Artificial Intelligence Technologies Driving Four Key Technology Market Applications
Louis Lehot, business lawyer and partner at Foley & Lardner LLP in Silicon Valley, and formerly the founder of L2 Counsel, P.C.
Artificial intelligence (AI) is disrupting technologies, markets and applications everywhere. But what does it really mean? Professors Andreas Kaplan and Michael Haenlein defined AI as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation.” AI’s ascendancy has been made possible by exponential advances in computing power and the proliferation of devices capturing quantum of data. During this pandemic, we have a new appreciation for the international exchange of information about the spread of COVID-19, and the artificial intelligence technologies that are powering models about the spread and how to control it are critical to human survival. We can also imagine how much more productively the response to the novel coronavirus could have been handled (and still could be) with the help of sophisticated data collection and predictive AI technologies currently used in business transformations.
In the second half of 2020, businesses that innovate, adapt and integrate AI into their business models will improve their chances of surviving and prospering as the COVID-19 disruption is controlled (if it can be). Those that don’t will fall by the wayside. While there are innumerable uses for AI technologies across industries, entrepreneurs, technologists and smart investors are digging deep for ways to implement those technologies to disrupt established markets. Applications for artificial intelligence will enable an accelerated global economic recovery to lift us out of a crippling recession.
Here are four ways in which AI could be used to change the post-pandemic economy:
In edge computing, data collection, processing and distribution are located near the IoT devices that generate the information. As these devices proliferate, keeping data flow local and distributed, as opposed to transmitting to and from a cloud, should reduce latency. AI plays a central role in IoT devices. Training and inferencing software are tools that make the devices smart. As the number of embedded AI chips, whether inferencing or not, proliferate in IoT devices, the processing power, networking and storage at the edge mushrooms, making transfer with the cloud less necessary. According to the Gartner Group, edge computing and cloud computing are evolving as complementary models.
Edge data centers and edge computing became crucial with the advent of COVID-19 and its work from home and social distancing norms. Edge computing, supported by AI, improves network performance for end-users, whether they are streaming entertainment services or Zooming through meetings.
Smart spaces are physical or digital environments where open, connected, coordinated, and intelligent ecosystems are used by humans and technology-enabled systems to interact. Individual technologies come out of silos to combine efforts to create a collaborative environment, which leads to the development of new smart spaces. Smart cities are a great example of smart spaces. In such cities, intelligent urban ecosystem frameworks, linking to social and community collaboration, are used to design spaces that combine business, residential, and industrial communities. In the face of the global pandemic, smart spaces may be in high demand for adoption by businesses, considering how closely our everyday lives are intertwined with technology. Residential and commercial real estate will leverage this technology to redesign our living and work spaces to leverage AI to control the spread of COVID-19, informing us who can come into the office and when, and how often and when to do testing, contact tracing, regulating the healthy use of elevators, and more.
Data scientists have never before had such an embarrassment of riches. And while it sounds promising, it would be nearly impossible to explore all the possibilities presented by the data. Unable to parse the mountains of information, businesses miss opportunities and fail to monetize in the myriad of ways that the data suggests is low-hanging fruit. Data science and machine learning platforms dramatically changed the ways businesses generate analytics insights in recent years. Augmented analytics, using machine learning — a subset of AI — helps them find crucial hidden patterns, sometimes without the help of professional data scientists. Because of the high cost and scarcity of professional data scientists, the number of citizen data scientists may grow exponentially in the near term, to help businesses scale. Augmented analytics and data scientists will make data insights broadly available at all levels of an organization.
The global pandemic is not likely to end soon. Most businesses will need to rethink their strategies and find new ways to maintain necessary cash flows. But how much cash flow will be required? This is a crucial piece of information. With augmented analytics and compiled data, companies can create cash flow simulations, plan business operations, and conduct liquidity analyses to understand what it will take.
Businesses could also streamline internal operations and track productivity by measuring individual and collective time spent on meetings and selected tasks, for example. Comparing findings to pre-pandemic performance may provide insights into areas that are doing well and areas where improvement is needed.
Blockchain is a list of timestamped records (blocks), linked by cryptography, and stored in a distributed ledger. Because the ledger records transactions across many computers and because each record contains something of a breadcrumb trail to the prior block, recorded transactions cannot be altered without altering all subsequent blocks. With blockchain, businesses can track transactions and work with unknown or as yet untrusted parties without going through a third-party intermediary. Blockchain helps ease friction inside business processes and its popularity has spread to other spheres, such as government, healthcare, manufacturing, supply chains, and many more.
