The release of the film “Dolittle” has landed with a thud. During its first week, the box office receipts came to a disappointing $29.5 million. Keep in mind that the film–which stars Robert Downey Jr.–cost Universal Pictures $175 million to produce.

Of course, Hollywood film-making is a boom-bust business. But might fast evolving technologies like AI improve the odds?

Perhaps so.

Just look at Warner Bros. The company recently struck a deal with Cinelytic, which has built an AI-infused project management system. It is focused on the green-lighting process, such as by helping to predict the potential profits on new films.

Now this is not to imply that Cinelytic has essentially cloned the genius of Steven Spielberg or James Cameron. Rather, the technology helps with the myriad of smaller tasks, such as analyzing the impact on territories, the bidding process for talent and so on. This should free up time for filmmakers to work on more important tasks.

There are other startups gunning for this market opportunity, like Pex. The company has an AI-driven platform that analyzes movie trailers to determine the potential revenue generation. This is done by crunching data like views, shares, likes and other forms of online engagement. As a result, a studio can change a trailer to make it more impactful.

What’s Next?

AI in Hollywood is still in the early stages. There will certainly be lots of trial and error. But it also seems likely there will be much growth in the coming years.

Here’s what some other experts have to say:

• Richard Boyd, who is the co-founder and CEO of Tanjo: “AI is absolutely an effective solution to the question of content development. Netflix, Amazon and Google all know that if you want to understand people, you don’t ask them questions, you monitor what they do with their time, money, and attention.”
• Bret Greenstein, who is the Global Vice President and Head of AI at Cognizant: “AI can absolutely help Hollywood reduce risk, in the same way it is reducing risk in other industries. This has nothing to do with computer generated imagery (CGI). Rather, it is about quantifying and analyzing content, understanding and predicting audience preferences, and creating models that allow more experimentation with color, images, costumes, cgi, and dialog to predict the best outcomes for a film. With AI, it may be possible to quickly edit content based on sudden societal changes that would impact the audience. Understanding, for example, recent news and events might cause a director to decide to rapidly adjust dialog or even visuals to avoid backlash.”
• Ganesh Sankaralingam, the Director of Data Science and Machine Learning at Latent View Analytics: “Today there’s a wealth of actionable data available that movie studios and streaming entertainment companies can use to manage risk. Using AI and advanced analytics can not only help mitigate against box office flops, but also identify potential hits. After all, every Hollywood studio except Paramount turned down ‘Raiders of the Lost Ark’ and there are countless other examples. So by keeping close tabs on what audiences watch and categorizing that data, studios can develop ‘taste communities.’ These communities can highlight overlaps between actors, directors and genres that might otherwise have gone unnoticed.”

The Nagging Issues

There should still be tempering of expectations with AI and Hollywood. Let’s face it, how much do we really know about the creative process? Well, it’s still very sketchy.

And besides, there are so many variables that can determine the success or failure of a film.

“AI might find that certain actors and types of dialogue and plot structures worked well in the 1980s and without any current movies with similar properties there is no reason for an AI to expect that the same recipe that worked in the 1980s won’t still work now,” said Noah Giansiracusa, who is an assistant professor of Mathematics and Data Science at Bentley University. “Of course, such a movie may in fact do well for reasons of nostalgia, but whether the AI ‘grasps’ this nostalgia effect really comes down to whether there are enough similar movies produced in recent times to develop an expectation of modern performance. Another serious issue is that AI reflects all the biases and prejudice that have become embedded in data. If women and minorities have not done as well as white men as leads and writers and directors in the box office, then every AI algorithm we currently have will anticipate that trend continues and could potentially trap us in shortsighted thinking rather than recognizing that change is possible.”

In other words, don’t expect AI to put creative people out of work any time soon. But then again, the technology is likely to be a great help in the film-making process.

“AI is a tool,” said Greenstein. “Judgement of people is essential. Judgement informed by AI will make big money in the box office.”

Tom (@ttaulli) is the author of the book, Artificial Intelligence Basics: A Non-Technical Introduction, as well as the upcoming book, The Robotic Process Automation Handbook: A Guide to Implementing RPA Systems.

There has been a lot of talk about the enormous potential and market opportunity for smart factories. Yet, for all the talk, the reality on the ground for a lot of enterprises in manufacturing is that their machines are not yet connected to any network. Before they can even talk about digitization and predictive maintenance, they need to first get connected.

There are many sectors in which this lack of connection has traditionally been a pain point (consumer packaged goods, for example), but other sectors, particularly oil, gas, and automotive, have had the ability to measure and monitor things through connectivity and sensors for a long time.

