information accruing daily (2.5 quintillion bytes) is proving more necessary than ever. It seems unbelievable to reflect on the fact that search, the very act most closely associated with the internet, hasn’t kept pace with our changing times. However, as continuing developments push technology from the realm of the conceivable into the possible, it’s not so far off to imagine the death of search, or at least the rise of search 2.0. Instead of relying on so much querying to achieve the knowledge we seek, a day may come when more and more information seeks us out, intelligently predicting what need to know. Now.


Although the headline that gets the clicks is “AI is taking our jobs,” the current reality is that “Automation is replacing some of our tasks.” I know, it’s nowhere near as catchy.

I say automation rather than AI because it doesn’t really matter what technology the underlying system uses, so long as it does the job well. For example, being a bank teller was a human job requiring intelligence, but there’s no AI in an ATM cash machine (when it comes to technology evolution, some of them don’t even seem to have caught up to Windows Vista yet).

I also prefer "tasks" over "jobs" because mostly AI can only do certain elements. MIT professor Erik Brynjolfsson, one of the authors who, with Andrew McAfee, first wrote about this trend in "Race Against the Machine" has more recently been emphasizing the need to redesign jobs around AI opportunities.

AI’s Specific Strengths

The biggest recent advances in AI tend to be around pattern recognition and rule-based reasoning. For example, machine-learning, image recognition and speech recognition are all largely pattern-based. They work well when there is a large and appropriate data-set for training, but they don’t "understand" that a picture of a cat is a cat in the way that we do.

Chatbots, self-driving cars and IBM Watson then overlay pattern processing with a set of rules for deciding what to do. Hard coded-rules are easy to implement but tend to be brittle. Flexible rules take more work and can become unpredictable.

See also this business-oriented summary by Siw Grinaker, community manager at Enonic.

Related Article: Successfully Integrating AI Into the Workplace Is a Human Task

Can AI Replace Your Manager?

Several articles about AI replacing managers have been published recently. I suspect much of this is wishful thinking by oppressed copywriters. Let's break down a manger role into typical tasks.

In 1990, Henry Mintzberg broke down management into 10 roles. Let’s work our way through to see how amenable each is to automation:

  1. Figurehead. This is about inspiring and being a figure of authority. It requires emotional intelligence and AI is only at the very early stages of being able to respond to emotion.
  2. Leader. Mintzberg says this is about setting a high-level direction. It’s a very open-ended problem unsuited to AI.
  3. Liaison is about building internal networks. Although there’s an emotional component to this too, we’re already seeing automation help by recommending connections in social network software, for example.
  4. Monitor. Keeping track of progress and industry changes. Monitoring is hard for humans — often repetitive and limited by our information processing capacity. In part then, it is ideal as a pattern-recognition task for automation. For easily-quantified worker outputs, it's already happening. Think of call-center monitoring or gig-economy workers managed via apps.
  5. Disseminator. This is about sharing information with colleagues. Arguably we can automate some of this in the way that news services try to second-guess your interests. However, right now there’s still a way to go. Being able to think through the implication of new information has a creative element that most AI lacks.
  6. Spokesperson. Mintzberg talked about representing an organization externally. It’s a diverse and again open-ended task. A chatbot may cope with simple information requests, but it's unlikely to win new customers over.
  7. Entrepreneur. Solving problems and innovating. In general creativity is a big barrier for AI. Conversely, problem-solving has a long AI-history (Tower of Hanoiis a favorite AI student assignment). The issue is that AI is good at solving problems when the domain is well defined and the criteria for success can be articulated. Usually, this isn’t what we mean by "entrepreneurial" problem-solving.
  8. Disturbance Handler. Stepping in when a roadblock is hit. This is the opposite of how most AI works in practical terms. For example, when self-driving cars hit a roadblock, they expect the human supervisor to take over, not the other way round (and I’m giving myself a bonus point for the pun).
  9. Resource Allocator. Allocation of resources is about both tasks to people and funding to needs. Where the skills and needs are well-defined, there’s plenty of scope for automation on this one. Rule-based systems have been helping with shift-scheduling and timetabling for decades, for example. It becomes much harder when soft-skills come into play, however.
  10. Negotiator. Participating in, and directing negotiations sounds like a very soft skill to me. There is actually an active AI research theme around this , and sometimes people respond favorably to the idea of a machine facilitator because they feel it will be less biased. Sadly, there is plenty of evidence that algorithms can be biased too, it all depends on the training set. For example, one study of machine learning found that male names were more strongly associated with “professional” and “salary” than female names.

So of the list above, we have a decent case for automation being influential on two of the 10 roles (Monitor and Resource Allocator) and helpful in two more (Liaison and Disseminator).

Read Source Article: CMS Wire

In Collaboration with HuntertechGlobal

Artificial intelligence (AI) is no longer the stuff of science fiction. The technology is already disrupting multiple industries, many of which impact you on a daily basis. Own an iPhone X? Its facial recognition system is powered by AI. Ever been redirected by Google Maps because of an accident or construction ahead? You guessed it: AI.

And those are just a couple of small examples. By one estimate, AI contributed a whopping $2 trillion to global GDP last year. By 2030, it could be as much as $15.7 trillion, “making it the biggest commercial opportunity in today’s fast changing economy,” according to a recent report by PwC.

