How Media Companies Can Use AI To Keep And Win Subscribers

Think of your favorite movie as a kid, say in the first 10 years of your life. Now think of your favorite movie from the past decade. Do you have one? Do you have 100?

In a world with basically infinite content, choice is one of our greatest joys—and frustrations. With each passing year, consumers seem to grow more fickle and demanding, regularly moving to the platforms and publications that offer not only the best catalog but also the best customer service, content experience, user interface, and bang for the buck. And even these features may not be enough, as the recent upheaval among the major streamers has shown.

Holding on to viewers, readers, and listeners has become more important than ever. Yet most consumers can only maintain so many subscription services at once. The goal for media companies needs to be to sustain their interest, and with as much share of the consumer’s wallet as possible.

As such, churn is now the most prominent enemy of the media and entertainment industry business model. Consumers can be mercurial, sensitive to price and changes in content catalogs. Just as adding a service has rarely been easier, so is dropping one, which consumers have shown themselves more than willing to do when a channel is no longer serving their needs.

With these challenges front and center, leading media and entertainment companies are increasingly turning to data analytics and personalized content recommendations to improve customer experience and retention. In the dog-eat-dog digital world, it’s no longer the loudest bark that gets the most attention. It’s about pairing the right breed to the sensibilities of a specific person, and having the best stable of information and offerings to make that match and keep it going.

As subscriptions have risen, so has churn

A good example of the challenge of churn can be seen with streaming video. Deloitte performed a series of surveys in 2020 to gauge how consumers were changing their media consumption habits amidst the pandemic.

In January 2020, the average consumer in the United States subscribed to three paid streaming services; by October 2020, the number of subscriptions had risen to five. Overall, a positive development for media, but with the increase in subscriptions came a commensurate increase in churn.

In January 2020, Deloitte found that only 20% of people who had subscribed to a paid streaming service had cut at least one of those services in the past 12 months. By October, that number more than doubled, with 46% of consumers canceling a streaming service in the preceding six months. And at that time, 34% of consumers said that they’d both added and canceled a streaming service since the pandemic started.

Why did viewers churn? Deloitte noted that 62% of people in 2020 who had signed up for a service and then canceled it had done so because they signed up to watch a specific show, then canceled the service when they’d finished watching it. Price, as always, was also a big factor. In October 2020, 31% of people who canceled a service did so because it was too expensive. Another 28% canceled because a free trial or discount period ended. About 21% cut the service because of a lack of content they found interesting.

No matter how focused on addressing churn a company may be, what can they do when the whims of the consumer are so sensitive and fluctuate wildly?

Companies need to find ways to anticipate what their audiences want at least as well as the audience does—and certainly better than their competition. Two of the best defenses against churn are having an organized data platform, then using that data to personalize content recommendations and customer experience.

Data maturity is the first step to mitigating churn

Data maturity is the ability to have accurate and reliable data that can be utilized through cloud platforms, with advanced analytics informing every decision. It is one of the most important steps for media and entertainment companies to take in the effort to mitigate churn

In our experience working with companies as varied as Spotify, The New York Times, Major League Baseball, and Hearst, the first step to achieving data maturity is building a company culture where data is prioritized within the strategic business framework, and where funding is allocated to technology and human resources to build a mature data ecosystem.

Data maturity should not be a bolt-on to existing practices, but needs to become central to the company’s strategic business goals. Companies that have achieved data maturity tend to have specific teams or centers of excellence that manage goals, strategy, and tactics of the organization’s data framework.

In a 2020 survey by EY Global Media & Entertainment, 62% of media and entertainment executives said they saw the increasing availability of data as an opportunity. About 56% prioritized first-party data, versus only 13% who prioritized third-party data. When asked about their top three data priorities, 44% said that the consolidation of customer data was a top concern. About 40% said developing proprietary data sources was a priority, while 39% prioritized improving the relevance of data.

Consolidating data out of data silos to a unified data platform is the biggest challenge that most companies will face when building a roadmap to data maturity.

