Benjamin Valdez, a rideshare driver with Uber and Lyft in the Los Angeles area, used to drive seven days a week when the gig was more lucrative--but he says he makes far less per ride these days. When Valdez started driving, around nine years ago, he told me that he could earn anywhere from 60 to 85 to drive from West Hollywood to downtown Los Angeles at peak surge, a roughly 6-to-10-mile trip depending on the specific route. Now, if "the stars align," he can earn between 25 and 35 for the same trip. "It's gotten harder and harder to make money," he said. In recent years, rideshare drivers like Valdez have experienced shrinking incomes as the companies continue to increase their cut from each ride.

Summary: Whether you're a data scientist building an implementation case to present to executives or a non-data scientist leader trying to figure this out there's a need for a much broader framework of strategic thinking around how to capture the value of AI/ML. There are many articles written from a tools perspective about how to take advantage of specific capabilities of AI. Those encompass for example chatbots from NLP or image classification based on CNNs. To be clear, I'm talking about the expanded definition of AI that should more correctly be called AI/ML since the more mature field of machine learning is full of good implementation lessons ranging from marketing to fraud to forecasting. But whether you're a data scientist building an implementation case to present to executives or a non-data scientist leader trying to figure this out there's a need for a much broader framework of strategic thinking around how to capture the value of AI/ML.

By now we all understand the trade-off involved in using the Internet: We let companies collect data about us, and in return they offer a more personalized user experience. But what if I told you that the long-term arc of this trade-off is beyond anything you can possibly imagine? Everything we do online generates data, which platform companies carefully collect and categorize to create digital profiles. Their artificial intelligence (AI) systems then correlate our digital profiles with those of other users to determine what we see online: how our search queries are interpreted, what posts are included in our social media feeds, what adverts we are shown, and so on. This kind of micro-targeting, based on individual psychological profiling, exploits what Daniel Kahneman, 2002 Nobel prize winner in economics, called "fast" thinking – the decisions we make quickly and without conscious consideration, such as whether to click on a link, watch another video, keep scrolling through our timeline, or put down the phone.

Summary: Whether you're a data scientist building an implementation case to present to executives or a non-data scientist leader trying to figure this out there's a need for a much broader framework of strategic thinking around how to capture the value of AI/ML. There are many articles written from a tools perspective about how to take advantage of specific capabilities of AI. Those encompass for example chatbots from NLP or image classification based on CNNs. To be clear, I'm talking about the expanded definition of AI that should more correctly be called AI/ML since the more mature field of machine learning is full of good implementation lessons ranging from marketing to fraud to forecasting. But whether you're a data scientist building an implementation case to present to executives or a non-data scientist leader trying to figure this out there's a need for a much broader framework of strategic thinking around how to capture the value of AI/ML.

Observing electronic ecosystems and value chains change over time is fascinating. For instance, the design chain for mobile devices fundamentally changed over the past two decades with waves of disaggregation and aggregation. Today, the area of computing and data centers is amid tectonic shifts and transformation, with the combination of hyperscale, networking, artificial intelligence (AI), and machine learning (ML) fundamentally re-shuffling value creation. Back in 2002, Grant Martin and I wrote "A Design Chain for Embedded Systems" for IEEE Computer. We described the embedded SoC provider-integrator design chain and argued that "what used to be a vertically integrated process within each product company has become significantly fragmented. Platform-based design can accelerate the flow in this chain."

"The potential benefits are huge, since everything that civilization has to offer is a product of human intelligence; we cannot predict what we might achieve when this intelligence is magnified by the tools AI may provide, but the eradication of disease and poverty are not unfathomable. Because of the great potential of AI, it is important to research how to reap its benefits while avoiding potential pitfalls." It is a sunny morning in Bangalore. Rahul is a young machine learning expert working for a globally dominant technology platform company. He has come up the hard way from a low income family. He secured excellent marks in school and cracked the engineering entrance exam.

Shaywitz and Gibson make the implicit assumption that AI will be considered unproven by Biotech investors until assets picked by machines make it through clinical trials. But "not yet in the clinic" is too powerful an objection -- that benchmark would exclude many of the most exciting and best-funded biotech platform companies. To me, the more interesting questions are "Why do these platform companies get funded?" Because many of the most interesting therapeutic targets have zero lead compounds and are considered undruggable, the value of AI can be -- and has been! The clinic is a powerful conceptual and practical boundary, and so it is easy to assume that the IND divides drug discovery into an easy-and-cheap domain before and a difficult-and-expensive domain after. David Shaywitz wrote, "Often, little, if any, value is placed on earlier-stage assets or the platform itself", and Chris Gibson concurs when speaking about progress that "absent proof of concept data in human patients, is discounted severely."

According to our research, the total installed base of devices with Artificial Intelligence will grow from 2.694 billion in 2019 to 4.471 billion in 2024. Billions of petabytes of data flow through AI devices every day. However, right now, most of these AI devices are working independently of one another. Yet, as the volume of data flowing through these devices increases in the coming years, technology companies and implementers will need to figure out a way for all of them to learn, think, and work together in order to truly take advantage of the potential that AI can deliver. The key to making that a reality is multimodal learning, and it is fast becoming one of the most exciting – and potentially transformative – fields of AI.

It's about why we should be optimistic about a future that includes AI. The contrarian thing is that I think it's net beneficial even to people in rural parts of the country. I was a poor kid from rural central Virginia--Campbell County, a little town called Gladys. I went back there a year ago for the book. All the industry there evaporated years ago. Tobacco, textiles, furniture manufacturing all went poof. But some interesting things are emerging there now, some of which is powered by AI and advanced automation.

In the last two decades, the software industry provided a healthy breeding ground for incubating new businesses and ideas. From solving the day-to-day problems of end-users to building complementary tools for software developed by large companies, startups thrive in the disruptive market. They compete to differentiate based on the value they deliver to customers. But the changing dynamics of the industry have made it extremely risky to be an independent software vendor or a startup in the cloud and AI market. There was a time when large platform companies delivering enterprise software chose not to compete with ISVs.