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What the Advertising Industry Could Learn from Bots

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The application to the advertising industry is so obvious it is like a slap in the face with a wet fish.

By MediaStreet Staff Writers

Lately, social media has been all about heated exchanges and distribution of fake news. And right in the thick of these skirmishes are Twitter bots. They have certainly earned themselves a bad reputation, tweeting on behalf of politicians and driving troll trains through the media landscape with abandon.

But, not all bots are bad, according to a boffins at USC’s Information Sciences Institute. Computer scientist Emilio Ferrara undertook a large-scale experiment designed to analyse the spread of information on social networks. Ferrara teamed up with some Danish boffins from the Technical University of Denmark to deploy a network of “social bots,” programmed to spread positive messages on Twitter.

“We found that bots can be used to run interventions on social media that trigger or foster good behaviours,” says Ferrara, whose previous research focused on the proliferation of bots in the U.S. election campaign.

But it also revealed another intriguing pattern: information is much more likely to become viral when people are exposed to the same piece of information multiple times through multiple DIFERENT sources. Says Ferrara, “This milestone shatters a long-held belief that ideas spread like an infectious disease, or contagion, with each exposure resulting in the same probability of infection. Now we have seen empirically that when you are exposed to a given piece of information multiple times, your chances of adopting this information increase every time.”

To reach these conclusions, the researchers first developed a dozen positive hashtags, ranging from health tips to fun activities, such as encouraging users to get the flu shot, high-five a stranger and even Photoshop a celebrity’s face onto a turkey at Thanksgiving. Then, they designed a network of 39 bots to deploy these hashtags in a synchronised manner to 25,000 real followers during a four-month period from October to December 2016.

Each bot automatically recorded when a target user retweeted intervention-related content and also each exposure that had taken place prior to retweeting. Several hashtags received more than one hundred retweets and likes. “We also saw that every exposure increased the probability of adoption – there is a cumulative reinforcement effect,” says Ferrara. “It seems there are some cognitive mechanisms that reinforce your likelihood to believe in or adopt a piece of information when it is validated by multiple sources in your social network.”

This mechanism could explain, for example, why you might take one friend’s movie recommendation with a grain of salt. But the probability that you will also see that movie increases cumulatively as each additional friend makes the same recommendation.

This discovery could improve how positive intervention strategies are deployed in social networks in many scenarios, including public health announcements for disease control or emergency management in the wake of a crisis. The common approach is to have one broadcasting entity with many followers. But this study implies that it would be more effective to have multiple, decentralised bots share synchronised content.

Advertisers, mull this over. Bots can be your very best friend.

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8 Rules Of Positioning

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By Harsh Pamnani.

In a market crowded with a lot of brands offering similar products, a good positioning makes a brand and its products stand out from the competition

Getting in front of customers and prospects is an important thing, but more important thing is what you will communicate about your brand and product when you are in front of your audience. Positioning helps marketers to connect their brand and products best with their target audience. In a market crowded with a lot of brands offering similar products, a good positioning makes a brand and its products stand out from the competition.

Positioning is one of the most important components of marketing strategy and vital to success of any brand. Al Ries and Jack Trout, in their book Positioning: The Battle for Your Mind, introduce the subject by saying: Positioning is not what you do to a product. Positioning is what you do to the mind of the prospect. That is, you position the product in the mind of the prospect.

Let’s have a look at a few important rules of positioning:

1.    Positioning drives marketing strategy: The process of creating positioning statement requires identifying target audience; product category; product’s specific benefit, strengths and weaknesses and differentiation from the nearest competitor. Positioning drives all components of marketing strategy such as advertising, packaging, pricing, distribution, public relations, merchandising and brand communication. Additionally, strong positioning attracts partners, employees, investors, customers to associate with a company owning top positioned brands. Moreover, good positioning attracts influencers such as journalists, analysts, thought leaders etc. to cover a brand in their articles and reports. For example, in extremely competitive, coffee selling business, Starbucks has positioned itself as an upscale brand. Its stores’ locations, service, products display, packaging, socializing environment, pricing etc. are designed according to its positioning of an upscale brand.