A report by Deloitte in partnership with the Google News Initiative on how news and media companies can achieve digital transformation through data outlined some of the technologies that companies can adopt to achieve data maturity. Two elements are required. First, media and entertainment companies need to be able to collect and store data that they are gathering from their planet-sized audiences and users with the tools listed below.

  • Data management platform (DMP) helps to manage first-party data segments and integrate third-party data and push data to other systems.
  • Data lake or warehouse, a central repository of data from multiple sources.
  • Cloud storage for reliability, security, and scalability.
  • Customer relationship management (CRM) the backbone of customer data that records and tracks user interactions with registered subscribers.
  • Customer data platform (CDP) to record and track customer data across platforms and devices.

Second, companies need to make sense of all that data and derive actionable insights from it.

  • Data analytics and reporting tools that can collect, organize, and analyze data from multiple sources.
  • Artificial intelligence and machine learning tools. Derive even more insights through AI/ML-enabled capabilities such as computer vision, speech and object recognition, and text translation.
  • Propensity modeling helps build a better understanding of customer preferences, fulfilling the key elements of personalization to prevent churn.

Below we describe some of the unique data sources available to media and entertainment companies and how it can be applied to artificial intelligence and machine learning.

Media and entertainment have unique data sources

Media and entertainment companies can improve personalization by tapping two unique sets of data particular to the industry: media content and audience behavior.

Media content includes easily identifiable metadata such as the title, headline, genre, topic, or format of a piece of content. But media data can also include context of the actual content itself.

For instance, AI tools like object recognition and computer vision can detect items within a movie and then add the description of the object to the searchable metadata of the content. If a television show contains a border collie, the AI can recognize the good dog and surface the show in a search for “shows with dogs.” Or with speech recognition and translation, AI can build a data set of the dialogue within a movie and make certain keywords part of the search for that show.

Behavioral data of the audience can be used in a variety of ways. Data can come from many different sources including a person’s location, device, browsing and scrolling, user profile, engagement, billing preferences, purchase and support history. Companies can help personalize experiences with this data by understanding how people interact with content and how best to engage with them, such as what times of the week are best for push notifications or when a person might be most amenable to a content recommendation.

Using artificial intelligence to personalize user experience

If you’ve ever wondered how your favorite streaming service seems to so uncannily know what you want to watch—even better than you might—the answer is probably some clever AI. Personalization is the practice of combining the new, massive datasets outlined above with machine learning and artificial intelligence to create experiences tailored to the specific needs and behaviors of an individual person.

Personalization is often associated with content recommendations. For example, about 70% of what is viewed on YouTube comes from a personalized recommendation. Certain streaming services are known to have some of the best content recommendation systems in the business. The goal with the personalization of content is to surface a new show, video, movie, podcast, song, band, album, article, or blog to the person at precisely the right moment.

Personalization is also an important element in search. Consider that with the right data inputs, two users searching for the same keywords could get vastly different results attuned to their consumption preferences. In both cases, content better suited to a person’s interest will keep them from looking around at other platforms or publications, helping to reduce churn.

The same is true for more traditional outlets, as well. Take a recent example from the (digital) pages of Newsweek. The publication’s chief technology officer, Michael Lukac, recently noted that “Google Cloud Recommendations AI has not only improved our click-through rate by 50% to 75% and subscription conversion rate by 10% but also allowed us to increase total revenue per visit by 10%.”

If you’re looking for more information about why personalization matters and how to bring it to your own services and experiences, discover more in our new ebook, Personalizing Media for Global Audiences.

Lluis Canet, Solutions Lead for Media Analytics and AI, Google Cloud

Source: Churn It Down: How Media Companies Can Use AI To Keep And Win Subscribers

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AI And The Secret To Employee Happiness

When I started working as a mainframe operator in IT in 1988, I felt like I was part of a secret club. None of my family understood what I was doing; my friends would ask, “what’s a mainframe and why do you have to work nights?”

My onboarding took months, and a typical workday began with staring at a blank screen. Since mainframes didn’t come with a mouse, I would enter memorized commands like “=3.4” and “Sys3.AF*” to navigate the data sets I needed to find.I don’t think many workers today would put up with that.