However, to date they have been using data and analytics primarily for engineering value and manufacturing KPIs. Enterprises need to start looking at data from the angle of its business impact⁠. For instance, how can data be utilized to minimize downtime? Analytics have the potential to totally reconfigure the factory, but they must be utilized across the enterprise, from labor deployment to supply chain management.

Areas of Opportunity

Though each industry can utilize the interconnected nature of Industry 4.0, certain sectors will especially benefit. Companies in industries with products that rely on optimal mixtures of materials or chemicals are especially ripe for innovation.

For instance, there is a massive opportunity to build and leverage algorithms that can create recipes for chemicals by looking at the composition of a recipe and coming up with the optimal mix. This algorithmic blending brings value by quickly discovering relationships between chemicals and bringing new products to market. It saves valuable time and effort, as this process, when done manually, is very laborious. Humans would have to analyze each individual chemical and the relationships between them in a painfully time-consuming process otherwise.

Machines trained on chemical data not only do this much faster; they can also present the information in a visual manner that maximizes further analysis and immediately demonstrates the new product’s value. Armed with algorithmic blending capabilities, R&D teams can suddenly generate a series of new products with massive revenue potential. Moreover, algorithmic blending can be used for any chemical product, creating an enormous spectrum of applications.

In pharmaceuticals, for example, drug synthesis could be revolutionized through algorithmic blending. Companies have the internal data on their chemical compounds, but the data is not organized, and so algorithms cannot be trained on this data to optimize mixtures. The same goes for oil and gas, where they are mixing products such as petroleum, chemicals, and lubricants. With countless applications, algorithmic blending has the potential to revolutionize some of the largest and most profitable sectors in the world. It will be a key feature of Industry 4.0 in the future.

How Can Data Help?

It is easy to build solutions that leverage the power of analytics, but if they are not used, then the solutions are a waste. How can enterprises build solutions that work for them and drive value?

First, enterprises should start by creating a small pool of internal capabilities and people who understand how analytics can actually solve domain-specific problems. A huge limitation for companies is that only some have an internal team to focus on analytics initiatives. Forming cross-functional teams with one layer of people internally supported by another layer of external partners is a good place to start. Without internal analytics capabilities and buy-in from the top, analytics solutions become the shiny new toy for one to two quarters and then are forgotten.

Second, in order for AI and predictive analytics solutions to interface well with existing platforms, manufacturing should be able to integrate the cloud with manufacturing ERP solutions. These solutions work as an integration layer across all elements of an operation, vs. as a one-off solution.

Finally, it is important to implement pilot programs. These pilot programs should be designed to be repeatable. The big question enterprises should ask about ROI is how quickly they can repeat the program across other divisions in the enterprise. The goal is to be able to build one pilot program and then deploy it multiple times across the company and across divisions.

From Steam to Dreams

Built on the back of the steam engine, the First Industrial Revolution fundamentally transformed the way people and organizations worked. Today we are witnessing how data is transforming organizations in a similar way with Industry 4.0. The factories of tomorrow will be totally connected, allowing us to leverage the full impact of data analytics.

Over the past decade, the term “disrupt” became synonymous with innovation and success. A Google Trends search reveals a steady climb of the term’s use throughout the 2010s to a peak in July 2019.

As the 2010s come to a close, the big question for enterprises is how to start leveraging all of this disruptive technology to create true transformation. Here are five areas of disruption that hold significant promise to move from hype to driving true value for businesses and consumers over the next decade.

Bringing Context To Conversational AI

Earlier this year, IBM (via Nanalyze) spoke with 30 of AI’s most influential researchers and thought leaders to ascertain their predictions on the future of conversational AI. Though the speculation was diverse, all 30 agreed on one thing: Conversational AI will see important developments sooner rather than later. The consensus was that within three to five years, advances in AI will drive computers over their biggest conversational hump: understanding context.

Context is one of the biggest challenges in bringing conversational AI mainstream. The variety of conversational clues that allow humans to understand sarcasm or irony is the thing that most eludes conversational AI. Using two methods of machine learning (ML), supervised and reinforcement learning, AI experts are starting to zero in on the problem of context, but we still have a way to go before context is conquered.

For now, chatbots providing customer support is the dominant trend across industries; in the enterprise data analytics field, we are seeing AI voice assistants that are able to answer enterprise queries in natural language.

