Artificial intelligence is currently an inherent piece of our everyday lives. We don’t consider anything but seeing personalized product recommendations on Amazon or optimized real-time directions on Google Maps. The day isn’t far when we will have the option to bring driverless vehicles to take us home, where Alexa would have just arranged dinner subsequent to checking stock with our smart oven and fridge. That being stated, enterprise adoption of AI has been increasingly estimated however, it is advancing quickly to achieve tasks extending from planning, anticipating, and predictive maintenance to customer service chatbots and the like.

Understanding the province of Artificial Intelligence deployment, how comprehensively it is being utilized, and in what ways it is challenging for some business chiefs. AI and different innovations are progressing altogether quicker than many foreseen only a couple of years ago. The pace of development is accelerating and can be difficult to grasp.

KPMG 2019 Enterprise Artificial Intelligence Adoption Study is conducted to pick up understanding into the province of AI and automation deployment efforts to select huge top organizations. This is associated with in-depth interviews with senior pioneers at 30 of the world’s biggest organizations, as well as secondary research on work postings and media coverage. These 30 exceptionally powerful out of Global 500 organizations represent noteworthy worldwide economic value, on the whole, they utilize roughly 6.2 million individuals, with total incomes of US$3 trillion. Together, they additionally represent a noteworthy part of the AI market.

Almost all the employees so surveyed consider Artificial Intelligence to be playing a job in making new champs and losers. Artificial intelligence has wide enterprise applications and the possibility to move the competitive position of a business. The advances under the AI umbrella are as of now adding to product and service upgrades and they will be significant drivers of innovation for completely new products, services, and business models.

O’Reilly survey results show that AI efforts are developing from prototype to production, however, organization support and an AI/ML skills gap remain snags.

Artificial intelligence adoption is continuing apace. Most organizations that were assessing or exploring different avenues regarding AI are currently utilizing it in production deployments. It’s still early, however, organizations need to accomplish more to invest their AI efforts on strong ground. Regardless of whether it’s controlling for regular risk factors, inclination in model development, missing or poorly conditioned data, the tendency of models to degrade in production—or instantiating formal processes to promote data governance, adopters will have a difficult, but not impossible task ahead as they work to build up reliable AI production lines.

At present, AI requires refined HR, for example, data scientists to build machine learning models, and computational linguistics experts to compose knowledge extraction applications. This confines AI applications and developments to a chosen few and subsequently constrains the speed of adoption within the enterprise. However, this situation won’t keep going long.

The most exceptional thing about these outcomes is their year-over-year consistency. Similar skill areas that were dangerous in 2019 are again hazardous in 2020 and by about similar margins. In 2019, 57% of respondents referred to an absence of ML modeling and data science mastery as a hindrance to ML adoption; this year, marginally progressively near 58% did as such. This is valid for other sought after abilities, as well. The awkward truth is that the most critical skill shortages can only with significant effort be addressed. The data scientist, for instance, is a hybrid animal: in a perfect world, she should have theoretical and technical expertise, yet down to earth, domain-specific business expertise, too.

Technology organizations are building tools to automate tasks performed by these talented people, in this way empowering even a data analyst or business user to assemble AI applications. For instance, Infosys Nia, a cutting edge AI platform working for big business, merges a few AI advances, machine learning, deep learning, information extraction, natural language generation, among others – with the goal that an enterprise can utilize the right tool for every one of its issues. What’s more, in light of the fact that most functions are automated on the platform, it cuts down the time, cost and effort, of adoption and advancement within the enterprise.

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Over the past few years, articles and blog posts have started to ask some version of the same question: “Why are all websites starting to look the same?”

These posts usually point out some common design elements, from large images with superimposed text, to hamburger menus, which are those three horizontal lines that, when clicked, reveal a list of page options to choose from.

My colleagues Bardia Doosti, David Crandall, Norman Su and I were studying the history of the web when we started to notice these posts cropping up. None of the authors had done any sort of empirical study, though. It was more of a hunch they had.

We decided to investigate the claim to see if there were any truth to the notion that websites are starting to look the same and, if so, explore why this has been happening. So we ran a series of data mining studies that scrutinized nearly 200,000 images across 10,000 websites.

How do you even measure similarity?