Any manager who has tried to hire an employee today will agree the war for talent is real. Job perks like free lunches and on-site laundry just don’t cut it anymore. To recruit talent today, there’s really one thing that every enterprise needs to do: Make work better.

Make work easy

I’ve found that companies invest in digital transformation for three reasons: To work faster, to work more efficiently, and to change or expand their business models. But the end result of any digital transformation should be a better experience, and leaders often neglect the everyday experience of the workers who actually achieve these goals.

Consider this. Outside of work, most people have grown used to finding a new home, getting pet care, and organizing travel all with just a swipe of their finger on the touchscreen. They expect the same level of ease when it comes to the technologies they use at work. It’s no coincidence that the latest release of the Now Platform invested so heavily in improving user experience.

Sure, the interface looks beautiful. But the experience goes deeper than the surface by making the usage more intuitive. Good user experience is about simplifying and hiding complexity so that using it comes naturally to anyone. Make work easy.

Flex on flexibility

Many workplaces have returned to on-site or hybrid work, but I don’t think we’ll bring back the rigid workday schedule. The last two years have taught us that, while face-to-face and real-time interactions are invaluable, many other tasks can be done just as well, if not better, asynchronously.

Yes, it wasn’t fun to work from a makeshift standing desk in the kitchen while keeping one eye on a freakishly fast toddler. It’s no wonder why some employees have eagerly returned to the ergonomic office stocked with free snacks. But some of us love attending a meeting without sitting in traffic, having lunch without navigating a packed cafeteria, or taking a two-hour afternoon break to compensate for that evening call with Tokyo. You have to accommodate both types—and everyone in between.

Leaders learned the hard way in 2020 that you can’t just flip a switch and change the way a business is run. You have to stay ready with workplace technology that can support various—and changing—work models.

Flexibility, supported with a solid digital foundation, is no longer a choice. Clearly communicate what your employees need to deliver and let them decide where, when, and how. Or you can try to force a rigid work model and watch your talent flock to another employer.

AI and human intelligence aren’t mutually exclusive. They work best when they work together.

Automate the mundane

Automation has freed employees from many repetitive tasks, making work more fulfilling and creative. The digitization of work can go a step further by tapping artificial intelligence that effectively sorts through massive amounts of data and makes prescriptive recommendations. AI can even be used to make it easier for employees to be promoted internally—a huge factor in retaining and rewarding your workforce.

There’s a misconception that AI is designed to replace human workers. But for me, artificial intelligence is actually about the interface between people and machines, making lives more interesting by automating the mundane, removing friction, and presenting the right information and insights.

Better together

Knowledge workers thrive when they can harness technology to make more effective decisions. These decisions aren’t only reactive but also proactive—something that AI enables through its predictive power, which can anticipate and adjust to a world full of constantly changing variables.

When it comes to digital transformation, we think of how it impacts the bottom line by improving speed and efficiency. But how do we improve speed and efficiency? By empowering our talent with the delightful and intuitive experiences they deserve.

AI and human intelligence aren’t mutually exclusive. They work best when they work together.

Dave Wright is ServiceNow’s chief innovation officer and acts as an evangelist for how to improve workplace productivity. He has worked with thousands of

Source: AI And The Secret To Employee Happiness

.

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Does AI Have The Answer To The Customer Experience Riddle?

The telecommunications industry is so many enterprises wrapped into one—they have to get every aspect of customer experience right. It’s a challenge every organization can learn from.

Everywhere you look, there’s another business attempting to harness data, analytics, and artificial intelligence to help them increase sales and crack the code to provide higher-quality, lower-cost goods and services.

Travel and hospitality companies want to make persuasive, personalized offers at just the right moment to drive bookings. Retailers are honing inventory management to better anticipate customer demand and drive same-store sales—while navigating the current supply chain challenges. Hospitals, health insurers, and even governments utilize AI to comb through vast data sets to develop predictive models of disease.

Financial institutions have accelerated credit and risk underwriting decisions using AI/ML models; they’ve also enhanced customer satisfaction online and on the phone with AI-driven virtual assistants. Manufacturers are employing AI to improve process efficiency, enable predictive maintenance, and scale quality control efforts in their core operations. And everyone is trying to reduce customer churn.