2.    Positioning is relative: In any category, customers think about brands relative to other brands in the same category. To gain strong position for its brands, a company must differentiate its brands and products from others in the market. The most important point is that differentiation has to be sustainable. Differentiations such as price and features can be surpassed by competition in some time but it is difficult for competition to surpass the differentiation of quality, service, availability and leadership. For example, there are many digital wallets such as PayTm, MobiKwik, Freecharge, BHIM, State Bank of India’s SBI Buddy etc. All of these wallets have almost similar features and pricing and solve the similar purpose, but in customers’ mind PayTm has taken up the top position and has strong perception of quality and leadership.

3.    Positioning changes as market changes: In today’s fast changing world, products change, markets change, customers’ demands change, competition change, technologies change, regulations change and so on. These changes can create an opportunity for a new player to shake the positioning of an established player. For example, non-polluting electric vehicles are seen as norm of the future and Tesla is a prominent player in elegant electric vehicles. As per an article in recode, the 14-year-old company Tesla is now worth more than 113 year old company Ford. In a way, Tesla’s positioning seems to be surpassing Ford’s position.

4.    Positioning is multidimensional: Positioning has multiple dimensions such as product positioning, market positioning, industry positioning and leaders’ positioning. Product positioning is defined by a company based on its strategy, focus on market segment, price point, distribution channel etc. Market positioning of a brand or product is defined by word of mouth of influencers such as customers, analysts, retailers, journalists, partners etc. Industry positioning is defined by revenue and profit of a company. And most importantly, success of company elevates the positioning of its leader. For example, iPhone is a product brand, Apple is a company brand and Steve Jobs is a leader brand.  iPhone is positioned as a premium smart phone with higher price point targeted towards upper middle class and rich customers and available through selective channels. Positive word of mouth by influencers including customers has helped iPhone in gaining market recognition as the top positioned smart phone. Revenue through sales of iPhone helps Apple in achieving better positions in rankings such as Fortune 500. Success of Apple’s products such as iPhone has contributed to Steve Jobs’ position as one of the best business leaders. Again, Steve Jobs’ positioning as one of the best leaders drives positioning of his company, company’s products and so on.

5.    Positioning evolves over time: As company grows over time, its market segments evolve, its products evolve and it’s positioning in market evolves. If a company is focussed on niche market segment then it has to position itself for niche customers. But over the time, when market segment evolves or when company tries to enter into adjacent market segments then its positioning evolves. For example, when Uber was new in India, smart phones were available with limited number of people and taxi riding was not a preferred option as compared to auto rickshaws. Initially, Uber targeted customers who were looking to enjoy a luxury experience, had smart phones and credit cards. It was positioned as a taxi ride service for classes. Later on, Uber expanded its offerings such as low cost small cars, medium cost sedans and higher cost big cars. It also expanded its services from point to point transfer to outstation travel, taxi hire for personal usage, economical ride sharing etc. Moreover, along with credit card, it started accepting money through PayTm and cash. This evolution not only expanded Uber’s market segment, but also their positions from a transportation option for classes to a transportation option for masses.

6.    Positioning is strongest in the new category: In a mature category, there are already established players and to create its position, a brand has to compete with existing brands. But if a brand is able to create a new category then it can achieve leadership status in that category. For example, fast food is an overcrowded category with many popular brands such as McDonald’s, KFC, Subway, Taco Bell, Dominos, Dunkin Donuts, and Starbucks etc.  But all of these brands have created their leadership positions in separate subcategories within fast food category. For example, McDonald’s is known for burgers, KFC is known for chicken, Subway for sandwiches, Taco Bell for Mexican food, Dominos for pizza delivery, Dunkin Donuts for donuts, Starbucks for coffee and so on. Though all these players try to enter into each other’s’ offerings but their positioning is strongest around their key fast food offerings.

7.    Positioning is internal: The purpose of positioning statement is to align internal stakeholders such as marketing team, sales team, delivery team etc. on a common view of market. This alignment helps in having common interpretation of target audience, product category, differentiation from competitors, benefits for customers and so on. When everybody internally is on the same page, external communication becomes homogenous, relevant, targeted and clear.  For example Harley-Davidson’s internal positioning statement is: The only motorcycle manufacturer that makes big, loud motorcycles for macho guys (and “macho wannabes”) mostly in the United States who wants to join a gang of cowboys in an era of decreasing personal freedom. Taglines are external facing catch phrases that summarize positioning statement extremely concisely. For Harley-Davidson, tagline is “Define your world in a whole new way.”