It’s virtually impossible to study the entire internet; there are over a billion websites, with many times as many webpages. Since there’s no list of them all to choose from, performing a random sample of the internet is off the table. Even if it were possible, most people only see a tiny fraction of those websites regularly, so a random sample may not even capture the internet that most people experience.

We ended up using the websites of the Russell 1000, the top U.S. businesses by market capitalization, which we hoped would be representative of trends in mainstream, corporate web design. We also studied two other sets of sites, one with Alexa’s 500 most trafficked sites, and another with sites nominated for Webby Awards.

Because we were interested in the visual elements of these websites, as data, we used images of their web pages from the Internet Archive, which regularly preserves websites. And since we wanted to gather quantitative data comparing millions of website pairs, we needed to automate the analysis process.

To do that, we had to settle on a definition of “similarity” that we could measure automatically. We investigated both specific attributes like color and layout, as well as attributes learned automatically from data using artificial intelligence.

For the color and layout attributes, we measured how many pixel-by-pixel edits we would have to make to transform the color scheme or page structure of one website into another. For the AI-generated attributes, we trained a machine learning model to classify images based on which website they came from and measure the attributes the model learned. Our previous work indicates that this does a reasonably good job at measuring stylistic similarity, but it’s very difficult for humans to understand what attributes the model focused on.

How has the internet changed?

We found that across all three metrics—color, layout and AI-generated attributes—the average differences between websites peaked between 2008 and 2010 and then decreased between 2010 and 2016. Layout differences decreased the most, declining over 30% in that time frame.

These findings confirm the suspicions of web design bloggers that websites are becoming more similar. After showing this trend, we wanted to study our data to see what kinds of specific changes were causing it.

You might think that these sites are simply copying each other’s code, but code similarity has actually significantly decreased over time. However, the use of software libraries has increased a lot.

Libraries feature collections of generic code for common tasks, like resizing a page for mobile devices or making a hamburger menu slide in and out. We looked at which sites had lots of libraries in common and how similar they looked. Sites built with certain libraries—Bootstrap, FontAwesome and JQuery UI—tended to look much more similar to each other. This could be because these libraries control page layout and have commonly used default options. Sites that used other libraries, like SWFObject and JQuery Tools, tended look much different, and that might be due to that fact that those libraries allow for more complex, customized pages.

The changes of websites from 2005 to 2016 illustrate what’s happening.

Sites with average similarity scores in 2005 tended to look less similar than those with average similarity scores in 2016.

For example, in 2005, Webshots.com and Yum.com were considered relatively similar, but had somewhat different color schemes and very different layouts. While they both mostly use white, blue and black, the site on the right has a blue background.

Two 2016 sites, Xfinity.com and Gilt.com, on the other hand, are even more similar: They both have a menu bar on the top and are primarily white and black with images. These pages have much less text and make better use of the higher resolution monitors that exist now.

Is conformity healthy?

What should be made of this creeping conformity?

On the one hand, adhering to trends is totally normal in other realms of design, like fashion or architecture. And if designs are becoming more similar because they’re using the same libraries, that means they’re likely becoming more accessible to the visually impaired, since popular libraries are generally better at conforming to accessibility standards than individual developers. They’re also more user-friendly, since new visitors won’t have to spend as much time learning how to navigate the site’s pages.

On the other hand, the internet is a shared cultural artifact, and its distributed, decentralized nature is what makes it unique. As home pages and fully customizable platforms like NeoPets and MySpace fade into memory, web design may lose much of its power as a form of creative expression. The Mozilla Foundation has argued that consolidation is bad for the “health” of the internet, and the aesthetics of the web could be seen as one element of its well-being.

And if sites are looking more similar because many people are using the same libraries, the large tech companies who maintain those libraries may be gaining a disproportionate power over the visual aesthetics of the internet. While publishing libraries that anyone can use is likely a net benefit for the web over keeping code secret, big tech companies’ design principles are not necessarily right for every site.

This outsize power is part a larger story of consolidation in the tech industry—one that certainly could be a cause for concern. We believe aesthetic consolidation should be critically examined as well.

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How often do you now casually converse with a black (or white) box of some sort? Natural language processing (NLP) has become an integral part of our daily lives: Whether we’re asking our smartphone for directions or engaging with Alexa or Google, NLP and its sub-categories are hard at work behind the scenes, translating our voice or text input and (hopefully) providing an appropriate voice or text output.