When you stop and think about it, the telecommunications industry and its myriad communications service providers (CSPs) do all of this—advertising, supply chain, online and physical stores, operations and maintenance, customer care—and more, for both consumers and businesses. Thus, CSPs offer a unique lens through which to examine how companies in any industry can utilize AI to convert data to insights and information to actions.

The pressure on CSPs to take action, to do more with less, has never been greater.

Growing demands on the network, growing demands for the network

CSPs are in an unusual position: As global demand for data has grown 256% between 2016 and 2020, intense competition has meant that revenues grew less than 13% over the same period. Operators have so far relied upon technical advances and gaining scale efficiencies through consolidation to manage the gap, but one of the greatest untapped opportunities remaining is to become dramatically better providers of customer service.

While the concept of “AI-driven customer service” may seem like an oxymoron—after all, what do algorithms really know about serving people better?—the answer now turns out to literally be more than you could ever know.

The decline of third-party cookies has many operators renewing their focus on collecting and acting upon their own first-party data across the customer lifecycle.

In an evolving industry like telecommunications, the race for customer acquisition and retention is paramount. This is driving heightened operator focus on better advertising performance and retail sales—whether in their own stores, their retailer partners, or various digital channels. AI can help here with informing target audience creation, creative optimization, and inventory forecasts.

Related: Google and Automation Anywhere reimagine customer experience by giving virtual agents a boost

The decline of third-party cookies has many operators renewing their focus on collecting and acting upon their own first-party data across the customer lifecycle. Here, too, AI models can help CSPs identify and act upon signals, such as usage patterns or customer care calls. This type of customer context, an often overlooked signal, can be especially valuable when it comes to identifying “at risk”’ customers for retention efforts.

Contact centers supporting upwards of 100 million subscribers are an expensive endeavor. Several top global operators have turned to conversational AI to decrease agent volumes and document AI tools to shorten call handle times. Some companies report Google’s conversational AI can cut the number of customer inquiries that need a human agent by half.  Besides helping reduce costs and maintain margins for the operator, many customers also appreciate the efficiency and control of self-service.

Furthermore, while CSPs may not have a “factory” in the traditional sense, their network operations are far-flung and national, even global, in scale. They must operate at the industry standard of “five 9’s” (i.e., 99.999%) reliability for emergency communications and simultaneously deliver massive amounts of bandwidth to meet the public’s insatiable demand for communications and data.

And if it seems like a lot now, just consider the 23% annual bandwidth growth the industry will undergo with the rise of 5G and all the IoT, VR, and Web3 experiences that come with it. Keeping up, and keeping customers happy, will take new levels of network automation and predictive maintenance that only AI can provide.

Related: Deploying and operating cloud-based 5G networks

TELUS, a world-leading communications technology company based in Canada, is already leveraging conversational AI through Google Cloud’s CCAI Insights to better serve its roster of global clients and their customers.

Read more:

Most Important Artificial Intelligence Skill: A Sense of Imagination

The Rise of Artificial Intelligence in Business and Society

How Artificial Intelligence Powered Customer Service Will Help Customer Support Agents

Artificial Empathy: Call Center Employees Are Using Voice Analytics to Predict How You Feel

“As a company that supports our customers through many channels, we are able to provide a streamlined experience that transitions from digital support to live agent support,” Phil Schultz, vice-president of customer experience, told us in an interview. “With this new experience, we are able to provide a simple, consistent, intuitive, and friendly experience for simpler tasks, with our agents being able to focus on supporting our customers’ more complex issues. CCAI and Data Insight help TELUS ensure our customers get the support they need, when they need it.”

Realizing the value of AI for customer experience

Of course all of these grand data aspirations are easy to articulate but hard to implement—at Google Cloud, we know these challenges first hand. It’s why we empathize with the added challenges CSPs face from their legacy systems, and from the network complexity that has arisen over generations of technology and industry consolidation. It’s also why we’re excited to be partnering with top CSPs to solve these challenges.