COVID-19 disruption shows us that blockchain can be used in a multiplicity of new ways. For example, the U.S. Department of Homeland Security’s guidelines defined blockchain managers in food and agricultural distribution as “critical infrastructure workers.” In the healthcare arena, blockchains could be used to facilitate interactions between payers and providers or to allow patients to create rules regarding access to their records.
Criticism of blockchain is commonplace. Blockchain is blamed for sacrificing efficiency for security — current public blockchains can consume exorbitant amounts of energy — but this could be resolved using AI to improve algorithms. The amount of storage needed for blockchain transactions may also decrease through the use of AI. Energy consumption in other sectors has already moderated via AI application, and all industries that rely on blockchain for data transfer and supply-chain management will benefit if blockchain energy consumption diminishes. Lessened energy consumption might also accelerate widespread adoption of the technology.
Market Trends in AI
The PwC/CBInsights MoneyTree report for Q1 2020 notes that VC firms invested more than $4.0 billion of fresh capital into 148 AI deals. Among these, five companies were identified as receiving at least $200 million in so-called “mega-rounds”: pony.ai, Netskope, Berkshire Grey, SambaNova Systems and SentinelOne. These companies integrate many of the applications mentioned previously in this article. SambaNova Systems focuses on building machine learning and big data analytics platforms. Pony.ai is developing a fully autonomous car. Netskope provides a computer security platform. Berkshire Grey combines AI and robotics to automate omnichannel fulfillment, and SentinelOne markets an autonomous cybersecurity platform.
Applications for AI
Healthcare employs AI to let users analyze health data to find any possible anomalies, diagnose disorders and prescribe solutions. The automotive industry uses AI to make autonomous driving possible. AI is widely used by fund managers to deploy assets and harvest dividends and returns. E-tailers use AI to predict and offer products that consumers will be interested in buying. Cyber-security uses AI to aid in identifying and eliminating threats. Lawyers use AI to quickly crunch terabytes of data, find discoverable evidence, and identifying potential liabilities while maintaining due diligence. In the gaming industry, AI predicts player behavior, identifies anti-social conduct, and increases the sale of virtual goods. The military uses AI to identify threats and improve security. AI is seemingly omnipresent.
Innovating Inside and Out
If you are searching for examples of companies that failed to implement new technology to innovate and evolve their business models, look no further. Bankruptcy courts’ archives are filled with cautionary tales. If you still doubt, compare the value of a New York taxi medallion in 2010 (before Uber and Lyft entered the market) to its value now. Or, consider the number of smartphones that used the once-widespread Symbian operating system, and contrast that number to smartphones using it today (hint, close or equal to zero).
To be successful companies, need to expedite their own digital transformation plans and enhance internal innovation and venturing initiatives. Among the most successful tech companies, dual innovation platforms are normal. To make this process organic, companies establish internal skunk labs and R&D programs. Famously, in 2004, Google Founders Sergey Brin and Larry Page encouraged employees to spend “20% of their time working on what they think will most benefit Google,” in addition to their regular projects.
Innovation can be imported, as well as homegrown, by implementing licensing programs, accelerator and incubator initiatives, commercial agreements, joint ventures, and strategic investments. During the last ten years, many corporations formed their own venture groups to look for new technologies, invest minority seed-stage capital, and advance go-to-market strategies by connecting the recipient with the investing corporation’s internal business units. Such groups look for new technologies, invest minority seed-stage capital, and advance the recipient’s go-to-market strategy by connecting it with the investors own business units. Corporate development arms look for adjacent or complementary technologies or teams of engineers to acquire in strategic acquisitions and “acqui-hires.”
Our economy may benefit from the infusion of new products, partnerships, distribution channels, revenue streams, and higher-paying, value-added jobs from these combined internal and external efforts to innovate.
We can achieve “singularity” if potential AI applications — automating processes, developing operations, improving security, promoting commerce and protecting against fraud — accelerate.
AI technologies and applications aimed at revamping business models may be the key to recovery from this global crisis, for struggling companies and economies. Marc Andreesen recently wrote, “it’s time to build.” To his stirring manifesto, we would add, “it wouldn’t hurt to do it with AI.”
Louis Lehot is a partner and business lawyer with Foley & Lardner LLP, based in the firm’s Silicon Valley, San Francisco and Los Angeles offices, where he is a member of the Private Equity & Venture Capital, M&A and Transactions Practices and the Technology, Health Care, and Energy Industry Teams. Louis focuses his practice on advising entrepreneurs and their management teams, investors and financial advisors at all stages of growth, from garage to global. Louis especially enjoys being able to help his clients achieve hyper-growth, go public and to successfully obtain optimal liquidity events. Louis was the founder of a Silicon Valley boutique law firm called L2 Counsel. He previously served as both the co-managing partner and co-chair of the emerging growth and venture capital practice of a global law firm in Silicon Valley.
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