But what is NLP, and how does it relate to artificial intelligence (AI) in general? The distinctions are important, as NLP has just as much, if not more, value in the enterprise as it has in our personal lives.

AI, defined

Let’s start with AI, the broader category under which NLP and a number of other flavors of machine-based intelligence reside. “AI is the use of intricate logic or advanced analytical methods to perform simple tasks at greater scale in ways that mean we can do more at large scale with the workers we have, allowing them to focus on what humans are best at, like handling complex exceptions or demonstrating sympathy,” says Whit Andrews, vice president and distinguished analyst with Gartner.

AI is essentially some computerized simulation of human intelligence, says Zachary Jarvinen, head of technology strategy, AI and analytics at OpenText, that can be programmed to make decisions, carry out specific tasks, and learn from the results.

With AI, computers can learn to accomplish a task without ever being explicitly programmed to do so, says Timothy Havens, the William and Gloria Jackson Associate Professor of Computer Systems in the College of Computing at Michigan Technological University and director of the Institute of Computing and Cybersystems.

For those who prefer analogies, Havens likens the way AI works to learning to ride a bike: “You don’t tell a child to move their left foot in a circle on the left pedal in the forward direction while moving your right foot in a circle… You give them a push and tell them to keep the bike upright and pointed forward: the overall objective. They fall a few times, honing their skills each time they fail. That’s AI in a nutshell.”

AI vs. NLP, explained

When you take AI and focus it on human linguistics, you get NLP.

Like machine learning or deep learning, NLP is a subset of AI. But when exactly does AI become NLP? SAS offers a clear and basic explanation of the term: “Natural language processing makes it possible for humans to talk to machines.” It’s the branch of AI that enables computers to understand, interpret, and manipulate human language.

NLP itself has a number of subsets, including natural language understanding (NLU), which refers to machine reading comprehension, and natural language generation (NLG), which can transform data into human words. But, says Wayne Butterfield, director of cognitive automation and innovation at ISG, “the premise is the same: Understand language and sew something on the back of that understanding.”

Natural language processing makes it possible for computers to extract keywords and phrases, understand the intent of language, translate that to another language, or generate a response.

NLP has its roots in linguistics, where it emerged to enable computers to literally process natural language, explains Anil Vijayan, vice president at Everest Group. “Over the course of time, it evolved from rule-based to machine-learning infused approaches, thus overlapping with AI.” 

In addition to techniques derived from the field of computational linguistics, NLP (which may also be referred to as speech recognition in some contexts) might employ both machine learning and deep learning methodologies in order to effectively ingest and process unstructured speech and text datasets, says JP Baritugo, director at business transformation and outsourcing consultancy Pace Harmon.

What does NLP look like in action? Let’s look at some of the problems it can solve:

NLP use cases

While NLP may get the most attention in consumer applications today, it has significant implications for organizational IT. “Understanding language and communication in general is huge for the enterprise as we spend most of our day communicating in one form or another,” Butterfield says.

Any area of the business where natural language is involved may be fodder for the deployment of NLP capabilities, says Vijayan. Think chatbots, social media feeds, emails, or complex documentation like contracts or claims forms.

“NLP is typically deployed to categorize content, extract content, analyze sentiment, summarize documents, translate languages, deploy voice-driven or chat-driven interfaces, etc.,” Baritugo adds.

The discipline of NLP may be broken down into any number of more discrete NLP tasks, based on the enterprise’s need. Some may involve serious AI, while others are more rules-based. 

Those tasks then combine to create NLP capabilities like content categorization, contextual extraction, sentiment analysis, document summarization, or speech-to-text and text-to-speech conversion, as examples.

The challenges of enterprise NLP

NLP applications come with the same risks of failure as any other AI deployments, says Vijayan: Most notably, they can suffer from inflated expectations, unclear business cases, and lack of training data. Additionally, NLP opportunities may require entirely different training sets depending on the language being processed and the context, Vijayan says. You may need one set of training data when creating an NLP-enabled solution for processing contracts and entirely different data when coming up with a solution to answer payroll queries, for example.