Through our experiences in these partnerships, Google Cloud has identified four key success factors for driving business value from AI applied across the customer experience:

  1. Clear Focus. Success starts with a clear and shared understanding of what CSPs are solving for and the business value of doing so. This clarity will drive every activity to follow, with the business value serving as an important motivator to plow through challenges.
  2. No Silos. Nearly all enterprises struggle with how to break down data silos. Successful companies have a proactive strategy for data integration, data management, and analytics platforms to address the current as well as future needs.
  3. Data-driven. Choosing which part of the problem to tackle first and how to do so is a major determinant of value. Leading companies rely on data to help inform their approach to everything from deciding which use cases to tackle first, to developing and optimizing AI-driven virtual assistants.
  4. Shared risk & reward. We have found that success takes a partnership in which incentives are aligned, with partners having skin in the game.

In Google Cloud’s new report, “Using AI to win the customer experience battle in telecommunications,” we delve into these dimensions, using CSPs as a vehicle, and examine new and innovative ways to apply AI, and best practices for building an AI program focused on delivering value, not just promises.

For TELUS, the investment of time and planning required to execute on AI was apparent from the start. “Through our 10-year partnership with Google, TELUS is able to dive into all the phases of our customers’ journey ensuring it is easy for them to get the support they need,” Schultz said. “This allows our customers to more easily service themselves online, and our world class agents to have all of the information they need to provide quicker and easier support to our customers.”

AI solutions offer the exciting potential to transform the customer experience and bend the value curve for enterprises. Realizing this value requires thoughtful preparation, technology excellence, iterative progress, and a committed, aligned partnership. No company—whether an operator, cloud provider, or solution provider—can afford to let the sizable program investment become just another hype-cycle science experiment that fails to deliver business results.

Sean Allbee, Senior Principal, Customer Value and Transformation Advisory, Google Cloud

Sean works with telecommunications and media companies

Amol Phadke joined Google Cloud in June 2020 as managing director: global telecom industry solutions. He is responsible for working with the product and

Source: Does AI Have The Answer To The Customer Experience Riddle?

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Now You Can Rent a Robot Worker For Less Than Paying a Human

Polar Manufacturing has been making ​metal ​hinges, locks, and brackets ​in south Chicago for more than 100 years. Some of the company’s metal presses—hulking great machines that loom over a worker—date from the 1950s. Last year, to meet rising demand amid a shortage of workers, Polar hired its first robot employee.

The robot arm performs a simple, repetitive job: lifting a piece of metal into a press, which then bends the metal into a new shape. And like a person, the robot worker gets paid for the hours it works.

​Jose Figueroa​, who manages Polar’s production line, says the robot, which is leased from a company called Formic, costs the equivalent of $8 per hour, compared with a minimum wage of $15 per hour for a human employee. Deploying the robot allowed a human worker to do different work, increasing output, Figueroa says.

“Smaller companies sometimes suffer because they can’t spend the capital to invest in new technology,” Figueroa says. “We’re just struggling to get by with the minimum wage increase.”

The fact that Polar didn’t need to pay $100,000 upfront to buy the robot, and then spend more money to get it programmed, was crucial. Figueroa says that he’d like to see 25 robots on the line within five years. He doesn’t envisage replacing any of the company’s 70 employees, but says Polar may not need to hire new workers.

Formic buys standard robot arms, and leases them along with its own software. They’re among a small but growing number of robots finding their way into workplaces on a pay-as-you-go basis.

The pandemic has led to shortages of workers across numerous industries, but many smaller firms are reluctant to write big checks for automation.“Cost declines are great for the diffusion of a technology.” Andrew McAfee, principle research scientist, MIT

“Anything that can help reduce labor count or the need for labor is obviously a plus at this particular time,” says Steve Chmura, chief operating officer at Georgia Nut, a confectionery company in Skokie, Illinois, that has been struggling to find employees and also rents robots from Formic.

The robot-as-employee approach could help automation spread into smaller businesses more rapidly by changing the economics. Companies such as Formic see an opportunity to build large businesses by serving many small firms. Many are mining the data they collect to help refine their products and improve customers’ operations.

Shahan Farshchi, an investor in Formic, likens the state of robotics today to computing before personal computers took off, when only rich companies could afford to invest in massive computer systems that required considerable expertise to program and maintain. Personal computing was enabled by companies including Intel and Microsoft that made the technology cheap and easy to use. “We’re entering that same time now with robots,” Farshchi says.

