Could a computer think like a human?
Insights from Morgan Stanley Research05/28/21
Summary: New modes of computing with the capacity to “think" will power the next generation of artificial intelligence (AI), potentially creating significant opportunities.
Dr. John Kelly of IBM estimates 90% of the world's data is dark, meaning humans and computers can’t yet use it in a meaningful way.
As data accumulate, the incentive grows to mine it for insights that may fuel a broad range of AI applications, from voice assistants to robotics to driverless cars. Doing so effectively will take a new class of computing known as cognitive computing.
Rather than relying on faster processors, computers will gain speed and power by mimicking the human brain using breakthroughs in physics and biological science. “The future of computing will not—and cannot—be based on ever-increasing processing power,” says Shawn Kim, Head of the Asia Technology team for Morgan Stanley Research. “Instead it will rely on understanding and drawing inferences from massive collections of data." In other words, computers will use data to learn, adapt, evolve and “think,” much like the human brain.
Two new computing paradigms, quantum computing and neuromorphic computing, aim to make that possible within a decade. Quantum computing is capable of solving problems that would take the world's fastest supercomputers years to tackle. It performs operations without human intervention, such as autonomous vehicles, personal assistants, and drones.
Neuromorphic computing creates the capacity to sense, learn, infer, and make real-time decisions much like the human brain, potentially augmenting human capabilities by collaborating with us to solve tough problems, like medical diagnoses.
Taking a page from biology
Hopes for cognitive systems hinge largely on the emergence of neuromorphic computing, which mimics characteristics of the human brain to drive massive improvements in efficiency, processing, and reasoning, relative to traditional computer architectures.
Emulating the brain is no small task. Nevertheless, researchers have made significant progress creating neuromorphic chips that store and retrieve large amounts of information simultaneously.
Unlike traditional AI, neuromorphic chips don't require explicit instructions in code or millions of prior examples to learn from, thus providing significant opportunities to power a range of applications.
Market-intelligence consultancy Grand View Research sees the neuromorphic computing market growing to $6.5 billion by 2024. Morgan Stanley Research expects image processing to account for the largest share of applications by revenue, with neuromorphic chips supporting key AI endeavors like autonomous driving.
Quantum computing has arrived—just don't expect to find a quantum computer near you anytime soon. These machines operate at extremely cold temperatures and live in the cloud, where an increasing number of full-scale systems are up and running.
Experts tell Morgan Stanley that large-scale commercial rollout may only be years away. To speed up that timeline, quantum-computing market leaders are focused on educating potential customers about possible applications while building an ecosystem of developers.
To be sure, quantum computing is no panacea. Coding can be incredibly complex, and qubits, the basic units of quantum computing, remain unstable and error-prone.
Still, Morgan Stanley sees three categories of investments: companies building universal quantum computers, companies with task-specific quantum abilities, and companies offering simulation platforms for quantum computers.
Racing to own the next era of computing
Other, more-distant frontiers of computing also show promise in the lab. Optical computing, for example, transmits data through glass cables to achieve orders-of-magnitude performance gains over current systems.
As these new computing paradigms make their way into commercial use, international competition to own the next era of computing is heating up. With critical national security advantages and national prestige at stake, China and the US are in a race to file patents for new quantum technologies.
What investors should consider
Those who are looking for investing opportunities in this area of technology may look to mutual funds or exchange-traded funds (ETFs) focused on artificial intelligence.
ETFs and mutual funds are both collections, or “baskets,” of individual stocks, bonds, or other assets—in some cases hundreds of them—all pooled together. When you buy a share of the fund, you own a small piece of this basket of assets. ETFs and mutual funds are similar in that they give you a broad range of investment choices and inherently offer greater diversification than buying a single stock. That said, there are distinct differences in these investment choices. ETFs can be bought and sold throughout the trading day, tend to have lower fees, and are typically passively managed—mirroring the performance of an index. On the other hand, mutual funds trade once a day, typically have higher expense ratios, and are managed by professional fund managers who actively try to outperform a market or index.
To learn more about the distinctions between mutual funds and ETFs, check out ETFs vs. mutual funds: Understand the difference.
Bottom line: A new era of quantum computing promises to transform businesses for years to come. Investors looking for exposure to this trend may turn to ETFs, but do your homework before diving in.
The source of this Morgan Stanley article, Could a Computer Think Like a Human?, was originally published on October 1, 2020.
What to read next...
How can E*TRADE help?
Find ETFs that align with your values or with social, economic, and technology trends.
Choose from a list of 100 leading exchange-trade funds, selected by E*TRADE's investment strategy team.
Investing and trading account
Buy and sell stocks, ETFs, mutual funds, options, bonds, and more.