Butterfield points out some of the hurdles NLP must overcome: Interpreting the actual meaning of voice or text correctly, dealing with sarcasm, understanding local dialects, parsing multiple potential intents, and generating bespoke responses, to name just a few.

Can’t picture where NLP will fit into your organization’s work? Consider this: “NLP is everywhere, and [is] much farther-reaching than the more recently developed smart assistants,” Keiland Cooper, director at ContinualAI and a neuroscience researcher at the University of California, Irvine, recently told us. “Everything from search, email spam filtering, online translation, grammar- and spell-checking, and many more applications [use NLP]. Any machine learning that is done involving natural language will involve some form of NLP.”

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Eager to make extra money on the side, Washington, D.C., resident Paula Alves Silva turned to a gig emblematic of the digital age: She recorded sentences read aloud in the comfort of her home to help train artificial intelligence (AI) software.

Silva completed the tasks in her native Portuguese tongue for Seattle-based startup DefinedCrowd, which develops machine learning algorithms that power products for businesses including heavyweights MasterCard and BMW. Such recordings could be used in voice recognition products introduced in new countries, or to train existing systems to recognize non-native speakers or regional accents, the company says.

Silva earned $20—from 8 to 33 cents per sentence—and considered that satisfactory given the short amount of time it took to complete the tasks. The knowledge that her task would contribute to a new artificial intelligence system was a bonus, she said.

When voice-activated software such as Amazon’s Alexa responds to the simple command of calling Mom, thousands of global workers have helped train the software to ensure that, say, Tom from work isn’t dialed instead. The workers transcribe and annotate recordings that are fed back into the software to improve Alexa’s human speech recognition (sometimes using recordings from unaware consumers, according to Bloomberg).

The rise of AI in the age of the gig economy has ushered in an invisible workforce in which ordinary people, like Silva, train technology to be smarter.

Created in 2015 by CEO and founder Daniela Braga, DefinedCrowd is one of many companies that use so-called crowdworking to teach tech devices how to follow commands. Others including Amazon Mechanical Turk, and Figure Eight, formerly known as CrowdFlower, were established over a decade ago.

In the past two years, DefinedCrowd has grown from 20 employees in 2017 to 110 globally. That’s not counting the 130,000 people across 60 countries that DefinedCrowd says have worked on its tasks. It calls them the Neevo community and says they can work in 50 different languages.

Not all their tasks involve speech: For instance, if the makers of an autonomous vacuum want their machines to clean a living room without running into objects, crowdworkers would annotate the different objects in images of living rooms. Those annotated images would then be used as training data to teach the machines precision, said DefinedCrowd spokeswoman Catarina Salteiro.

“You can think of us as the gasoline of a car,” said Salteiro. “You’ll put good gas in there to make sure that it’s working properly.”

To complete tasks for DefinedCrowd, workers register with their email or social media account on the company’s web platform or the recently launched app. After passing a test based on a skill set such as native French fluency, the worker is sent email notifications of available tasks and paid through Paypal at the end of a gig.

Another DefinedCrowd crowdworker, Rakesh Kumar of Delhi, India, said in an email that the recordings he makes in English and Hindi provide a necessary extra income of nearly $10 per month for about six hours of work altogether. It’s helpful, although he noted the payment “is quite less than other freelance work I do.”

A user gets paid only if their work is high quality and matches the requirements. Several members of the Neevo community tweeted at DefinedCrowd that they hadn’t gotten paid yet, nor received notifications of new tasks in a while. “You guys have not paid many completed tasks and the site seems to be down,” wrote one user in December.

The availability of jobs and the skills required for tasks are dependent upon the projects that clients develop, said Salteiro. Certain languages are in a higher demand for tasks than others. “As we grow our client base, the offer of work on our Neevo platform will increase and more tasks will become available for different languages and at a higher frequency,” she said.

A 2018 International Labour Organization report found that crowdworkers who complete tasks for sites such as Amazon Mechanical Turk are paid low wages. Based on two surveys of 3,500 crowdworkers in 75 countries, the report found a third of them relied on the tasks as their main source of income. The report concluded that across five online global platforms—Amazon Mechanical Turk, Microworkers, CrowdFlower, Prolific and Clickworker—the average pay per hour amounted to U.S. $4.43 for work considered payable. When accounting for work that was rejected, pay that wasn’t received, or the amount of time it took to search for tasks, respondents averaged $3.31 per hour. Similar to Kumar, nearly 90% of the surveys’ respondents said they wanted more work than was available, with workers averaging about 25 hours of crowdwork per week.