8.    Positioning gets spoiled by brand extension: Brand extension is a common method used by companies to launch a new product by using an existing brand name on a new product in a different category. A company using brand extension hopes to leverage its existing customer base and brand loyalty to increase its profits with a new product offering. If a company expands its business too fast by launching multiple products using its powerful brand name, then it is necessary for it to maintain quality. If quality of a few of the products of a respected brand is bad, then customers no matter how loyal they are will start rethinking about the brand. Lowered image of a few products in customers’ mind would eventually impact the brand position and the business’ revenue. For example, Baba Ramdev’s Patanjali brand has a strong positioning in Ayurvedic products. But since last few years, Patanjali has been launching many new products in different categories and that’s too fast. There have been incidences when government’s food safety departments have raised questions on a few of the Patanjali’s products. Though strong brand name of Baba Ramdev and Patanjali have helped the company to launch and distribute many new products, quality concerns on a few products, effect overall positioning of the brand Patanjali.

(Views expressed are author’s personal and don’t necessarily represent any company’s opinions.)

Disclaimer: The views expressed in the article above are those of the authors’ and do not necessarily represent or reflect the views of this publishing house. Unless otherwise noted, the author is writing in his/her personal capacity. They are not intended and should not be thought to represent official ideas, attitudes, or policies of any agency or institution.

 

By Harsh Pamnani

The author is a Marketer & Author. He is an alumnus of XLRI, Jamshedpur  More From The Author >>

Sourced from BW BUSINESSWORLD

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Artificial intelligence and the coming health revolution

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By Rob Lever.

Your next doctor could very well be a bot. And bots, or automated programs, are likely to play a key role in finding cures for some of the most difficult-to-treat diseases and conditions.

Artificial intelligence is rapidly moving into health care, led by some of the biggest technology companies and emerging startups using it to diagnose and respond to a raft of conditions.

Consider these examples:

— California researchers detected cardiac arrhythmia with 97 percent accuracy on wearers of an Apple Watch with the AI-based Cariogram application, opening up early treatment options to avert strokes.

— Scientists from Harvard and the University of Vermont developed a machine learning tool — a type of AI that enables computers to learn without being explicitly programmed — to better identify depression by studying Instagram posts, suggesting “new avenues for early screening and detection of mental illness.”

— Researchers from Britain’s University of Nottingham created an algorithm that predicted heart attacks better than doctors using conventional guidelines.While technology has always played a role in medical care, a wave of investment from Silicon Valley and a flood of data from connected devices appear to be spurring innovation.

“I think a tipping point was when Apple released its Research Kit,” said Forrester Research analyst Kate McCarthy, referring to a program letting Apple users enable data from their daily activities to be used in medical studies.

McCarthy said advances in artificial intelligence has opened up new possibilities for “personalized medicine” adapted to individual genetics.

“We now have an environment where people can weave through clinical research at a speed you could never do before,” she said.

Predictive analytics 

AI is better known in the tech field for uses such as autonomous driving, or defeating experts in the board game Go.

But it can also be used to glean new insights from existing data such as electronic health records and lab tests, says Narges Razavian, a professor at New York University’s Langone School of Medicine who led a research project on predictive analytics for more than 100 medical conditions.

“Our work is looking at trends and trying to predict (disease) six months into the future, to be able to act before things get worse,” Razavian said.

— NYU researchers analyzed medical and lab records to accurately predict the onset of dozens of diseases and conditions including type 2 diabetes, heart or kidney failure and stroke. The project developed software now used at NYU which may be deployed at other medical facilities.

— Google’s DeepMind division is using artificial intelligence to help doctors analyze tissue samples to determine the likelihood that b****t and other cancers will spread, and develop the best radiotherapy treatments.

— Microsoft, Intel and other tech giants are also working with researchers to sort through data with AI to better understand and treat lung, b****t and other types of cancer.

— Google parent Alphabet’s life sciences unit Verily has joined Apple in releasing a smartwatch for studies including one to identify patterns in the progression of Parkinson’s disease. Amazon meanwhile offers medical advice through applications on its voice-activated artificial assistant Alexa.

 

IBM has been focusing on these issues with its Watson Health unit, which uses “cognitive computing” to help understand cancer and other diseases.

