As researchers aim to better predict, diagnose and treat depression, artificial intelligence is being explored as a potential solution.
Some of the questions that need answering to better understand the role of artificial intelligence in efforts to diagnose and treat depression:
What types of AI applications are currently in use to manage depression?
How has the market responded to these AI applications?
Are there any common trends among these innovation efforts – and how could these trends possibly contribute to reducing the rates of people living with depression?
Depression AI Applications Overview
The majority of AI use-cases for managing depression appear to fall into three major categories:
Virtual Counseling: Companies are developing software using machine learning to recognize episodes of depression and to provide support using natural language processing.
Patient Monitoring: Machine learning is employed to monitor patients and to predict and prevent the onset of a mental health crisis.
Precision Therapy: Firms are using machine learning analytics to track and correlate cognitive function, clinical symptoms and brain activity.
Virtual Counseling Using Natural Language Processing
San Francisco-based startup Woebot said its chatbot uses machine learning and natural language processing to help users manage their mood and mitigate depression.
Accessed through the Facebook Messenger platform, Woebot prompts users with questions to assess their mood. Over time, the algorithm, which is trained on cognitive behavior therapy (CBT) methods, learns the emotional profile of each user and recommends activities to help maintain a more balanced mood.
The early-stage startup bases its clinical significance on the results of a clinical trialconducted in partnership with Stanford University. The study demonstrated that Woebot users aged 18-28 experienced “significant reductions in anxiety and depression” compared with the control group that used an e-book published by the National Institutes of Health (NIH).
Woebot was used every day or almost daily by 85 percent of participants for two weeks. Effectiveness was measured using a standard patient health questionnaire for depression called PHQ-9 with scores ranging from 0 (no symptoms) to more than 20 for severe depression.
The chatbot hit 50,000 users in its first week and receives roughly 1 million messages each week and has secured Series A funding. However, the amount of funding is not specified. Woebot, which was launched in June 2017, is currently available for free but a paid version may be in the works as the startup moves toward a sustainable business model.
While there has been a lot of discussion about “what’s left for humans?” as AI improves at exponential rates — the customary answer is that humans need to focus on the things they are uniquely good at, such as creativity, intuition, and personal empathy — I think we now have to ask, “what’s left for firms?”
In many ways this is an old question, because it takes us back to the arguments of Nobel Laureates Ronald Coase and Oliver Williamson that firms exist to coordinate complex forms of economic activity in an efficient way. If computer technology has the capacity to simplify and streamline transaction costs, more and more work can be done through these smart-contract arrangements, making traditional human-managed firms obsolete. For example, when you say to Alexa “order more dog food,” a chain of activities is initiated that leads to the delivery of a fresh supply of Kibble 24 hours later, with little or no human intervention. This work is coordinated by a single firm, Amazon, but it often involves third parties (makers of dog food, delivery companies) whose systems interact seamlessly with Amazon’s.
But is this coordination logic, this ability to internalize transactions to make them more efficient, really the raison d’etre of firms? I would argue that it is just one among many reasons that firms exist. And as computer technology simplifies and reduces transaction costs further, it is these other things that firms do uniquely well that will come more to the forefront. Here are four areas where firms excel.
1. Firms create value by managing tensions between competing priorities.
In today’s parlance, firms have to exploit their established sources of advantage (to make profits today) while also exploring for new sources of advantage (to ensure their long-term viability). However, getting the right balance between these two sets of activities is tricky because each one is to a large degree self-reinforcing. Hence the notion of organizational ambidexterity — the capacity to balance exploitation and exploration.
Artificial intelligence is evidently helping many firms to exploit their existing sources of advantage — whether through process automation, improved problem-solving or quality assurance. Artificial intelligence can also be useful in exploring new sources of advantage: in the famous case of AlphaGo, the winning “strategy” was one that no human player had ever come up with; and computers are increasingly writing new musical scores and painting Picasso-like landscapes.
But AI is not helpful in managing the tension between these activities, i.e. knowing when to do more of one or the other. Such choices require careful judgment — weighing up qualitative and quantitative factors, being sensitive to context, or bringing emotional or intuitive factors into play. These are the capabilities that lie at the heart of organizational ambidexterity and I don’t believe AI can help us with them at all right now. IBM’s recently-announced Project Debater is a case in point: it showed just how far AI has come in terms of constructing and articulating a point of view, but equally how much better humans are at balancing different points of view.