Robots have been taking on new jobs in recent years as the technology becomes more capable as well as easier and cheaper to deploy. Some hospitals use robots to deliver supplies and some offices employ robotic security guards. The companies behind these robots often provide them on a rental basis.

Jeff Burnstein, president of the Association for Advancing Automation, an industry body, says rising demand for automation among smaller companies is driving interest in robotics as a service. The approach has seen particular traction among warehouse fulfillment firms, Burnstein says.

It might eventually become normal to pay robots to do all sorts of jobs, Burnstein says, pointing to RoboTire, a startup developing a robot capable of switching the tires on a car. “As more and more companies automate in different industries, you’re seeing more receptivity to robotics as a service,” he says. Search our artificial intelligence database and discover stories by sector, tech, company, and more.

The International Federation of Robotics, an organization that tracks robot trends globally, projected in October that the number of robots sold last year would grow 13 percent. One market analysis from 2018 projected the number of industrial robots that are leased or that rely on subscription software will grow from 4,442 units in 2016 to 1.3 million in 2026.

“Cost declines are great for the diffusion of a technology,” says Andrew McAfee, a principle research scientist at MIT who studies the economic implications of automation.

McAfee says robots themselves have become cheaper and more user friendly in recent years thanks to the falling cost of sensors and other components, a trend that he expects will continue. “They are the peace dividend of the smartphone wars,” he says.

Dustin Pederson, CFO of Locus Robotics, a company that leases robots for use in warehouses, says his company’s revenue has grown sixfold over the past year amid rising demand for ecommerce and a shortage of workers. “To be able to step in with a subscription model makes automation a lot friendlier,” Pederson says. “And we are still early on in the overall adoption of robotics in the warehousing industry.”

It’s unclear—even to economists—what impact the growing use of robots will have on the supply of jobs. Research from Daron Acemoglu and Pascual Restrepo, economists at MIT and Boston University, respectively, suggests that the adoption of robots from 1990 to 2020 resulted in fewer jobs and lower wages overall.

But one study of robot adoption in Japanese nursing homes, from January 2021, found that the technology helped create more jobs by allowing for more flexibility in working practices. And another study, from 2019, also found that robot adoption among Canadian businesses had often affected managers more than workers by changing business processes.

Lynn Wu, an associate professor at the University of Pennsylvania’s Wharton School and a coauthor on the 2019 study, says she expects robots paid by the hour to become more common. But she notes that in contrast to many information technologies, few businesses know how to use robots. “It’s going to take longer than people think,” she says.

For now, most robots found in industrial settings are relatively dumb, following precise movements repetitively. Robots are gradually becoming smarter thanks to use of artificial intelligence, but it remains very challenging for machines to respond to complex environments or uncertainty. Some researchers believe that adding AI to robots will prompt companies to reorganize in ways that have a bigger impact on jobs.

Saman Farid, CEO of Formic, says the company hopes to position itself to be able to offer more capable robots to all sorts of companies in the future. “Robots are going to be able to do a lot more tasks over the next 5 to 10 years,” Farid says. “As machine learning gets better, and you get to a higher level of reliability, then we’ll start implementing those.”

By:

Will Knight is a senior writer for WIRED, covering artificial intelligence. He was previously a senior editor at MIT Technology Review, where he wrote about fundamental advances in AI and China’s AI boom. Before that, he was an editor and writer at New Scientist. He studied anthropology and journalism in the UK before turning his attention to machines.

Source: Now You Can Rent a Robot Worker—for Less Than Paying a Human  | WIRED

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More Great WIRED Stories

AI Can Write Code Like Humans Bugs and All

Some software developers are now letting artificial intelligence help write their code. They’re finding that AI is just as flawed as humans.

Last June, GitHub, a subsidiary of Microsoft that provides tools for hosting and collaborating on code, released a beta version of a program that uses AI to assist programmers. Start typing a command, a database query, or a request to an API, and the program, called Copilot, will guess your intent and write the rest.