When IBM’s Watson computing system won the TV game show Jeopardy in 2011, “there were a lot of folks in health care who said that is the same process doctors use when they try to understand health care,” said Anil Jain, chief medical officer of Watson Health.

Systems like Watson, he said, “are able to connect all the disparate pieces of information” from medical journals and other sources “in a much more accelerated way.”

“Cognitive computing may not find a cure on day one, but it can help understand people’s behavior and habits” and their impact on disease, Jain said.

It’s not just major tech companies moving into health.

Research firm CB Insights this year identified 106 digital health startups applying machine learning and predictive analytics “to reduce drug discovery times, provide virtual assistance to patients, and diagnose ailments by processing medical images.”

Maryland-based startup Insilico Medicine uses so-called “deep learning” to shorten drug testing and approval times, down from the current 10 to 15 years.

“We can take 10,000 compounds and narrow that down to 10 to find the most promising ones,” said Insilico’s Qingsong Zhu.

Insilico is working on drugs for amyotrophic lateral sclerosis (ALS), cancer and age-related diseases, aiming to develop personalized treatments.

Finding depression 

Artificial intelligence is also increasingly seen as a means for detecting depression and other mental illnesses, by spotting patterns that may not be obvious, even to professionals.

A research paper by Florida State University’s Jessica Ribeiro found it can predict with 80 to 90 percent accuracy whether someone will attempt suicide as far off as two years into the future.

Facebook uses AI as part of a test project to prevent suicides by analyzing social network posts.

And San Francisco’s Woebot Labs this month debuted on Facebook Messenger what it dubs the first chatbot offering “cognitive behavioral therapy” online — partly as a way to reach people wary of the social stigma of seeking mental health care.

New technologies are also offering hope for rare diseases.

Boston-based startup FDNA uses facial recognition technology matched against a database associated with over 8,000 rare diseases and genetic disorders, sharing data and insights with medical centers in 129 countries via its Face2Gene application.

Cautious optimism 

Lynda Chin, vice chancellor and chief innovation officer at the University of Texas System, said she sees “a lot of excitement around these tools” but that technology alone is unlikely to translate into wide-scale health benefits.

One problem, Chin said, is that data from sources as disparate as medical records and Fitbits is difficult to access due to privacy and other regulations.

More important, she said, is integrating data in health care delivery where doctors may be unaware of what’s available or how to use new tools.

“Just having the analytics and data get you to step one,” said Chin. “It’s not just about putting an app on the app store.”

By Rob Lever.

Sourced from MSN News

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How AI Is Changing the World of Advertising Forever

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By George Beall.

For how much Hollywood loves remakes, I’m curious to see what a futuristic Mad Men is going to look like. Don’t get me wrong; I’m not expecting to see robotic Don Draper, who writes poignant lines of copy aggregated from data points all over the world (that’d be cheesy and boring). Rather, I’d be more excited to see how technology is going to change the world of advertising for good.

A lot of people might think that under the reigns of Artificial Intelligence every job will suddenly be replaced by a robot. However, the core component of advertising is storytelling, which is something that requires a human touch. Even more, AI isn’t going to replace storytellers, but rather empower them. Yes, the world of artificial intelligence is about to make advertising more human. Here’s why:

From Madison Avenue to Silicon Valley

It’s no secret that the advertising world goes giddy over any innovation in the tech realm. After all, a big portion of how firms gain an edge in their industry is by being up on the latest and greatest, as well as demonstrating a capacity to look at how new practices can be applied to client campaigns. And when it comes to AI, a lot of major agencies have already situated themselves ahead of the curve.

The interesting thing to note here isn’t necessarily that these agencies are using AI in general, but rather, how they’re using it. For example, the link above notes how a few firms have teamed up with AI firms to work on targeting and audience discovery. While these practices have been implemented long before, Artificial Intelligence has been accelerating the process. However, even with major players teaming up with the likes of IBM Watson, smaller agencies and startups have been on this trend as well.

An excellent example of this is the company Frank, an AI based advertising firm for startups. Frank’s goal is to use AI in the same manner of targeting mentioned above, only offering it to those businesses that could really use the savings. The platform allows you to set the goals of your campaign, as well as hones in on targeting and bidding efficiently. This saves time and money often devoted to outsourcing digital advertising efforts, as well as gives an accurate depiction of how ads are performing in real time. Expect players like Frank to make a significant change in how small businesses and startups approach how to use AI in their marketing.