2. Firms create value by taking a long-term perspective.
As a variant of the first point, firms don’t just manage trade-offs between exploitation and exploration on a day to day basis, they also manage trade-offs over time. My former colleagues Sumantra Ghoshal and Peter Moran wrote a landmark paper arguing that, unlike markets, firms deliberately take resources away from their short-term best use, in order to give themselves the chance to create even more value over the long term. This “one step back, two steps forward” logic manifests itself in many ways — risky R&D projects, pursuing sustainability goals, paying above-market wages to improve loyalty, and so on. We actually take it for granted that firms will do many of these things, but again they involve judgments that AI is ill equipped to help us with. AI can devise seemingly-cunning strategies that look prescient (remember AlphaGo) but only when the rules of the game are pre-determined and stable.
An example: the “Innovator’s Dilemma” is that by the time it’s clear an invasive technology is going to disrupt an incumbent firm’s business model, it’s too late to respond effectively. The incumbent therefore needs to invest in the invasive technology before it is definitively needed. Successful firms, in other words, need to be prepared to commit to new technologies in periods of ambiguity, and to have a “willingness to be misunderstood,” in Jeff Bezos’s terms. This isn’t an easy concept for AI to get used to.
3. Firms create value through purpose — a moral or spiritual call to action.
There is a second dimension to long-term thinking, and that is its impact on individual and team motivation. We typically use the term purpose here, to describe what Ratan Tata calls a “moral or spiritual call to action” that leads people to put in discretionary effort — to work long hours, and to bring their passion and creativity to the workplace.
This notion that a firm has a social quality — a purpose or identity — that goes beyond its economic raison d’etre is well established in the literature, from March and Simon through to Kogut and Zander. But it still arouses suspicion among those who think of the firm as a nexus of contracts, and who believe that people are motivated largely through extrinsic rewards.
My view is that you just need to look at charities, open source software movements, and many other not-for-profit organizations to realize that many people actually work harder when money is not involved. And it is the capacity of a leader to articulate a sense of purpose, in a way that creates emotional resonance with followers, that is uniquely human.
Successful firms, in other words, institutionalize a sense of identity and purpose that attracts employees and customers. Ironically, even though blockchain technology is — by definition — about building a system that cannot be hacked, or misused by a few opportunists, people still prefer to put their faith in other people.
4. Firms create value by nurturing “unreasonable” behavior.
There are many famous cases of mavericks who succeeded by challenging the rules, such as Steve Jobs, Elon Musk, and Richard Branson. With apologies to George Bernard Shaw, I think of these people as unreasonable — they seek to adapt the world to their view, rather than learn to fit in. And if we want to see progress, to move beyond what is already known and proven, we need more of these types of people in our firms.
Unreasonableness is antithetical to the world of AI. Computers work either through sophisticated algorithms or by inference from prior data, and in both cases the capacity to make an entirely out-of-the-box leap doesn’t exist. Consider the case of investment management, where robo advisors are not just making trades, they are also providing investment advice to investors, and at a fraction of the cost of human financial advisors. But as the Financial Times said last year, “when it comes to investing, human stupidity beats AI.” In other words, if you want to beat the market, you need to be a contrarian — you need to make investments that go against the perceived wisdom at the time, and you need to accept the risk that your judgment or your timing might be wrong. Both qualities that — at the moment — are distinctively human.
So one of the distinctive qualities of firms is that they nurture this type of unreasonable behavior. Of course, many firms do their best to drive out variance, by using tight control systems and punishing failure. My argument is that as AI becomes more influential, though the automation of basic activities and simple contracts, it becomes even more important for firms to push in the other direction — to nurture unorthodox thinking, encourage experimentation, and tolerate failure.
In a recent Fast Company article, Vitalik Buterin described how all the elements of Uber’s ride-sharing service could be provided through Ethereum-based applications that worked seamlessly with one another: “the whole process is basically as before, but without the middleman [Uber].” This is may be true, but it doesn’t necessarily follow that a computer-mediated service is the better option.
In 1985, we thought Einstein’s brain wasn’t much different from anyone else’s. We were wrong.
We still don’t completely understand how the brain works and yet we’re building machines to replicate it. Our quest to create artificial intelligence has grown into a near-frenzy as we surge ahead with unprecedented progress. But will we really reach the finishing line?
Any hope of success will depend on our ability to answer one simple question: What exactly is intelligence?
