Google, Apple, Facebook, and Intel Battle for AI Supremacy
I am sure by now, you have heard the phrase that has been thrown around quite a lot by mostly, venture capitalists: “Artificial Intelligence (AI) is the new mobile.” The reason why this phrase has been echoed in the tech industry is to emphasize that AI is not a short-lived fad, rather a revolution like mobile. More importantly, they seem to be right as in the last five years, giant tech companies have been pouring money into this technology. In fact, over 200 private companies using AI algorithms across different verticals have been acquired since 2012, with over 30 acquisitions taking place in Q1’17 alone.
The acquisitions of AI startups are getting feisty, too. The price tags are often not a priority of these deals. Don Harrison, Head of Corporate Development at Google, for instance, stated: “We’re definitely AI-first. We pay attention to [valuation] but don’t necessarily worry about it.” Mostly, because acquirers see these startups as a talent and technology source rather than a business.
Brandon Purcell, senior analyst at Forrester says: “AI will precipitate a new data gold rush, marked by data-motivated acquisitions. Companies that acquire data assets around a specific use-case will win. The resulting barrier to entry will be insurmountable.”
Now that we know the major tech titans are getting serious about AI to shape the future, let’s analyze the most active acquirers in the space to obtain a deep grasp that goes beyond the hype. Here is a splendid timeline chart created by CB Insights providing a glimpse into the mergers and acquisitions of AI startups by the top acquirers:
As seen in the chart above, Google has acquired 11 private AI startups up to date. The acquisition of DeepMind, with a price tag estimated at $660 million marked one of the largest acquisitions among the M&A deals in the AI area. DeepMind’s specific focus on improvements in general AI research has helped Google apply the research to its business process to beef up its AI capabilities. Another main drive behind these acquisitions was developing Google’s Home to have a leg up on the competition against the virtual personal assistant market leader, Amazon's Alexa. To that point, the tech behemoth acquired three companies specialized in image recognition; DNNResearch which is focused on deep learning, image search, and facial recognition; Moodstocks which is specialized in image recognition for smartphones; Vision Factory which is used for improving accuracy and speed in object and text recognition. Many other acquired technologies have also been used for leveraging Google’s search functions.
Since the introduction of Siri about five years ago, we have consistently seen Apple in the news making a significant number of acquisition and product enhancements to leverage its AI capabilities.
To make Siri better understand human speech and give it a more competitive edge, Apple acquired VocalIQ, and it bought Perceptio to protect consumer data by keeping data local to each device instead of uploading user data to the cloud. Last year, it snapped up Emotient as its technology can read facial expressions to determine a person's mood. To process the data from iPhones in real time, Apple also bought Tuplejump focused on applying deep learning to large data sets. Of all its recent acquisitions, Turi is arguably the most interesting one as with that deal, Apple allowed developers to build apps through deep learning that could scale for many users. The idea is similar to Google's TensorFlow, the open-source machine-learning library.
We may see more acquisitions, especially related to facial recognition technology, in the upcoming months as the company has been significantly utilizing the technology in the latest releases of iOS.
To allow users to add various filters in pictures or real-time videos and sharing them on Facebook, the company snatched up Belarus-based Masquerade Technologies specialized in facial recognition technology. Facebook also acquired Switzerland-based Zurich Eye, a computer vision startup, recently. Prior to these two acquisitions, Facebook also bought Wit.ai focused on creating an API for building voice-activated interfaces. The Wit.ai product lets developers add a few lines of its code to instantly build in speech recognition and voice control so, with the deal, Facebook aimed at leveraging voice-to-text input for Messenger, improving Facebook’s understanding of the semantic meaning of the voice, and creating a Facebook app users can navigate through speech.
“Wit.ai has built an incredible yet simple natural language processing API that has helped developers turn speech and text into actionable data,” Facebook told TechCrunch. “We’re excited to have them onboard.”
Intel CEO Brian Krzanich said: “Intel is uniquely capable of enabling and accelerating the promise of AI. Intel is committed to AI and is making major investments in technology and developer resources to advance AI for business and society.”