Big Dollars For Small Budgets

One of the biggest news stories to hit about AI and advertising was Goldman Sach’s $30 million investment into Persado. If you haven’t heard about it yet, Persado essentially aggregates and compiles ‘cognitive content,’ which is copy backed by data. It breaks down everything, from sentence structure, word choice, emotion, time of day, and even can bring in a more accurate call-to-action. And for those that hire digital marketers and advertisers, this sounds like a dream come true in saving time and money. However, when it comes to writing, AI can only go so far.

While some content creators and digital copywriters might be a little nervous that AI will eventually take their jobs, that’s simply not the case. Writing involves a certain sense of emotional intelligence and response that no computer can feel. Moreover, the type of content that AI can create is limited to short-term messages. I’m not sure about you, but I’ll safely bet that no major marketing director is willing to put their Super Bowl ad in the hands of a computer. Overall, while Wall Street recognizes Artificial Intelligence’s potential impact in the creative world, it’s safe to say when it comes to telling a story, that human touch will never go away.

The Unexpected Players

Perhaps one of the most underrated things about AI is its potential to eliminate practices altogether. While we mentioned above that, yes, certain jobs in the creative field will never go away, there’s a possibility that certain processes in the marketing channel might change drastically.

For example, companies like Leadcrunch are using AI to build up B2B sales leads. While before B2B sales could rely on either targeted ads or sales teams to bring clients in, software like Leadcrunch’s is eliminating those processes altogether. Granted, this isn’t exactly a bad thing as a lot of B2B communications relies heavily on educating consumers, something a banner ad can’t do as accurately as a person. Overall, companies like this are going to drastically change how our pipelines work, potentially changing how the relationship between advertising and AI work hand-in-hand for a long time.

By George Beall

George Beall is a student at the Wharton School of the University of Pennsylvania. He has a deep admiration for true innovation and has been involved in multiple in technology startups. He is currently an active angel investor. In his spare time he enjoys horseback riding, discovering upcoming music, and binge watching Netflix.

Sourced from TNW

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How Artificial Intelligence will impact professional writing

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By Ben Dickson.

Professional writing isn’t easy. As a blogger, journalist or reporter, you have to meet several challenges to stay at the top of your trade. You have to stay up to date with the latest developments and at the same time write timely, compelling and unique content.

The same goes for scientists, researchers and analysts and other professionals whose job involves a lot of writing.

With the deluge of information being published on the web every day, things aren’t getting easier. You have to juggle speed, style, quality and content simultaneously if you want to succeed in reaching your audience.

Fortunately, Artificial Intelligence, which is fast permeating every aspect of human life, has a few tricks up its sleeve to boost the efforts of professional writers.

Smart proofreading

In 2014, George R. R. Martin, the acclaimed writer of the Song of Ice and Fire saga, explained in an interview how he avoids modern word processors because of their pesky autocorrect and spell checkers.

Software vendors have always tried to assist writers by adding proofreading features to their tools. But as writers like Martin will attest, those efforts can be a nuisance to anyone with more-than-moderate writing skills.

However, that is changing as AI is getting better at understanding the context and intent of written text. One example is Microsoft Word’s new Editor feature, a tool that uses AI to provide more than simple proofreading.

Editor can understand different nuances in your prose much better than code-and-logic tools do. It flags not only to grammatical errors and style mistakes, but also the use of unnecessarily complex words and overused terms. For instance, it knows when you’re using the word “really” to emphasize a point or to pose a question.

It also gives eloquent descriptions of its decisions and provides smart suggestions when it deems something as incorrect. For example if it marks a sentence as passive, it will provide a reworded version in active voice.

Editor has been well received by professional writers (passive voice intended), though it’s still far from perfect.

Nonetheless AI-powered writing assistance is fast becoming a competitive market. Grammarly, a freemium grammar checker that installs as a browser extension, uses AI to help with all writing tasks on the web. Atomic Reach is another player in the space, which uses machine learning to provide feedback on the readability of written content.

Quicker scanning of written documents

Writing good content relies on good reading. I usually like to go through different articles describing conflicting opinions about a topic before I fire up my word processor. The problem is there’s so much material and so little time to read all of it. And things tend to get tedious when you’re trying to find key highlights and differences between articles written about a similar topic.

