11 Tech Experts On How Small Businesses Can Effectively Leverage Machine Learning

We hear a lot these days about how technologies such as artificial intelligence and machine learning are changing the ways businesses operate. Even casual followers of the technology industry have read stories about how household-name companies including Amazon, Google and IBM are using AI and ML for everything from tailored product recommendations to piloting autonomous vehicles to developing computers that can defeat chess champions and win TV game shows.

Entrepreneurs and small-business owners may think ML is a tool only “the big guys” can afford or use effectively; but there are plenty of ways even startups can use the technology to boost their odds of success, from predicting future purchases (just like the big guys do) to taking labor-intensive tasks off their team members’ plates. Below, 11 members of Forbes Technology Council share ways machine learning can help entrepreneurs and small-business owners and why they’re so effective.

1. Scoring And Prioritizing Leads

Machine learning can help small businesses better identify leads and provide valuable insights into them. ML can not only be used to “score leads” but also to provide insights on the probable time to close. This allows entrepreneurs to create better portfolios from their leads and prioritize them effectively, as you want to balance high-value leads in terms of those that will close “now” versus those that will need some nurturing. – Neerav Vyas, Capgemi

2. Eliminating Time-Intensive, Repetitive Tasks

Technology for the sake of technology is never a good idea. The pandemic has left workers burned out, and the labor shortage is real. Entrepreneurs and small businesses should look for AI/ML technology that helps eliminate the time-intensive, mundane tasks employees do so they can focus on higher-order work. This will keep productivity moving and keep employees motivated. – Prasad Ramakrishnan, Freshworks

3. Analyzing Data

Many entrepreneurs and small businesses do not have the budget for one data analyst, let alone a team of analysts. Machine learning is a way to cover your analytical base without the positional cost. And while most entrepreneurial initiatives start out with a great vision, expecting yourself to peer with otherworldly wisdom into analytics at every turn is simply not tenable. – Denver Hunter, PenServ Plan Services, Inc.

4. Identifying Purchasing Patterns

One of the simplest ways to utilize machine learning in smaller businesses is to use its capabilities to identify patterns, such as purchasing patterns, and to predict near-future purchasing. This can drastically cut supply chain and storage costs—smaller businesses often neither have significant resources in these areas, nor can they afford them. Predictions are excitingly accurate, even with sudden trend changes. – Markus Bernhardt, OBRIZUM

5. Handling Personal Scheduling

Time is money, and entrepreneurs have little of either. Smart scheduling solutions have been a game-changer for me. They analyze my calendar, automate coordination, and streamline how meetings get booked, rescheduled and canceled. – Sean Steele, Infolock

6. Turning Insights Into Ideas

Machine learning can drive home the importance of data and the commodity of technology. Entrepreneurs and small-business owners will find machine learning closing the loop to shorten the distance between doing and dreaming. Operationally, this means turning insights into ideas that are executed for real business impact, with those learnings creating even more insights. – Cleve Gibbon, Wunderman Thompson

7. Understanding Consumer Behavior

In light of recent technological advances, machine learning is becoming more accessible, which can help small businesses and entrepreneurs understand consumer behavior. This alone can provide you with better insight into customer needs and the updates or changes that should be made in order for your company’s products/services to best serve your customers. – Matt Pierce, Immediate

8. Analyzing Customer Feedback

Machine learning should definitely be used to analyze the feedback from your customers. Ideally, you will want to know how to evolve your products and understand what would make a customer become interested in or evolve with your product. Therefore, the feedback loop is highly important; but understanding what is noise and what is truly critical feedback is even more so. Then you can react accordingly. – WaiJe Coler, InfoTracer

9. Identifying Product ‘Winners’ And ‘Losers’

ML can help automate the process of product development. By using artificial intelligence to predict which products are likely to be successful, business owners can save time and money by passing on projects that would have failed anyway, freeing up their resources for more promising endeavors. – Leon Gordon, Pomerol Partners

10. Business Planning

There’s a lot more potential for machine learning to help in business planning processes than is realized at the moment. While machine learning is generally popular in aspects such as marketing and customer service, it is rather underutilized in planning, despite its high potential. Business planning is an important and complicated process where human efforts are not always enough to stay competitive. – Peter Abualzolof, Mashvisor

11. Cleansing Data, Processing Documents And Combating Fraud

ML can be overwhelming for small-business owners because of the learning curve. To start with, small businesses can certainly leverage ML for customer data cleansing, fraud detection, automation of document processing and guarding against phishing attacks. These areas are common pain points, and ML can certainly help small businesses solve these issues as well as realize some long-term cost savings. Later on, small businesses can invest more money in automation. – Kiran Palla, Kiran Palla

Successful CIOs, CTOs & executives from Forbes Technology Council offer firsthand insights on tech & business.

Source: Council Post: 11 Tech Experts On How Small Businesses Can Effectively Leverage Machine Learning

Critics by Gero Keil

Machines have become smart but we are still years away from human-like intelligence. However, until then it is possible for us to understand how machines can learn from data. Depending on the use case, data may be classified, segment, interpreted, extracted and you can run regressions on it. Looking at your business processes (and data) in this manner is the entry ticket to the promised land of AI.

Artificial intelligence has been helping big companies for years. Only recently has AI become available to small businesses through AIaaS (AI as a service). The benefits for small businesses are similar to those of big companies—let’s take a look:

Reduce time spent on mundane tasks: using AI to automate repetitive tasks and analyze large amounts of data helps small businesses free up time to focus on other tasks.

Lower costs: implementing an AI solution reduces costs in the long run by improving efficiency and freeing up time and resources to develop the business.

Improve service: using AI to understand your consumers and how they feel about your product can help you proactively improve your offerings.

Improve security: AI solutions can be implemented to help small businesses detect any suspicious cyber activity before it becomes an issue. AI solutions can help businesses improve customer service in a number of ways, from analyzing customer sentiment to categorizing support requests.

Having insights into customer behavior and sentiment is key for improving your support and product—often before customers themselves request it. AI can help businesses by improving efficiency and productivity when it comes to business operations. Whether that’s by categorizing incoming emails based on their contents, or identifying promising leads and unpromising leads.

AI can help with both simple and complex issues, and can help you and your team free up a lot of time for other important tasks. AI enables businesses to continuously improve their product and offerings with insights into how users interact with them. Simply use AI to monitor what users are talking about and the sentiment behind their words, and make changes accordingly.

AI can help highlight product issues before they’re vocalized by users—meaning you can solve problems before they even arise. Gone are the days where only the biggest organizations leveraged AI—small businesses can now use AI to optimize their processes, too. AI solutions make harnessing the power of machine learning easier than ever before—let’s see how to implement AI in your small business.

Machines have become smart but we are still years away from human-like intelligence. However, until then it is possible for us to understand how machines can learn from data. Depending on the use case, data may be classified, segment, interpreted, extracted and you can run regressions on it. Looking at your business processes (and data) in this manner is the entry ticket to the promised land of AI….

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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 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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