It’s hard to read about a new advancement in technology these days without the mentioning of artificial intelligence (AI). That’s because AI, without a doubt, has been one of the defining technologies of the decade. Moving forward, it’s impossible to predict where AI will take humanity, as researchers continue to bring to reality many futuristic concepts that once only existed in our imaginations or sci-fi movies.
Since the start of 2010, AI has beaten professional gamers, sparked a new age of digital innovation, and is now powering the self-driving car movement. The IBM Watson and DeepMind have already shown that machines can, in at least in some ways, outsmart humans.
Many of us remembered, way back in 2012, Google showed how a computer can be used to identify what a cat looks like after learning from thousands of cat videos, indicating the eerie yet untapped potential of deep learning in an advanced computer. Deep learning, a subset of machine learning, uses neural networks and adjusts them until it can return the right results.
Alongside the meteoritic rise of Big Data, deep learning became invaluable in the 2010s. With massive pool of raw data, AI systems were able to learn more (and faster) than ever before without the need for human programming.
In 2017, Technology Review’s Karen Hao released an exhaustive analysis of recent papers in AI that concluded that machine learning was one of the defining features of AI research this decade. “Machine learning has enabled near-human and even superhuman abilities in transcribing speech from voice, recognizing emotions from audio or video recordings, as well as forging handwriting or video,” it said.
As the decade progressed, most tech majors clearly understood that to be in the good books of investors, AI should clearly be the agenda. Amazon, Google, Microsoft, Apple and Facebook are have been investing heavily into AI. For example, when Facebook began in 2004, it focused on connecting people. These days, it’s fixated on doing so with artificial intelligence. It’s become so core to the company’s products that a year ago, Facebook’s chief AI scientist, Yann LeCun, told CNN Business in an interview that without deep learning the social network would be “dust.”
The decade also saw the mushrooming of AI startups. From Silicon Valley to India, AI startups are in abundance and many of them are striking gold. In recent years, AI has been a part of the daily lives in a form of facial recognition, smartphone passwords, virtual assistants, voice recognition and internet search, to name a few, bringing it closer to the enterprise from the research labs.
Needless to say then, more and more enterprises are succeeding with AI, proving that an inflection point for AI use is already here. AI is touching upon every industry, such as healthcare, finance, automotive, manufacturing and retail and even media and art.
A study by Information Age reflects that by the end of this year, 30% of large enterprises will start generating Data as a Service revenue because of AI and over 40% of digital transformation initiatives will use AI services. AI technologies that may boost business success in the coming years include advanced AI assistants, virtual agents and conversational chatbots and AI-powered search etc. It is also anticipated that 75% of commercial enterprise apps will have AI built in by 2021, and over 50% of consumers will interact with it.
Again, as per the latest report by PwC, AI could add $15.7 Trillion as combined output of India and China to global economy by 2030. This technology has various applications from countering terrorism to space exploration. Yet it has many hurdles, which needs to be overcome.
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Despite the momentum, a CNN article suggests that AI needs a lot of work on making machine learning systems better at generalizing and learning from newer examples, something that thoroughly depends on data work. It is high time companies pay attention to utilizing dark data to derive insights. Often times, business leaders are confused as where they should start? And how AI could benefit your company?
Dr. Panos Constantinides, associate professor at Warwick Business School, writes in his blog that in most part of the 2010s, AI mostly remained a loosely defined term with lack of clarity, leaving room for unscrupulous vendors to rebrand legacy software by throwing AI into the sales pitch. To sidestep that confusion, it’s better to be more specific: what most tech vendors mean today when talking about AI is machine learning (ML).
Companies with mix of domain expertise and in-house data science skills can help decide what you want to achieve and how machine learning is a good fit for an organization – bringing up the question of massive talent shortage in the AI space that haunted enterprises through 2010s and is likely to continue in the next decade.
Ranjit Nair, CEO and Founder of Germin8 Solutions, a digital intelligence company, believes, “Without mass market use-cases there will not been a great deal of money in it. As a result there are only handful organizations that were willing to cough up money for development of talent and skill. AI is also not well-represented in industry-focused education and training curriculum. As a result there is a skills shortage – simply not enough qualified people who know how to operate machines which think and learn for themselves. Also, trust building in AI is a big issue. People don’t feel comfortable when they don’t know and understand how the decision was made by a system.”
Finally, malicious use of AI could play out in the form of terrorists deploying autonomous weapons or hacking large-scale operations controlled by AI that could affect businesses and societies, causing chaos and destruction.
From the learnings of the decade, a McKinsey article states that AI is poised to “unleash the next wave of digital disruption”, and advises companies to prepare for it now. From adding efficiencies in operations and logistics, to improving and expediting customer service experiences and introducing smarter means of transportation, AI’s potential is rich.
Experts believe, early adopters of AI are already solving mission-critical problems and seeing immediate value-add to revenues, profit and overall industry leadership. But those who will continue to be hesitant will surely miss the boat.