Now, Artificial Intelligence is making inroads in the field by providing smart summaries of documents. An AI algorithm developed by researchers at Salesforce generates snippets of text that describe the essence of long text. Though tools for summarizing texts have existed for a while, Salesforce’s solution surpasses others by using machine learning. The system uses a combination of supervised and reinforced learning to get help from human trainers and learn to summarize on its own.

Other algorithms such as Algorithmia’s Summarizer provide developers with libraries that easily integrate text summary capabilities into their software.

These tools can help writers skim through a lot of articles and find relevant topics to write about. It can also help editors to read through tons of emails, pitches and press releases they receive every day. This way they’ll be better positioned to decide which emails need further attention. Having hundreds of unread emails in my inbox, I fully appreciate the value this can have.

Advances in Natural Language Processing have contributed widely to this trend. NLP helps machines understand the general meaning of text and relations between different elements and entities.

To be fair, nothing short of human level intelligence can have the commonsense knowledge and mastery of language required to provide flawless summary of all text. The technology still has more than and few kinks to iron out, but it shows a glimpse of what the future of reading might look like.

Smarter search engines, content-writing robots and beyond

No matter how high-quality and relevant your content is, it’ll be of no use if you can’t reach out to the right audience. Unfortunately, old keyword-based search algorithms pushed online writers toward stuffing their content with keywords in order to increase their relevance for search engine crawlers.

“Although with PageRank, Google did a great job in organizing the web, it also created a web where keywords ruled over content,” says Gennaro Cuofano, growth hacker at WordLift, a company that develops tools for semantic web. “Eventually, web writers ended up spending a significant amount of time improving the findability.” The trend resulted in poor quality writing getting higher search ranking.

But thanks to Artificial Intelligence, search engines are able to parse and understand content, and the rules of search engine optimization have changed immensely in past years.

“Since new semantic technologies are now mature enough to read human language, journalists and professional writers can finally go back to writing for people,” Cuofano says. This means you can expect more quality content to appear both on websites and search engine results.

Where do we go from here? “The next revolution (which is already coming) is the leap from NLP to a subset of it called NLU (Natural Language Understanding),” Cuofano says. “In fact, while NLP is more about giving structure to data, defining it and making it readable by machines; NLU instead is about taking unclear, unstructured and undefined inputs and transforming them to an output that is close to human understanding.”

We’re already seeing glimmers of this next generation in AI-powered journalism. The technology is still in its infancy, but will not remain so indefinitely. Writing can someday become a full-time machine occupation, just like many other tasks that were believed to be the exclusive domain of human intelligence the past.

How does this affect writing? “Currently, the web is a place where how-to articles, tutorials and guides are dominant,” Cuofano says. “This makes sense in an era where people are still in charge of most tasks. Yet in a future where AI takes over, wouldn’t it make more and more sense to write about ‘why’ we do things? Thus, instead of focusing on content that has a short shelf life, we can focus again on content that has the capability to outlive us.”

By Ben Dickson.

Ben Dickson is the founder of TechTalks. He writes regularly on business, technology and politics. Follow him on Twitter.

Sourced from TNW

 

 

 

 

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Artificial Intelligence is Going to Destroy Fake News

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By Rosalie Chan.

With the rise of email came came the rise of spam filling inboxes.

Email has become sophisticated faster than spamming technology and now, the internet’s junk mail is often caught in a folder; out of sight and out of mind are messages with the subject line “Kindly get back to me urgently” and the greeting “Dear Beneficiary.”

There’s good news for anybody who sees fake news — not the sort that’s simply true but politically difficult for the president; but actual, fake, conspiracy theory-baiting chum — as another form of spam.

At least that’s what Dean Pomerleau, research scientist at Carnegie Mellon University’s Robotics Institute, said recently during a panel in New York on the proliferation of fake news. We solved the spam problem using artificial intelligence, he argued, and with A.I., we can solve the problem of fake news by filtering out credible news from the misinformation. Wheat from chaff, etc.

Also on the panel, put on by the New York Daily News Innovation Lab in Manhattan, was CNN political commentator Sally Kohn, who was well aware of her network’s reputation as purveyors of fakery over its coverage of the notoriously sensitive President Donald Trump.