Tracking Transit With Blockchain

Some have called blockchain “a solution in search of a problem,” and there’s definitely been a lot of hype over the past few years. According to a forecast by Gartner, Inc. (via AiThority), only 10% of companies will have achieved radical transformation using blockchain technologies by 2023. Yet Gartner also forecasts that blockchain will start to climb out of the trough of disillusionment as early as 2021, and it could generate as much as $1.55 trillion in new business value by 2025.

Blockchain’s immutable digital ledger has the most promising applications (outside of cryptocurrency) in sectors with extensive documentation, such as payments, insurance, healthcare and finance. This is perhaps most true of the transportation sector, where paperwork and tracking are especially complex. Some of the largest maritime carriers have already joined IBM and Maersk’s blockchain-enabled “TradeLens” platform.

With the FAA recently certifying UPS as the nation’s first drone airline, 2020 will be the first year in the era of drone delivery. Blockchain will play a large role in helping the FAA track this upcoming proliferation of drones, with companies such as Red Cat having already developed “black boxes” for drones using blockchain technology.

Cybersecurity: Fear Of The Cloud

2020 will be the year of cloud security anxiety. According to a survey conducted by Cyber Security Hub, 85% of executives view it as one of their largest cybersecurity threats. Though AWS, Azure and Google have worked hard to bring down costs and increase security measures, vast data storage will always be vulnerable to attack, and these attacks continue to grow in quantity and quality.

With more connected devices comes the possibility that those devices and the networks connecting them will be hacked. Cybersecurity will also become increasingly important with new regulations like the California Consumer Privacy Act going into effect January 2020. Data security solutions focused on compliance will continue to gain traction.

As fears of data breaches grow, many CTOs should turn to AI for cybersecurity in 2020. AI tools can be used to detect fraud using pattern recognition such as business email compromise, in which companies are sent multiple invoices for the same work. As AI systems develop, they will become better at detecting these attempts, but they will also face increasingly complex AI-powered attacks.

Augmented Reality: Trained On Success?

Though popularly regarded as an entertainment technology, augmented reality (AR) will develop some pretty interesting enterprise applications into 2020. According to a story published by Analytic Insights, virtual reality (VR) and AR will play a large role in sectors such as education, navigation systems, advertising and communication.

Perhaps the most useful development will be in training. For instance, according to a report published by Mint, XR Labs is using VR/AR as a low-risk tool for low-skilled employees. Using their technologies, trainees can gain highly interactive experience doing dangerous tasks such as repairing power-generating windmills without having to climb hundreds of feet in the air.

The challenges, however, are that AR applications aren’t universally easy or cheap to develop — especially the more sophisticated they get. For use cases like job training, the cost of mistakes has to be high enough to justify companies investing in them.

The Analytics Of 5G

Right now, 5G is trickling out in a handful of cities across the United States, but that trickle will turn into a stream by the end of 2020, as many of the largest network providers have concrete plans to expand their 5G networks.

5G promises speeds that are five times faster than peak-performance 4G capabilities, allowing users to download movies in five seconds. This lightning-fast connection could revolutionize mobile communication.

With such limited deployment, however, there have been few use cases. As the technology matures, new business use cases will need new data analysis. Data analytics will play an increasingly important role in shaping how 5G is used. Will it be a consumer technology or an enterprise technology? Maybe data analytics can help us find out.

Disruption To Transformation

Every year, the business and tech community talks about what’s next, and those trends often remain the same from year to year. While the trends above have been talked about, 2020 will be a launchpad to move these trends from “talk” to true transformation across industries. AI and ML applications will drive and enable much of this innovation and accelerate the move from disruption to transformation. I’m excited to see a new wave of innovation in action over the next year and beyond.

Originally published in Forbes.

With the concepts of wellness and mindfulness becoming ever more important for consumers, fitness has become a more relevant goal for many people. Work done by LatentView Analytics looked at approximately 150 million data points including information on products, product usage, product reviews as well as search terms and social media conversations over the last 10 years. The data revealed the following major trends about gyms and fitness:

–   Working out at home is not a trend. It is a seasonal fad that peaks in the winter months when people have made New Year’s resolutions and home workouts are not likely to grow overall. There are a few companies that have grown rapidly in the home workout market and they are rare exceptions. Most notable is Peloton but most other equipment makers are targeted at clubs or have not been nearly as successful as Peloton in reaching consumers buying home workout equipment.

–   Community in gym workouts is the top motivator for consumers to go to gyms. The data are not clear whether the social motivator for going to gyms is the obligation to a trainer or class or whether the social interactions consumers have with fellow members is the catalyst or whether it’s just having other people around. But social experiences at gyms is by far the highest motivating factor for joining and going. Having a professional setup in a gym is also rated highly.

