AI and the Ballad of Automation
What tech reporters get wrong about AI, and why Systems of Intelligence are about to eat the world
Perhaps the fastest-growing genre of tech news today goes like this: ‘AI performance disappoints, experts predict chaos.’
No doubt this makes pretty good journalistic fodder but articles like this don’t do much for understanding the current state or likely future value of AI technology. The foundational mistake they all make is to conflate automation and augmentation when it comes to AI.
The core value of AI lies in its ability to augment human capabilities. And this reality is about to power an entirely new wave of value from technological development.
To help understand why, we’ve drawn from our experiences in building enterprise AI technology at ViewX to outline why automation is so hard, why augmentation is so powerful, and why ‘System of Intelligence’ is the next tech phrase you’re about to hear lots more.
1. Automation is hard, and is not getting much easier
The single most persistent reality to appreciate about AI is that human capabilities remain extraordinarily difficult to replicate.
Consider robotics as an example: A study released this year by the influential journal Science Robotics noted that in the pursuit of robotics technology, “the major challenges… have remained largely unchanged for 30 years”.
Even NASA’s $2M Valkyrie humanoid robot — built to accomplish tasks in extreme space environments — still struggles with the kind of hand dexterity that you’d expect from most human toddlers.
If these challenges exist with respect to hand movement, consider the difficulty of replicating the performance of the human brain.
The human brain is a miraculous mess of billions of neurons that come together to operate at an estimated 1 exaFLOP, or one billion billion calculations per second. The world’s fastest supercomputer, by comparison, has a processing power of about 1/50th of that.
Indeed, in 2014, a group of researchers attempted to apply their supercomputer to process one second of one percent of the type of work the human brain is able to do. It took it 40 minutes to complete.
The reason that the human brain is so difficult to replicate is that it is enormously flexible, powerful and efficient. Its decision-making capabilities are based not just on intelligence, but also on our “massively parallel processing wetware” which consists of things like human instincts, common sense, and experiences. Computers can be programmed with vast libraries of information, but — without getting too poetic about it — will simply never be able to live life as humans do.
2. Augmentation is far easier, and it’s getting better every day
Whilst the brain is miraculous, it is also has major limitations. And so, while replicating human capabilities is difficult, augmenting them is not.
What’s more, computers possess enormous capability advantages over humans in multiple broad areas: they have vastly superior memories, they can find and recall information much faster, they can be taught quicker, they are more reliably accurate in processing complex information, and they are overall less influenced by external factors.
The vast improvements in computing power over the past few decades have transformed our understanding of computational capabilities. Fortunately, these advances have come at the perfect time.
3. As information gets more valuable, computing capabilities become more critical
As you’ve probably noticed, we live in an information economy, one in which success is increasingly dependent on the quantity, quality and accessibility of information available to us.
Indeed, information is now so critical to economic success that that The Economist recently declared data — replacing oil — as the world’s most valuable resource.
Yet data and oil differ in many respects. The most important of which is scarcity. Today’s data is not scarce. The combination of the internet and the digitization of the economy combines to produce an astonishing 2.5 quintillion bytes of digital information every single day. Moreover, this information is expanding at such a rate that every two years we roughly double the amount of data that has ever existed in all of history before it.
What this means is that we’ve reached an information tipping point, where quantity of data becomes less of a differentiator, and can become more of a disadvantage. Unless it’s properly harnessed.
4. AI is really good at synthesizing information
This is where the unique value of AI starts to come into focus. And especially deep learning.
Deep learning thrives on large datasets. This is not only because large datasets provide sufficient training data to see algorithm improvement start to occur, but also because large datasets provide sufficient complexity for the capabilities of deep learning algorithms to really shine through.
Traditional algorithm performance tends to plateau at certain database sizes, limiting their impact. On the other hand, deep learning performance continues to improve as data inputs increase. What this means is that with sufficient data, deep learning algorithms continue to generate increasingly sophisticated insights and patterns, leaving conventional alternatives further and further behind.
This gives deep learning enormous power. And at ViewX we’ve seen the extraordinary benefits of this capability first-hand. Using deep learning to power our proprietary contextual recommendation technology, we’re able to process vast amounts of unstructured data to identify the most contextually relevant connections between various sets of information.
Moreover, the more data we feed it, the more performance has continued to improve, to the extent that having now processed over 100M human interactions, we’re now seeing our AI-powered recommendations outperform human selections by up to 155%. The usefulness of this is that we’re able to powerfully process huge amounts of information in a way that delivers our human users the specific and highly-relevant information they need to improve their decision-making and increase their productivity.
5. The rise of systems of intelligence
This change in our relationship to data marks the beginning of a new phase within the information age.
Until now, the winners have been those that have generated vast amounts of valuable information, yet this will no longer be enough.
The winners of this next era will be defined by their ability to not just generate data, but make use of it, particularly in a way that augments the unique decision-making capabilities of their users.
This reality is succinctly captured by the concept of a System of Intelligence, described by Jerry Chen at Greylock Partners as “the fountain of the next generation of great software companies”, and which refers to any application that is able to apply AI to connect vast data sets, business processes, and user workflows, to generate valuable predictive insights for their users, whether they be customers or employees.
This next wave of technology will be defined by AI that augments human capabilities, rather than tries to automate them.
We’re excited to be at the forefront of this change, applying deep neural networks to build the first true system of intelligence for the fastest-growing facet of human capital management: workforce development.
The reality facing every workforce today is that one in two workers complain that they don’t have the information they need to succeed. When information is critical to success, it’s no surprise that low-information workers are less productive, less engaged, and more likely to be looking for other work. This situation currently describes 50% of your workforce.
AI provides a transformative solution, and we’ve harnessed it to build a product that understands what every worker is working on in real-time and predictively delivers them the information they need to do their job.
By using AI to personalize the flow of information to every employee, we’re augmenting their innate human ability to add context and make effective decisions. In doing so, we’re building an AI-enabled workforce that is more informed, more engaged, and more productive than ever before.
What this means for the AI future
What we’ve learned from building enterprise AI technology is that the value of AI lies in its ability to augment human capabilities rather than automate them.
And though we don’t expect to see the end of the media’s fascination with ‘AI performance’ stories any time soon, we do expect to see a huge rise in companies building the ‘systems of intelligence’ that use AI to transform human capabilities and power the next wave of the technological revolution. We’re excited to be riding the wave.