“According to half the country, that means I’m an expert on fake news,” Kohn said. “As a citizen, I’m invested in facts. As a journalist, even an opinion journalist, I’m grounded in facts.”

At the panel, Pomerleau spoke about ways A.I. can combat fake news. He co-directs the Fake News Challenge, a competition to create a fact-checking tool. The idea for the challenge started shortly after the election. So far, there are almost 200 teams signed up and 300 people who registered for the Fake News Challenge Slack channel.

“We were speculating among friends, what could we as machine-learning people do to improve the situation moving forward? That was the genesis of the idea,” Pomerleau tells Inverse.

Even the challenge problem for the annual International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction and Behavior Representation in Modeling and Simulation is fake news and propaganda. “Simulation studies, data science studies, machine learning studies, and network science studies are all encouraged,” the prompt announced when it was posted recently.

Inverse asked Pomerleau just how fake news will be killed and here’s what he said.

How do we see A.I. creating fake news in the future?

I think in the near future, technology is likely to help in the creative process. It won’t be too long before generated video can be created, very much like PhotoShop. Those two things together will really undermine our ability to believe what we see. Image processing and audio processing tools will foster easy creation by just about anyone to create fake news.

How can A.I. fight fake news?

Using smart filtering and content analysis and natural language processing — these can all be used as signals to an algorithm that’s attuned to detect fake news, just like we have filters for spam to prevent it from getting into your inbox. There are ways A.I. can assist humans in identifying fake news, or in the future, do it automatically.

What are some ways the Fake News Challenge has addressed fake news so far?

We kicked it off as a casual wager on Twitter, thinking naively we can jump to the end stage and build a system that can classify real news versus fake news. It turns out it’s a much more subtle problem than that. You run into problems like opinion pieces or satire that falls into a gray area that makes it a much more challenging task that so far requires some human judgment.

We backed off the task of trying to predict the fake versus real distinction to focus on a tool to help fact-checkers by solving a problem. It will allow fact-checkers or journalists to gather the best stories on both sides of an issue. By gathering those pro-con arguments quickly, human fact checkers will be able to quickly assess what the truth is and debunking things that are clearly made up.

How can A.I. help the average user who might read or run into fake news?

We’ve actually started brainstorming about how the kind of tool we’re building can assist the average individual rather than news organizations. Suppose you read an article that makes a claim about a fact in the article. You can imagine that if you highlight it or click a button, you can use our detection tool that finds other content on the internet that takes the pro or con stance and find out just how credible the claim is in the story you’re currently reading.

How might tech companies respond to fake news?

I would love to see a more concerted effort for tech companies. They’ve set up industry groups for other things. They recently created partnerships for A.I., and I’d like to see them do something similar and do a best practices industry group to address fake news.

I think one of the biggest problems is the economy has made it quite lucrative to game the system and get more clicks and eyeballs because that’s how you make money on the Internet now. The model that tech giants have created has seriously undermined the news media industry to make it almost incumbent for news media creators to create tantalizing headlines that get people to click on them for whatever reason.

[The Fake News Challenge has] caught the attention of well-meaning people who see fake news as a big problem and use their machine learning skills and try to address it. I’ve been a little disappointed that the tech community and machine learning community haven’t faced up to the responsibility of using their skills in development to benefit society.

One of the reasons we’ve attracted so many smart people from the tech community is it does offer the opportunity to use some of the cutting-edge machine learning and natural language processing work in A.I. to do something for the social good right here and right now.

This interview has been edited for clarity and brevity.

Photos via Flickr / The Public Domain Review

By Rosalie Chan

Rosalie is an editorial intern at Inverse. She grew up in the Chicago area and studies journalism and computer science at Northwestern University. She has previously worked for TIME and the Chicago Reporter. She likes writing, books, podcasts and running.

Sourced from Inverse Innovation

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4 Ways Office Technology Is Going to Change in 2017

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Sourced from medium.com

We live in an era where we can tell a virtual assistant like Amazon’s Alexa to re-order washing powder while we’re video chatting with friends across the globe and Googling how long it took to build the pyramids.

We can organise a weekend away with friends in a WhatsApp group and have a chat bot change the time of our flight.

But we have almost none of that technology at work.

