Here are the five scientific instruments that will make the invisible visible in the next 5 years:
Brain disorders, including developmental, psychiatric and neurodegenerative diseases, represent an enormous disease burden, in terms of human suffering and economic cost.
For example, today, one in five adults in the U.S. experiences a mental health condition such as depression, bipolar disease or schizophrenia, and roughly half of individuals with severe psychiatric disorders receive no treatment. The global cost of mental health conditions is projected to surge to US$6 trillion by 2030.
If the brain is a black box that we don’t fully understand, then speech is a key to unlock it. In five years, what we say and write will be used as indicators of our mental health and physical wellbeing. Patterns in our speech and writing analyzed by new cognitive systems will provide tell-tale signs of early-stage developmental disorders, mental illness and degenerative neurological diseases that can help doctors and patients better predict, monitor and track these conditions.
At IBM, scientists are using transcripts and audio inputs from psychiatric interviews, coupled with machine learning techniques, to find patterns in speech to help clinicians accurately predict and monitor psychosis, schizophrenia, mania and depression. Today, it only takes about 300 words to help clinicians predict the probability of psychosis in a user.2
In the future, similar techniques could be used to help patients with Parkinson’s, Alzheimer’s, Huntington’s disease, PTSD and even neurodevelopmental conditions such as autism and ADHD.
More than 99.9 percent of the electromagnetic spectrum cannot be observed by the naked eye. Over the last 100 years, scientists have built instruments that can emit and sense energy at different wavelengths. Today, we rely on some of these to take medical images of our body, see the cavity inside our tooth, check our bags at the airport, or land a plane in fog. However, these instruments are incredibly specialized and expensive and only see across specific portions of the electromagnetic spectrum.
In five years, new imaging devices using hyperimaging technology and AI will help us see broadly beyond the domain of visible light by combining multiple bands of the electromagnetic spectrum to reveal valuable insights or potential dangers that would otherwise be unknown or hidden from view. Most importantly, these devices will be portable, affordable and accessible, so superhero vision can be part of our everyday experiences.
A view of the invisible or vaguely visible physical phenomena all around us could help make road and traffic conditions clearer for drivers and self-driving cars. For example, using millimeter wave imaging, a camera and other sensors, hyperimaging technology could help a car see through fog or rain, detect hazardous and hard-to-see road conditions such as black ice, or tell us if there is some object up ahead and its distance and size. Cognitive computing technologies will reason about this data and recognize what might be a tipped over garbage can versus a deer crossing the road, or a pot hole that could result in a flat tire.
Embedded in our phones, these same technologies could take images of our food to show its nutritional value or whether it’s safe to eat. A hyperimage of a pharmaceutical drug or a bank check could tell us what’s fraudulent and what’s not. What was once beyond human perception will come into view.
IBM scientists are today building a compact hyperimaging platform that “sees” across separate portions of the electromagnetic spectrum in one platform to potentially enable a host of practical and affordable devices and applications.
Today, an estimated 80 percent of a data scientist’s time is spent scrubbing data instead of analyzing and understanding what that data is trying to tell us.
Thanks to the Internet of Things, new sources of data are pouring in from millions of connected objects – from refrigerators, light bulbs and your heart rate monitor to remote sensors such as drones, cameras, weather stations, satellites and telescope arrays. There are already more than six billion connected devices generating tens of exabytes of data per month, with a growth rate of more than 30 percent per year. After successfully digitizing information, business transactions and social interactions, we are now in the process of digitizing the physical world.
In five years, we will use machine learning algorithms and software to help us organize the information about the physical world to help bring the vast and complex data gathered by billions of devices within the range of our vision and understanding. We call this a “macroscope” – but unlike the microscope to see the very small, or the telescope that can see far away, it is a system of software and algorithms to bring all of Earth’s complex data together to analyze it for meaning.
Beyond our own planet, macroscope technologies could handle, for example, the complicated indexing and correlation of various layers and volumes of data collected by telescopes to predict asteroid collisions with one another and learn more about their composition.
Diseases like cancer can be hard to detect – hiding in our bodies before symptoms appear. Information about the state of our health can be extracted from tiny bioparticles in bodily fluids such as saliva, tears, blood, urine and sweat.
In the next five years, new medical labs “on a chip” will serve as nanotechnology health detectives – tracing invisible clues in our bodily fluids and letting us know immediately if we have reason to see a doctor. The goal is to shrink down to a single silicon chip all of the processes necessary to analyze a disease that would normally be carried out in a full-scale biochemistry lab.
At IBM Research, scientists are developing lab-on-a-chip nanotechnology that can separate and isolate bioparticles down to 20 nanometers in diameter, a scale that gives access to DNA, viruses, and exosomes. These particles could be analyzed to potentially reveal the presence of disease even before we have symptoms.
Most pollutants are invisible to the human eye, until their effects make them impossible to ignore. Methane, for example, is the primary component of natural gas, commonly considered a clean energy source. But if methane leaks into the air before being used, it can warm the Earth’s atmosphere. Methane is estimated to be the second largest contributor to global warming after carbon dioxide (CO2).
In five years, new, affordable sensing technologies will enable the industry to pinpoint invisible leaks in real-time. Networks of IoT sensors wirelessly connected to the cloud will provide continuous monitoring of the vast natural gas infrastructure, allowing leaks to be found in a matter of minutes instead of weeks, reducing pollution and waste and the likelihood of catastrophic events.
Scientists at IBM are tackling this vision, working with natural gas to explore the development of an intelligent methane monitoring system and as part of the ARPA-E Methane Observation Networks with Innovative Technology to Obtain Reductions (MONITOR) program.
At the heart of IBM’s research is silicon photonics, an evolving technology that transfers data by light, allowing computing literally at the speed of light. These chips could be embedded in a network of sensors on the ground or within infrastructure that can be used to build complex environmental models to detect the origin and quantity of pollutants as they occur.