Artificial Intelligence was not born in recent circumstances. It has existed and gradually developed for a long time, because of the works of scientists like Alan Turing, John McCarthy and Marvin Minsky. While scientists have worked in this field for a considerable length of time, progresses in AI have more recently been propelled by advancements in machine learning and deep learning.
Everything from journalism to hiring, is already being replaced by AI that is increasingly ready to replicate the experience and capacity of humans. What was once observed as the future of technology, is already here, and the only question here is ‘How will it be implemented in the mass market.
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AI spending is forecasted to grow from $640 M in 2016 to $37 B by 2025. Sectors like image recognition, algorithmic securities trading and healthcare patient data administration, have colossal scale potential, and as well as in areas like business services, consumer products, industry (industrial robotics), advertising, finance, media and defense.
Particularly, AI is a super sub-sector of technology, which is set for a development surge which will traverse not just for years but decades.
The Vital Stats of Sharks
Tech giants like Google, IBM, Yahoo, Intel, Apple and Salesforce are contending in the race to acquire AI companies, with Ford, Samsung, GE, and Uber developing as new participants. More than 200 private companies using AI algorithms crosswise over various verticals have been acquired since 2012, with more than 30 acquisitions happening in Q1’17 alone.
Look at Apple, it has increased its M&A activities, and ranked second with an aggregate of 7 acquisitions. As of late it acquired Tel Aviv-based RealFace, valued at $2M.
Investing in Artificial Intelligence
Investments in AI requires a different sort of analysis than with traditional enterprise software. AI technology is new to most managers and getting the desired results relies upon having adequately large and relevant data sets to feed the AI machine.
Since AI can create results that go a long way past process change and productivity, characterizing investment outcomes and goals can be much more bewildering than with traditional process automation software.
The greatest risk is that we as managers will settle on bad decisions about where to invest, and we’ll wind up squandering billions of dollars on imbecilic projects that no one cares about. I believe that, in some ways, interesting, yet obvious question is in front of us for the next 5-10 years. This is still a poorly understood and badly researched part of the question.
“What is the right microeconomic framework for thinking how to invest in machine learning or AI?”
I think as a rule, most economists and most business schools are still more centered around the large-scale monetary ramifications. Be that as it may, those larger scale financial ramifications don’t make a difference unless we use sound judgment at a micro level.
Investing in AI is not a simple work: AI advancements are new algorithms and unless you can dig into lines of code they might be inscrutable. Essentially taking a gander at verification of ideas won’t be sufficient to really comprehend the underlying stack behind applications, and this represents a major barrier for investors to effectively allocate their capitals.
Being an investor and working with AI-driven businesses, has helped me identify areas that make a startup investable.
Here’s My Advice To Investors Looking To Invest In AI
• If a problem was not addressable before, it is likely that a machine learning algorithm is behind the proposed solution of that problem
• Data effect is the regular information that neural nets require to be trained, and if the startup has an approach to create a virtuous data cycle or approaches proprietary data, most of the time it is enough to be esteemed as investable
• The greatest barrier to the passage AI/ML is talents and Intellectual Property. Therefore, if a startup is made of scientists and has patents (obtained or pending), it would qualify to be a decent candidate for an investment even without revenues. This trend is driven by tech organizations investing in new businesses for their ‘brain power’ as opposed to their actual numbers.
Better Understanding Is The Need of the Hour
Better believe it, we’re equally in this migration from a data world to a model world. The organizations that do that best, or figure that out sooner, will be the ones that will be ‘Agile’, ‘Dynamic’ or whatever good words you can think of.
The ones that are model centric, and are savvy about being model-driven will be the ones that will be successful.
The advantages and risks of investments are shapeless, uncertain and an issue for theory. The one known risk common to all things new in business is uncertainty itself. Along these lines, the risks mainly come through making a bad investment, which is nothing new to the universe of finance.
So as with all things eccentric and new, the prevailing knowledge is that the risk of being left out is far greater, and far grimmer, than the benefits of playing safe.