It won’t be this way for long. Everyone, and not just millennials, clearly want more intelligent software at work. We’re moving from a workplace where Bring Your Own Device (BYOD) is the norm to one where Bring Your Own Software (BYOS) is the expectation.

The return of Clippy? Not so much.

Workplace technology doesn’t just need to catch up — it needs to leap ahead. The demands of a modern digital workplace call for innovations such as smart bots, micro applications, artificial intelligence, and team messaging.

A study of business leaders by outsourcing giant Capita found that 91% of HR Directors see automation as an opportunity, with 76% believing it will drive greater productivity.

Our previous blog post dealt with the dire state of software at work in contrast to the smart software we now have at home. Here, we’ll offer some solutions and set out a vision of a modern digital workplace.

1. The end of email
Two of the biggest roadblocks to modern productivity were actually building blocks in the past: meetings and emails.

Almost everyone hates meetings — it’s that sinking feeling your time would be better spent doing something else. It’s the same with email. According to research by McKinsey as far back as 2012, employees can spend as much as 28 per cent of their day reading and responding to emails. Yet meetings and emails still prevail as the primary time drain, despite there being clearly better ways of communicating and getting things done.

The benefits of a digital workplace go much further though. They can actually help make companies more flexible and agile, not to mention more attractive to talent.

Using modern messaging apps, a unified interface to work becomes possible, where conversations and applications come together in one place. Group messaging replaces email for projects, initiatives and team collaboration. Workflow and actions take place within the conversation, using intelligent bots.

Furthermore, the content within these faster, real-time conversations becomes an easily searchable resource of all past organisational knowledge.

2. Searchable information
Among the manifold problems with enterprise software, one of the principal shortcomings is the archaic user experience. The simple solution is to use an intelligent interface that can access the most relevant functions and data from within these enterprise systems and present them in a more intuitive and streamlined way.

Search remains a real problem at work — looking for the right information in so many different places. The rise of BYOS makes things worse, as people bringing more cloud applications into their work multiplies the numer of potential locations. How much time do you spend asking, “Was that file on Sharepoint or Dropbox? Where are we tracking that project?”

McKinsey found that the aforementioned employees wasting 28 per cent of their time managing email were spending a further 20 per cent on looking for internal information or for colleagues to help them with something.

By connecting your individual applications to a single intelligent app, a universal search function then allows you to search across all content and applications. That means work can be treated as a single entirety, rather than as bits of content scattered across numerous independent silos.

At last, a single search box for work.

3. Everyone gets an assistant
In modern offices, the directors won’t be the only ones with an assistant. Now, with digital assistants becoming smarter, more intuitive and easier to use, anyone within the company can have a Alexa-like helper.

The growing crop of digital assistants are becoming more mainstream, thanks to endorsements from celebrities like Alec Baldwin and Missy Elliott. However, these digital assistants have practical uses in the office. Instead of using a paper calendar or trying to remember a schedule, just talking to a digital assistant can save time and money — and keep employees productive.

4. Real-time data
Data is the lifeblood of modern organisations, offering insights into customers and processes that are critical to decision making. But how easy is it to have the right data to hand, in the right place, at the right time? Most useful data is hidden away in silo’d systems that are hard to access.

But a modern enterprise messaging platform can use micro-apps and bots to talk to these systems in realtime, from anywhere, and get the information you need. Often before you even realise you need it.

Internal data is even harder to come by. Want to know the internal dynamics of your company and gain sentiment analysis from its people? Good luck. Want to know how well your employees like a new programme you started a few months ago? No chance.

But it doesn’t have to be this way. We can use sentiment analysis, bots and natural language processing to talk to thousands of employees — anonymously if necessary — in seconds.

Executives and managers can easily take the pulse of the office and see what can be improved and what is working smoothly, rather than wait for anecdotal feedback when it might be too late.

Businesses deserve better
The verdict on meetings and emails is loud and clear. People don’t like them, they waste huge amounts of time and productivity — and therefore money — and they are holding businesses back. Intelligent workplace technology such as messaging, bots and micro-apps offer new ways of doing everything better. It’s not a case of if you move to better ways of working, but when.

The digital workplace isn’t just new software you use at work. It is a whole new way of working. It’s about working intelligently.

We’re Blink, and we’re building the intelligent interface to work. Blink connects to your existing applications and applies modern intelligence to cut through the noise, automate tasks and help you accomplish more.

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