The race for AI dominance is a tough game to lose. For the five biggest giants of the Tech industry at least — Microsoft, Google, Facebook, Amazon, and Apple — it is a race they can’t afford to lose. Each year, they pour billions of dollars into their AI research divisions, each trying to advance the possibility of Artificial General Intelligence (AGI) with the intention to gain the first-movers advantage in shaping the business era of the future.
AI has already, to some extent, permeated the modern business landscape. Narrow AI has given the modern consumer unique innovations like Siri and Alexa. Although this version of AI does not really compare with actual human intelligence, what it can do is take a specific skill like facial recognition or voice identification and perform these tasks at a superhuman level. Narrow AI is also more prominently applied across any useful value chain for reducing human effort. By 2021, Gartner predicts that it will save over 6.2 billion hours of worker productivity globally, and create up to $2.9 trillion of business value.
The investments into advanced AI requires some seriously deep pockets to fund these projects. Google’s DeepMind, for instance, burned through $500M in 2018 alone. Most companies would shudder at the thought of burning through that kind of money, but the reality is that for companies who are serious about winning the AI race — half a billion dollars is a small price to pay.
A lot of companies are also investing heavily in conversational platform capabilities. They believe that as more and more people transit into the mobile-first era, the way people communicate will also change. Microsoft is also leading this charge, through their innovative ‘conversation as a platform’ offering, which anticipates a momentous shift from the app-focused world we are so used to living in now.
For the Big Five, the race is to create the most advanced brain. A brain that can either match or surpass human intelligence. (Not so subtly, Google’s AI research lab that acquired DeepMind is called Google Brain). This ‘brain’ will live in the cloud and that is where all of this phenomenal effort points to. A connected sphere of trillions of data points being captured and stored on the cloud, where AI systems will comb through the data in near real-time to perform extremely complex tasks in a few seconds. A cautionary note: Even the most sanguine experts opine that AI is still decades away from achieving this level of intelligence.
The AI battlefield is filled with cases of organizations & business leaders who were not bold enough or didn’t possess the adequate vision for competing in the AI space. When Satya Nadella, Microsoft’s current CEO, took over from Steve Ballmer – he corrected Ballmer’s 14 year-long misguided turns that ultimately led to Microsoft failing in the mobile devices space, on his very first day. He made Microsoft reposition itself as cloud and mobile-first. Nadella steered the company back from a $500M company in 2014, to slightly over a trillion-dollar valuation in 2020. He spurred a new vision for Microsoft, and it’s future bets — starting with Artificial Intelligence.
However, to be a leader in the AI space, you need the best talent that’s out there working for you and not for the competition. And so, a new race was on — who could acquire and afford the best minds in AI?
The Godfathers of AI
Each year, the Association for Computing Machinery (ACM) presents the Turing award to individual(s) who have made significant contributions to the field of computing. The award is widely considered the Nobel prize of Computing. In 2018, three researchers — Geoffrey Hinton, Yann LeCun and Yoshua Bengio were recognized for their contributions to neural nets and deep learning (a subfield within AI).
The trio fostered several breakthroughs in the early 90s in areas like computer vision and speech recognition. It is the result of this work that has eventually found its way into almost every smartphone such as facial unlocking features in most devices, and futuristic AI technologies that are still in early development like self-driving cars. Widely respected and deemed within the computing community, the three are often regarded as the ‘Godfathers of AI’.
Geoffrey Hinton, a professor at the University of Toronto, is often recognized as the ‘Godfather of Deep Learning’. In 2012, he and some fellow students entered the ImageNet challenge to prove neural networks were able to achieve more accurate results for object recognition tasks.
They beat the second-best algorithm at the competition by more than 40%. Once the story broke and the World took notice of their work, the techniques pioneered by Hinton, LeCun and Bengio have become the foundational basis of convolutional neural networks and AI in general.
Google took notice and quickly realised that these algorithms were far superior to what they previously had, and realised they could use it for making their ‘photo searching’ more precise and faster. Hinton’s research, that he himself spun off into a company called DNN Research, would be acquired by Google in 2013, and with that, the ability to pursue true computer vision for their image search features, and get machines to look at the world as humans do.
While LeCun, would eventually join Facebook as it’s Chief AI Scientist, Yoshua Bengio decided to stay neutral and not get involved with corporations. Satya Nadella, however, pursued Bengio for a few years, before Bengio’s scepticism, although not unprecedented, waned. If companies were truly pursuing AGI, then such technology should not be in the hands of one or two companies alone; that could be disastrous.
Microsoft makes bold plans for AI
With Bengio at the helm of Microsoft’s AI research, Nadella infused AI within all three core offerings of the company: Office, Windows and Azure. Today, Microsoft’s approach has allowed their products to remain competitive alongside Google ’s search engine, productivity suite tools and personal assistant capabilities. Microsoft leads the AI patent race with around 18,300 patents.
Microsoft recently demonstrated how Cortana can sit in on meetings, transcribe the entire conversation, add reminders, tasks, and update the meeting attendees with a copy of the minutes – irrespective of language.
The cloud-based AI era of computing is fast upon us. However, the problem with having AI in the cloud is that latency (the time it takes to receive information over the internet) becomes a real problem. With 5G, things can go much faster, but the key lies in its tandem with supercomputing processors.
Enter Nvidia Volta, which some say is the key to unlocking real AI computing speeds. In fact, the fastest supercomputers on the planet also run on these graphic processors.
At a crucial turning point in the race, Elon Musk decided to back one of Hinton’s former students who was part of the three-member team at ImageNet, Ilya Sutskever. Sutskever, even at the time was somewhat of a prodigy in the field, and shared Musk’s concerns of AGI if it fell into the wrong hands. Together, they started Open AI, with a vision to build safe AGI. In 2015, Microsoft invested in the company with deal specifics implying Microsoft could commercialize any Artificial General Intelligence technology that Open AI creates.
Apple is betting on AI at the edge
Three years ago, Apple was falling behind Amazon, Facebook and Google in terms of AI investments and the failure of their flagship smartphone, the iPhone X, to reveal any new significant technological improvements. To catch up they have spent upwards of $200M to stay in the race. However, Apple, unlike its competitors, is not concerned with moving workloads onto the cloud. While everyone else is betting on the cloud, Apple is betting on hardware. Their AI strategy is to focus on devices and have Machine Learning workloads run locally on these devices. Apple believes this way they won’t compromise on user privacy. To facilitate this, they have launched an exclusive platform called ML Create (to train ML Models) and Core ML (to build AI models into Apple apps).
They have reinforced this strategy with several AI startup acquisitions like Turi and Spectral Edge, including their recent acquisition of Xnor.ai — a startup that builds low power ML hardware and software. Apple has acquired over 20 companies since 2010, to fulfil this vision.
However, with other open-source platforms like Google’s TensorFlow, Apple has failed to attract the attention of the AI developer community to latch onto its new ecosystem. TensorFlow came out three years before Apple launched ML Create, and in that time developers have already built a strong community committed to building products on other ecosystems that are not as notoriously closed off as Apple’s devices.
It is too early to tell, if Apple will be able to recover back some of that lost ground, or if the competitive advantage being forged by any of the Big Five at this stage will work out in the near future. The bets are placed, and the race is still on.