Alex Naka, a data scientist at a biotech firm who signed up to test Copilot, says the program can be very helpful, and it has changed the way he works. “It lets me spend less time jumping to the browser to look up API docs or examples on Stack Overflow,” he says. “It does feel a little like my work has shifted from being a generator of code to being a discriminator of it.”

But Naka has found that errors can creep into his code in different ways. “There have been times where I’ve missed some kind of subtle error when I accept one of its proposals,” he says. “And it can be really hard to track this down, perhaps because it seems like it makes errors that have a different flavor than the kind I would make.”

The risks of AI generating faulty code may be surprisingly high. Researchers at NYU recently analyzed code generated by Copilot and found that, for certain tasks where security is crucial, the code contains security flaws around 40 percent of the time.

The figure “is a little bit higher than I would have expected,” says Brendan Dolan-Gavitt, a professor at NYU involved with the analysis. “But the way Copilot was trained wasn’t actually to write good code—it was just to produce the kind of text that would follow a given prompt.”

Despite such flaws, Copilot and similar AI-powered tools may herald a sea change in the way software developers write code. There’s growing interest in using AI to help automate more mundane work. But Copilot also highlights some of the pitfalls of today’s AI techniques.

While analyzing the code made available for a Copilot plugin, Dolan-Gavitt found that it included a list of restricted phrases. These were apparently introduced to prevent the system from blurting out offensive messages or copying well-known code written by someone else.

Oege de Moor, vice president of research at GitHub and one of the developers of Copilot, says security has been a concern from the start. He says the percentage of flawed code cited by the NYU researchers is only relevant for a subset of code where security flaws are more likely.

De Moor invented CodeQL, a tool used by the NYU researchers that automatically identifies bugs in code. He says GitHub recommends that developers use Copilot together with CodeQL to ensure their work is safe.

The GitHub program is built on top of an AI model developed by OpenAI, a prominent AI company doing cutting-edge work in machine learning. That model, called Codex, consists of a large artificial neural network trained to predict the next characters in both text and computer code. The algorithm ingested billions of lines of code stored on GitHub—not all of it perfect—in order to learn how to write code.

OpenAI has built its own AI coding tool on top of Codex that can perform some stunning coding tricks. It can turn a typed instruction, such as “Create an array of random variables between 1 and 100 and then return the largest of them,” into working code in several programming languages.

Another version of the same OpenAI program, called GPT-3, can generate coherent text on a given subject, but it can also regurgitate offensive or biased language learned from the darker corners of the web.

Copilot and Codex have led some developers to wonder if AI might automate them out of work. In fact, as Naka’s experience shows, developers need considerable skill to use the program, as they often must vet or tweak its suggestions.

Hammond Pearce, a postdoctoral researcher at NYU involved with the analysis of Copilot code, says the program sometimes produces problematic code because it doesn’t fully understand what a piece of code is trying to do. “Vulnerabilities are often caused by a lack of context that a developer needs to know,” he says.

Some developers worry that AI is already picking up bad habits. “We have worked hard as an industry to get away from copy-pasting solutions, and now Copilot has created a supercharged version of that,” says Maxim Khailo, a software developer who has experimented with using AI to generate code but has not tried Copilot.

Khailo says it might be possible for hackers to mess with a program like Copilot. “If I was a bad actor, what I would do would be to create vulnerable code projects on GitHub, artificially boost their popularity by buying GitHub stars on the black market, and hope that it will become part of the corpus for the next training round.”

Both GitHub and OpenAI say that, on the contrary, their AI coding tools are only likely to become less error prone. OpenAI says it vets projects and code both manually and using automated tools.

De Moor at GitHub says recent updates to Copilot should have reduced the frequency of security vulnerabilities. But he adds that his team is exploring other ways of improving the output of Copilot. One is to remove bad examples that the underlying AI model learns from. Another may be to use reinforcement learning, an AI technique that has produced some impressive results in games and other areas, to automatically spot bad output, including previously unseen examples. “Enormous improvements are happening,” he says. “It’s almost unimaginable what it will look like in a year.”

Source: AI Can Write Code Like Humans—Bugs and All | WIRED

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