–   Technology development in fitness is growing and consumers are driving that growth. Although technology is not spurring more interest in home workouts, fitness apps are widely used at gyms and in outdoor workout and fitness settings. Fitbit is the best known of such devices; interest in that specific product has peaked. (Google has agreed to buy Fitbit, no doubt to leverage the health information it has gathered. With revenues and earnings off its peak, the deal could be a good way to reinvigorate the brand.)

I recently caught up with my colleague, Kim Karmitz, who has been studying the fitness industry and is on a related panel of experts for the upcoming Fitness and Active Brands Summit next month. That conference is awarding innovative fitness startups and, not coincidentally based on the LatentView data, its awards are largely to fitness technology companies. The technology driving fitness now breaks down into three categories: performance improvement, experiencing the gym in new ways and marketplaces.

Coaching And Performance Improvement

Two companies are using new technology to help amateurs be better at their sport with real-time, personalized instruction that uses artificial intelligence (AI). In the video below, the female voice you hear is an AI program created by a company called Asensei which puts sensors in your workout clothes to monitor your movements. It can then give you highly specific instructions about how to move your body based on what it senses through your clothing. At the moment, it works for yoga and rowing and Asensei is developing other sport modules. Asensei is working with apparel manufacturers to incorporate its technology into branded garments.

A different approach to market is being taken by another company, Sensoria whose founder previously ran a $14 billion business at Microsoft. Sensoria has taken an “Intel inside” approach to developing a platform for wearables of all types that uses artificial intelligence to help amateur athletes perform their sports better. The video below demonstrates how Sensoria helps a runner perform better, faster, safer, with greater endurance. Sensoria’s software can be incorporated into any garment or footwear by a manufacturer.

Moving The Gym To New Venues

Two companies are changing the way gyms are experienced. One brings gaming into gyms. [Based on the non-scientific sample of people I’ve spoken with,] If you’re over 40, Black Box VR will not be very interesting. But if you’re under 40, it will be one of the most interesting, exciting things you’ve ever seen. A user in a gym puts on a virtual reality headset and attaches to weights made for the purpose. A video game begins in which there’s no hand-held controller, the user’s body movements control the game. As the user moves, the weights are lifted. Because of the weights a user gets a complete workout by the time a game is over. If you love playing video games but are not highly motivated to work out in a gym, this will bring you in and keep you there. Like other video games, it is always changing and evolving based on your skill. It is designed to be played either against the computer or against other people who can be either nearby or anywhere else in the world. The video below demonstrates.

Another company called Forte moves the gym class to any other location in the world. It installs hardware and software into workout studios and gyms to bring live and on-demand fitness classes to consumers when they can’t make classes at the gym. Interestingly, Forte has found that consumers prefer live classes because they are real and authentic over recorded or repeated content. Consumers want new content all the time and don’t want to watch a repeat of exercise classes they’ve already taken. It’s consistent with the LatentView data indicating that social experiences are a key driver of gym memberships.

Marketplaces

Like so many other sectors, gyms and fitness are seeing the development of marketplaces that match providers with users. One of those is Athlete’s Guide, a digital marketplace that connects NCAA athletes with high school athletes to help them train better and mentor the younger athletes. The company is the first to be approved by the NCAA to provide income for college athletes. The appeal of the experience is for high school athletes to work with a role model in something that the younger students are highly engaged with. Parents of high school students like it as a way to enhance their child’s skill as well as keep them involved with sports

These technology-driven businesses are the kind of companies that are now driving the fitness business. Consumers interest in new fitness technology coupled with the continued desire for gyms is clearly how consumers want to manage their own fitness. Whether gyms and studios can keep up with the desire for new technology will determine their future success.

Karmitz says that data gathering is one of the economic drivers of new technology in fitness and gyms as we are already seeing in the acquisition of Fitbit by Google and that there will be more such deals. Karmitz points out that having Google enter the industry proves that big tech companies want to be players in the industry, won’t be afraid to invest big money and will push valuations up. She says the market now is in a land-grab mode, companies that can get on a path to gather consumer data and sell their technology profitably will be highly valuable in the future. That means timing is important now in the fitness business. No one knows how long this trend will last and companies that can embed their technology into consumer habits and gain share will become highly valuable. Their investors will be able to exit at very attractive multiples. Companies that don’t have the resources to grow now may miss the trend. As a result, we are likely to see a lot of technology innovation in the fitness and gym industry as well as a great deal of capital coming into the industry.