In 1985, American scientist Marian Diamond studied the brain of Albert Einstein and found an answer.
Was Einstein’s brain different?
We’re used to talking about neurons when referring to the brain, but we also have what are called glial cells. In Greek, glia means “glue.” Glial cells were given their name because we thought they did little more than just hold the brain together. One kind of glial cell is the star-shaped astrocyte.
In 1985, Diamond’s findings were almost disappointing. Einstein’s brain did not contain more neurons overall than the average person’s. It did, however, contain more astrocytes, in the left inferior parietal area of the brain, a region associated with mathematical thinking.
Since intelligence was assigned to neurons and astrocytes were thought to be little more than “glue,” this finding did not make headline news and was largely ignored.
What did Einstein’s brain actually reveal?
If you insert human astrocytes into the brains of newborn mice, they grow up to be more intelligent. Their learning and memory are significantly sharper. It’s only in the past few years that we’ve come to understand the extraordinary reason why.
We have always assumed that a synapse, the point where two brain cells join to carry information, is made up of two brain cells. We were wrong. A synapse is made of two brain cells — and an astrocyte.
Astrocytes nurture synapses. Not only are they key in synaptic plasticity, but they are plastic themselves. They grow and change. One astrocyte can be in contact with two million synapses, coordinating their activity and plasticity across vast realms of the human brain — and contributing to our intelligence.
How do astrocytes figure in artificial intelligence?
Artificial intelligence researchers from the University of A Coruña in Spain recently improved neural network performance by using an algorithm that included artificial astrocytes. When a neuron’s activity reached a maximum, the astrocyte was activated. It increased the weight of the neuron’s connections with the neurons of the adjacent layer by 25 percent, simulating what might happen in real life.
How do you increase astrocytes?
If Einstein was a genius because of his astrocytes, can we increase our astrocyte numbers and become geniuses too?
As early as 1966, Diamond and her team demonstrated that putting young rats in a stimulating environment rich with challenge and new experiences increased glial cells.
We now know that this even happens in elderly mice. Putting aged mice in an “enriched environment” increases astrocyte numbers and complexity, which correlates with better cognitive performance.
If you’re wondering, the effect is also seen in humans.
A study published this year followed production workers at a factory in Germany for 17 years. The volume of brain regions associated with executive function and motivation was larger in those who had been exposed to recurrent novelty in their work. This was associated with better cognitive performance at middle age.
Plasticity takes energy and effort and our brains are lazy. They don’t want to try to “grow” without good reason. Challenge and novelty tempt the brain with a reason to try.
What this means for you.
During her career as a professor of integrative biology at the University of California, Berkeley, Diamond concluded that five factors were crucial for healthy astrocytes — and for the human brain to thrive at any age: a good diet, exercise, challenge, novelty — and love (she noticed the mice in her lab lived longer and did better when cuddled).
Focusing on these five things can increase stress resilience and keep you mentally sharp. If you’re leading a team, you may not be able to change everyone’s diet and exercise routines or show love, but you can make sure your team has ample opportunities for “newness” and challenge. Minimize repetitiveness and standardization and encourage employees to learn and master new things outside of their skill set.
Astrocytes are one thread in the complex tapestry of intelligence, but our growing knowledge about astrocytes has made intelligence a little less baffling today than it was a few years ago.
When Diamond (who passed away last week) reported her findings in 1985, the overwhelming conclusion was that Einstein’s brain was not much different from anyone else’s. Today, we can confidently say that Einstein’s brain was very different, after all. Read More
Megvii Inc., the Chinese developer of facial recognition system Face++, is said to be raising at least $600 million from investors including Alibaba Group Holding Ltd. and Boyu Capital, according to people familiar with the matter.
The Beijing-based company, which already counts billionaire Jack Ma’s Ant Financial and one of China’s largest state-backed venture funds as investors, will close this round of funding within weeks, the people said, asking not to be named because the matter is private. The company will then seek a second tranche of funding, the people said.
Alibaba is ramping up its investment in China’s largest artificial intelligence startups, hoping to employ the technology across its growing internet and retail empire. Megvii provides face-scanning systems to companies including Lenovo Group Ltd. and Ant Financial, the payments company that underpins Alibaba’s e-commerce platforms. It’s competing with SenseTime, another startup backed by Alibaba, for market share in sectors such as retail, finance and smartphone and public security that could utilize facial recognition. Read More