Labor law professor Charlotte Garden of Seattle University’s School of Law considers crowdworking a form of outsourcing work that was once done by company employees. Such arrangements “can make workers more vulnerable” by preventing them from advancing into higher roles or enjoying the labor protections that regular employees have, said Garden.

Given the absence of clear guidelines on the treatment of crowdworkers, last week the Allen Institute for Artificial Intelligence (AI2) issued a set of ethical recommendations to AI companies on proper pay, privacy and transparency for crowdworkers. In a blog post, it said U.S. crowdworker companies should pay at least the U.S. average minimum wage of $8.50 per hour. Minimum hourly wage in developing countries should be around $4, given the lower prevailing incomes, AI2 recommended. Companies that employ crowdworkers also should be transparent about how long a task will likely take and conditions that may lead to the rejection of work, AI2 urged.

DefinedCrowd is already following the guideline of calculating payment based on the minimum hourly rate of the country where the job is aimed and the estimated time it will take to complete a task, said Salteiro. She wouldn’t be more specific about the pay ranges.

The lack of governance mechanisms in crowdworking has St. Louis University employment law professor Miriam Cherry wondering about the end goal in relying on temporary workers to train AI data systems. “Is it just efficiency for the sake of efficiency … or is it something that really could help people?”

One answer to Cherry’s question could be found in DefinedCrowd’s work with Portugal’s biggest electricity company, EDP, according to company spokesperson Jorge Simões. Last year, DefinedCrowd’s crowdworkers helped the company determine which electricity poles need to be repaired, a once expensive and time-consuming process that required specialists to survey the poles from a helicopter. DefinedCrowd instead devised a machine-learning algorithm to detect defects in poles from images captured by drones and from helicopters.

To create it, crowdworkers identified the type of damage in 900 images of electric poles that were then used to train and test a damage-detection AI system that monitors the state of poles.

EDP was so satisfied with the work that it became an investor in DefinedCrowd last year. The company is now working with DefinedCrowd to create a voice transcription algorithm that works in industrial environments with loud background noise and the use of technical language by service technicians.

To Simões, the work demonstrated that the most difficult tasks could be “successfully automated, optimizing the final result of the inspection by minimizing human error and visual limitations,” even if it took human intelligence to get there.

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• Movie-goer: Alexa, launch Atom App.
• Alexa: What do you want to do?
• Movie-goer: Alexa, buy tickets.
• Alexa: For which day?
• Movie-goer: Alex, today.
• Alexa: For what movie?
• Movie-goer: Spiderman – Far From Home.
• Alexa: What ZIP code are you in?

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Microsoft Dominates the Charts

Microsoft’s Research Asia team took home first place in eight of the 11 categories in which it participated (out of 19 categories in total). For the remaining three entries (English-Kazakh, Finnish-English, and Lithuanian-English), it won the second prize.

When we think of AI, voice recognition and translation likely isn’t the first thing that comes to mind. It’s certainly not as sexy as a robot butler doing your cooking. However, if AI is to become as disruptive as forecasts predict (Accenture speculates $8.3 trillion for the US market alone by 2035), then understanding us when we talk to it, and speaking to us in a language we can understand, is a cornerstone feature. AI is defined by its ability to simulate human intelligence, after all.

If you’ve used apps such as Google Assistant, Siri, Alexa, or Cortana, particularly with a non-American accent, you’ll know there’s vast room for improvement. The same goes if you’ve traveled abroad relying on a translation app, or if you’ve made use of automatic translation in your business, career, or social life. This pertains to anything from Google Translate on websites or document translations on Facebook. In all instances, it’s clear that it’s very much a work in progress.

Microsoft Climbing Back to the Top

The Microsoft brand is synonymous with tech’s early days, but while it’s still a mainstay, the behemoth doesn’t inspire the same awe it used to. Now that hot new tech moves a mile per minute and Google has taken the crown as a one-stop shop for everything from browsers to email to cloud storage.