Intel’s first AI acquisition was Saffron specialized in cognitive computing, combining data analytics with deep learning. Then the company bought Indisys as it is focused on natural language recognition, gesture recognition, and virtual assistant technologies. Additionally, Intel acquired 3 startups in 2016 alone: Itseez specialized in software for the Internet of Things (IoT), cameras, drones, and autonomous driving; Movidius focused on computer vision hardware for drones and cameras, and Nervana Systems marked the most significant of Intel's acquisitions and was acquired for reducing machine learning to a level that could reside on a processor.
Stephan Gillich, Europe, Middle East and Africa director for high-performance computing and AI at Intel, adds: “The more people involved in the AI conversation, the more industries will be able to benefit, and the sooner we will see the impact across government, business, and society.”
Twitter, Salesforce, and Microsoft
Although these four companies have been the most active acquirers in the space, other tech giants have recently joined the party, too. Twitter made 4 major acquisitions, the most recent being image-processing startup Magic Pony.
Last year, Salesforce launched its AI platform, Salesforce Einstein and claimed it is “the world’s first comprehensive artificial intelligence platform for CRM.” To develop Einstein, Salesforce splashed out about $650 million, acquiring dozens of artificial intelligence and machine-learning centric startups over the past two years. The CRM giant also quietly assembled a team of 175 data scientists, mostly through these acquisitions. "Think of Einstein as the intelligence layer between the data and the actual apps," said John Ball, Senior Vice President and General Manager of Content for Salesforce. "The best AI is when the user doesn't necessarily notice it. We surface lead and opportunity insights, and we've baked AI throughout the platform and the user experience so that, over time, the user won't even think of it as an AI-powered feature; it's just part of the platform."
In February, Microsoft confirmed that it has acquired SwiftKey, which makes predictive keyboard software that learns users’ typing habits and adapts accordingly, for $250 million. This acquisition was followed by the acquisition of a messaging app developer, Wand Labs in June. The deal was part of Microsoft’s strategy for Conversation as a Platform, which Satya Nadella introduced at the Build 2016 conference in March. Lastly, in August, the company also signed an agreement to acquire Genee, an artificial-intelligence-powered scheduling service in an effort to integrate its smart assistant Cortana with Office 365 apps.
The conference that was held at Dartmouth in 1956, is still considered the biggest groundbreaking event in the artificial intelligence field. Although it was believed that machines would be capable of using natural language, solving problems, simulating every aspect of learning and any other feature of intelligence within two decades, by the 1970’s, the period known as the AI winter had taken hold due to reduced funding and a lack of interest in artificial intelligence research. The enthusiasm around AI has cautiously grown since its low point in 1990, and in 2011, IBM’s Watson system beat the best human players in the game show Jeopardy! From there, it started to pick up speed. Last year, research firm IDC valued the market at $8 billion, forecasting a rise to $47 billion in 2020.
I don’t know about you folks but whenever I read or talk about Artificial Intelligence, still my mind turns to glorified battles to save the human race from villain robots, and as many of you, I often think of the Terminator series where Skynet, a neural network-based conscious group mind and artificial general intelligence system, obtains self-awareness after spreading to millions of computers and attempts a nuclear genocide. That being said, when it comes to AI and machine learning, Terminator is neither the reality nor the only thing comes to my mind because more importantly, my concern is ownership of data. It is not a coincidence that all the aforementioned companies such as Google, Apple, Intel, Microsoft, Facebook, IBM, and Twitter are highly focused on muscling up in the battle for AI supremacy. The main reason is their one simple common aspect: the data-intensive nature of their core businesses. To accomplish their main objective, vendors mostly need two things: first, highly trained, seasoned, and specialized data scientists, secondly, specific use-cases that provide real value to end-users so the adoption rate can be increased. Therefore, the key to the M&A activities in the AI area is a seamless integration between acquirers and acquirees in terms of both technology and teams.