AI's Projected Impact on Global GDPU.S. GLOBAL INVESTORS

AI: The “New Electricity”

Not every industry and sector will be affected equally, but none will go untouched.

 “AI is the new electricity,” says Chinese-English computer scientist and entrepreneur Andrew Ng. “I can hardly imagine an industry which is not going to be transformed by AI.”

Among the industries that have been fastest to adopt AI, according to PwC, are health care, automotive and financial services. Earlier and more accurate diagnostics, powered by AI, means earlier treatment of life-threatening diseases. Once on the market, self-driving cars will free up an estimated 300 hours the typical American spends driving every year. And more and more people are putting their trust in robo-advisors to manage their wealth.

Robo-Advisor Platforms Forecast to Continue Growing Around the WorldU.S. GLOBAL INVESTORS

AI patents have surged in the past five years alone, according to the World Intellectual Property Organization (WIPO). From 2013 to the end of 2017, the number of patents grew nearly three times, from 19,000 to more than 55,600.

The massive increase in patenting “means we can expect a very significant number of new AI-based products, applications and techniques that will alter our daily lives—an

d also shape future human interaction with the machines we created,” comments WIPO Director-General Francis Gurry.

A majority of the top 500 applicants are from China, the U.S. and South Korea. Only four are from Europe. At the top of the list sits IBM, with an incredible 8,290 inventions (so far), followed by Microsoft, which has 5,930 patents to its name.

Top 10 Patent Applicants in the AI FieldU.S. GLOBAL INVESTORS

As you might imagine, the U.S. government wants to ensure that the country remain competitive against Asia. This very  month, President Donald Trump signed an executive order urging federal agencies to prioritize AI investments in research and development. The American AI Initiative, as it’s called, says that these measures  are “critical to creating the industries of the future, like autonomous cars, industrial robots, algorithms for disease diagnosis and more.”

“I want 5G, and even 6G, technology in the United States as soon as possible,” Trump tweeted last week, presumably in response to news that Chinese telecommunications firm ZTE could be first to bring fifth-generation cellular technology to market. “American companies must step up their efforts or get left behind. There is no reason that we should be lagging behind on… something that is so obviously the future.”

Bringing AI to the Miners

Interestingly enough, the industry that’s been slowest to adopt AI is manufacturing, including industrial products and raw materials, according to PwC.

The metals and mining industry has been especially resistant to adoption, with spending on innovation far below that of other industries.

To be fair, not every miner has been behind the curve. For more than 10 years now, Rio Tinto has been using AI-powered autonomous trucks to haul materials, reducing fuel consumption and increasing safety in the process. The London-based producer also uses autonomous loaders and drills, and its highly anticipated “intelligent mine” in Western Australia is slated to begin operations in 2021.

A couple of weeks ago, I introduced you to an exciting new company called GoldSpot Discoveries, conceived and headed by mining visionary Denis Laviolette. GoldSpot is the world’s first exploration company to use artificial intelligence (AI) and machine learning in the discovery process for precious metals and other natural resources. Not yet three years old, it’s already had a number of successes locating optimal target zones.

I’m pleased to inform you now that GoldSpot began trading last week on the TSX Venture Exchange under the ticker SPOT. This is a giant leap forward not just for the company and its team but also AI in general.

Much more could be done, Denis says, especially when it comes to utilizing the mountains of data already at our fingertips. Miners were “paying for all this data, but no one was really doing anything with it,” he told me earlier this month.

Speaking to the Wall Street Journal in December, Denis commented that he had seen “an awful lot of posturing” when it came to miners claiming to be interested in modernizing operations and integrating AI. “They say they are working on this internally, then you find out they haven’t got anywhere.”

This is precisely why he conceived of GoldSpot Discoveries. I’m fully convinced that mining’s future belongs to AI, with Denis and GoldSpot leading the way.


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U.S. Global Investors, Inc. is an investment adviser registered with the Securities and Exchange Commission ("SEC"). This does not mean that we are sponsored, recommended, or approved by the SEC, or that our abilities or qualifications in any respect have been passed upon by the SEC or any officer of the SEC.

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Read Source Article:Forbes

in Collaboration with HuntertechGlobal

Image Source

A software developer has created a website that generates fake faces, using artificial intelligence (AI). generates a new lifelike image each time the page is refreshed, using technology developed by chipmaker Nvidia.

Some visitors to the website say they have been amazed by the convincing nature of some of the fakes, although others are more clearly artificial.

And many of them have gone on to post some of the fake faces on social media.

Nvidia developed a pair of adversarial AI programs to create and then critique the images, in 2017.

The company later made these programs open source, meaning they are publicly accessible.

Two faces from the websiteImage copyrightTHISPERSONDOESNOTEXIST.COM
Image caption Not all faces on the website are convincingly human

Realistic fakes

As the quality of synthetic speech, text and imagery improves, researchers are encountering ethical dilemmas about whether to share their work.

Media caption Why these faces do not belong to 'real' people

Last week, the Elon Musk backed OpenAI research group announced it had created an artificially intelligent "writer".

But the San Francisco group took the unusual step of not releasing the technology behind the project publicly.

"It's clear that the ability to generate synthetic text that is conditioned on specific subjects has the potential for significant abuse," the group said in a statement to AI blog Synced.

Read Source Article:BBC News

In Collaboration with HuntertechGlobal

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