Other than Windows, Microsoft seems to have lost favor with its original audience, the home user. But the company isn’t content with staying the stock standard corporate SaaS and cloud service provider it has become.

In the past few years, it’s been hard at work on two of the most promising technologies of the near-future: Blockchain and Artificial Intelligence.

So, Microsoft’s recent quiet win is a more significant feat than it appears to be. The market leader is committed to dominating the tech of tomorrow.

Marrying AI and Blockchain

The company has already added BaaS (Blockchain as a Service) to its arsenal. Now let’s fast-forward to the time when Microsoft will expand its AIaaS (AI as a Service) beyond current offerings like Azure Bot Service to offer a more sophisticated AI product catalog.

It’s not a big leap, then, to imagine that there might be a day when these two converge under the tech giant’s lead. Since Microsoft operates primarily at the enterprise level, this could be the mainstream push needed to accelerate the blending of two technologies into one powerful force. They are, after all, a natural fit, as numerous companies combining the two shows.

Though Microsoft could lead a centralized AI-Blockchain convergence, it would ultimately pave the way for decentralized solutions to be developed and refined. Blockchain already allows for a lower entry-level to high tech than most small and medium-sized businesses have hitherto had access to. By combining blockchain with AI, currently still one of the costliest R&D technologies, a new wave of business innovation will open itself up to become more efficient, safe, and transparent.

Many proponents of decentralization oppose the Galactus-like force with which mega corporations guzzle up the lion’s share of everything from profits to groundbreaking frontiers. However, it paves the way, smashing through the gigantic barriers to entry that only a company with Microsoft-scale resources can bring to the table. In so doing, it opens up the canal for smaller fish with less capital to follow.

In this arena, Microsoft doesn’t disappoint. It’s already democratizing access to simplified machine learning tools so that more developers will join the ranks to utilize AI in a transformed future.

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Amazon continued an autumn ritual this week releasing a new generation of Echo smart speaker products that ‘do stuff’ around the house. Echo, better known by its wake word, ‘Alexa,’ can be queried about the weather, stream news and music on demand and serves as a robotic assistant that responds to voice commands to control home lighting and much more. With this year’s introduction of 13 new products and applications, Amazon’s Alexa will connect its users to premium speakers, a smart wall clock and even to a new microwave oven that will ensure that you effectively warm up your just delivered meal kit.

Alexa is seemingly everywhere. ‘She’ is the artificial intelligence (AI) that lives with us. More than simply a smart speaker assistant in our living room, Alexa is rapidly becoming a presence throughout out our homes, offices and cars. When does such a presence that is always there, always on, always ready to help, always prepared to play a game or to tell a joke (mostly lame jokes) and even knows many of your preferences become something more? Is AI and connected home intelligence rising in influence in our daily lives just as our human connectedness appears to be falling?

Social isolation is a crisis that spans the generations. My colleagues at the MIT AgeLab, in partnership with Tivity Health, Health eVillages and the Jefferson College of Population Health, convened a summit on rural aging and social isolation. In a survey reported at the summit it was revealed that 29% of rural older adults do not see friends or family most days. Equally surprising was a Cigna Health survey conducted earlier in the year reporting that even young adults, ages 18 to 22 years old, have such strong feelings of loneliness they may be the loneliest generation.

A study published earlier this year in the Journal of Social and Personal Relationships suggests that it takes 50 hours with a person to consider that person a casual friend. The study further reports that it takes 200 hours to make a close friend. Few of us think of the ‘face time’ it takes to make a friend. Admittedly we spend many, many hours with coworkers that do not necessarily become our friends. To move from co-worker to a lasting adult friendship, the researchers argue you must spend time together beyond the workplace and invite co-workers into your life and into your home. This level of interaction, argue the study authors, may transform a co-worker into a friend you can count on.

Humans tend to anthropomorphize the ‘things’ we spend time with and use often. We name our boats, some have oddly intimate relationships with their cars. We even discipline devices when they don’t behave as desired. Just think of what you muttered to the office copier last time it jammed. I even had a friend that named and spoke to his power saw. No, ‘things’ do not readily show emotions like another human or even like a pet, but humans can become attached to things. As I observe in my book The Longevity Economy, the rapidly developing field of social robotics is producing animal-like and human-like ‘bots as therapeutic devices for everything from managing PTSD to a form of companionship for older adults that does not need to be fed nor walked.

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“Theory of mind is clearly a crucial ability,” for navigating a world full of other minds says Alison Gopnik, a developmental psychologist at the University of California, Berkeley, who was not involved in the work. By about the age of 4, human children understand that the beliefs of another person may diverge from reality, and that those beliefs can be used to predict the person’s future behavior. Some of today’s computers can label facial expressions such as “happy” or “angry”—a skill associated with theory of mind—but they have little understanding of human emotions or what motivates us.

The new project began as an attempt to get humans to understand computers. Many algorithms used by AI aren’t fully written by programmers, but instead rely on the machine “learning” as it sequentially tackles problems. The resulting computer-generated solutions are often black boxes, with algorithms too complex for human insight to penetrate. So Neil Rabinowitz, a research scientist at DeepMind in London, and colleagues created a theory of mind AI called “ToMnet” and had it observe other AIs to see what it could learn about how they work.

ToMnet comprises three neural networks, each made of small computing elements and connections that learn from experience, loosely resembling the human brain. The first network learns the tendencies of other AIs based on their past actions. The second forms an understanding of their current “beliefs.” And the third takes the output from the other two networks and, depending on the situation, predicts the AI’s next moves.

The AIs under study were simple characters moving around a virtual room collecting colored boxes for points. ToMnet watched the room from above. In one test, there were three “species” of character: One couldn’t see the surrounding room, one couldn’t remember its recent steps, and one could both see and remember. The blind characters tended to follow along walls, the amnesiacs moved to whatever object was closest, and the third species formed subgoals, strategically grabbing objects in a specific order to earn more points. After some training, ToMnet could not only identify a character’s species after just a few steps, but it could also correctly predict its future behavior, researchers reported this month at the International Conference on Machine Learning in Stockholm.

A final test revealed ToMnet could even understand when a character held a false belief, a crucial stage in developing theory of mind in humans and other animals. In this test, one type of character was programmed to be nearsighted; when the computer altered the landscape beyond its vision halfway through the game, ToMnet accurately predicted that it would stick to its original path more frequently than better-sighted characters, who were more likely to adapt.

Gopnik notes that the kind of social competence computers are developing will improve not only cooperation with humans, but also, perhaps, deception. If a computer understands false beliefs, it may know how to induce them in people. Expect future pokerbots to master the art of bluffing.

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In 2016 an artificial intelligence bot, “Hoffbot,” used neural networks to write all of David Hasselhoff’s lines for a bizarre short film called Sunspring.  Just three months ago Botnik Studios used a predictive algorithm to create a four-page script performed by Zach Braff of Scrubs. By 2019, most leading AI providers will offer tools and libraries for building AI-powered natural-language generation, image manipulation, and other generative use cases.

Artificial Intelligence exists as an aid to creativity across every discipline.  This year more solutions will come to market–in all verticals–that use leading-edge AI approaches known as generative adversarial networks (GANs) to algorithmically create digital and analog objects of all sorts with astonishing accuracy.

While many have daily experiences with Alexa and Spotify it is important that you demo some of the user friendly digital tools that are out there (no coding required).  If you need a new website, grid.io can design one for you in a snap, just provide the content and let AI do the work for you.

Amper music puts AI to work to compose a non-melodic musical bed for your next video or podcast, no stock music required. The web interface is completely intuitive and user friendly.  You hit render and have your track.

I realize it is Easter/Passover weekend, but for those of you who celebrate Halloween year-round, Shelley, is worth a try.  Co-author a horror story with AI and have some unexpected fun with your family and friends this weekend.

The legendary Buckminster Fuller once described a designer as “an emerging synthesis of artist, inventor, mechanic, objective economist and evolutionary strategist.”  While I am a believer in lateral thinking and support this definition in general, I would probably add programmer or data scientist to its definition today.

The relationship between humans and the intelligent machines an increasing feature in our lives, and we all should actively discuss and participate in this critical, magnification point we are in the midst of today.  For creatives and designers seeking more tools I encourage you look at these autonomous design resources, and everyone should read Garry Kasparov’s new book Deep Thinking.  Reimagine your life in the era of AI, and develop your own personal POV around AI and its impacts on your business.

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