6. Emergent artificial intelligence
What happens when a computer can learn on the job?
Artificial intelligence (AI) is, in simple terms, the science of doing by computer the things that people can do. Over recent years, AI has advanced significantly: most of us now use smartphones that can recognize human speech, or have travelled through an airport immigration queue using image-recognition technology. Self-driving cars and automated flying drones are now in the testing stage before anticipated widespread use, while for certain learning and memory tasks, machines now outperform humans. Watson, an artificially intelligent computer system, beat the best human candidates at the quiz game Jeopardy.
Artificial intelligence, in contrast to normal hardware and software, enables a machine to perceive and respond to its changing environment. Emergent AI takes this a step further, with progress arising from machines that learn automatically by assimilating large volumes of information. An example is NELL, the Never-Ending Language Learning project from Carnegie Mellon University, a computer system that not only reads facts by crawling through hundreds of millions of web pages, but attempts to improve its reading and understanding competence in the process in order to perform better in the future.
Like next-generation robotics, improved AI will lead to significant productivity advances as machines take over – and even perform better – at certain tasks than humans. There is substantial evidence that self-driving cars will reduce collisions, and resulting deaths and injuries, from road transport, as machines avoid human errors, lapses in concentration and defects in sight, among other problems. Intelligent machines, having faster access to a much larger store of information, and able to respond without human emotional biases, might also perform better than medical professionals in diagnosing diseases. The Watson system is now being deployed in oncology to assist in diagnosis and personalized, evidence-based treatment options for cancer patients.
Long the stuff of dystopian sci-fi nightmares, AI clearly comes with risks – the most obvious being that super-intelligent machines might one day overcome and enslave humans. This risk, while still decades away, is taken increasingly seriously by experts, many of whom signed an open letter coordinated by the Future of Life Institute in January 2015 to direct the future of AI away from potential pitfalls. More prosaically, economic changes prompted by intelligent computers replacing human workers may exacerbate social inequalities and threaten existing jobs. For example, automated drones may replace most human delivery drivers, and self-driven short-hire vehicles could make taxis increasingly redundant.
On the other hand, emergent AI may make attributes that are still exclusively human – creativity, emotions, interpersonal relationships – more clearly valued. As machines grow in human intelligence, this technology will increasingly challenge our view of what it means to be human, as well as the risks and benefits posed by the rapidly closing gap between man and machine.
7. Distributed manufacturing
The factory of the future is online – and on your doorstep
Distributed manufacturing turns on its head the way we make and distribute products. In traditional manufacturing, raw materials are brought together, assembled and fabricated in large centralized factories into identical finished products that are then distributed to the customer. In distributed manufacturing, the raw materials and methods of fabrication are decentralized, and the final product is manufactured very close to the final customer.
In essence, the idea of distributed manufacturing is to replace as much of the material supply chain as possible with digital information. To manufacture a chair, for example, rather than sourcing wood and fabricating it into chairs in a central factory, digital plans for cutting the parts of a chair can be distributed to local manufacturing hubs using computerized cutting tools known as CNC routers. Parts can then be assembled by the consumer or by local fabrication workshops that can turn them into finished products. One company already using this model is the US furniture company AtFAB.
Current uses of distributed manufacturing rely heavily on the DIY “maker movement”, in which enthusiasts use their own local 3D printers and make products out of local materials. There are elements of open-source thinking here, in that consumers can customize products to their own needs and preferences. Instead of being centrally driven, the creative design element can be more crowdsourced; products may take on an evolutionary character as more people get involved in visualizing and producing them.
Distributed manufacturing is expected to enable a more efficient use of resources, with less wasted capacity in centralized factories. It also lowers the barriers to market entry by reducing the amount of capital required to build the first prototypes and products. Importantly, it should reduce the overall environmental impact of manufacturing: digital information is shipped over the web rather than physical products over roads or rails, or on ships; and raw materials are sourced locally, further reducing the amount of energy required for transportation.
If it becomes more widespread, distributed manufacturing will disrupt traditional labour markets and the economics of traditional manufacturing. It does pose risks: it may be more difficult to regulate and control remotely manufactured medical devices, for example, while products such as weapons may be illegal or dangerous. Not everything can be made via distributed manufacturing, and traditional manufacturing and supply chains will still have to be maintained for many of the most important and complex consumer goods.
Distributed manufacturing may encourage broader diversity in objects that are today standardized, such as smartphones and automobiles. Scale is no object: one UK company, Facit Homes, uses personalized designs and 3D printing to create customized houses to suit the consumer. Product features will evolve to serve different markets and geographies, and there will be a rapid proliferation of goods and services to regions of the world not currently well served by traditional manufacturing.
8. ‘Sense and avoid’ drones
Flying robots to check power lines or deliver emergency aid
Unmanned aerial vehicles, or drones, have become an important and controversial part of military capacity in recent years. They are also used in agriculture, for filming and multiple other applications that require cheap and extensive aerial surveillance. But so far all these drones have had human pilots; the difference is that their pilots are on the ground and fly the aircraft remotely.
The next step with drone technology is to develop machines that fly themselves, opening them up to a wider range of applications. For this to happen, drones must be able to sense and respond to their local environment, altering their height and flying trajectory in order to avoid colliding with other objects in their path. In nature, birds, fish and insects can all congregate in swarms, each animal responding to its neighbour almost instantaneously to allow the swarm to fly or swim as a single unit. Drones can emulate this.
With reliable autonomy and collision avoidance, drones can begin to take on tasks too dangerous or remote for humans to carry out: checking electric power lines, for example, or delivering medical supplies in an emergency. Drone delivery machines will be able to find the best route to their destination, and take into account other flying vehicles and obstacles. In agriculture, autonomous drones can collect and process vast amounts of visual data from the air, allowing precise and efficient use of inputs such as fertilizer and irrigation.
In January 2014, Intel and Ascending Technologies showcased prototype multi-copter drones that could navigate an on-stage obstacle course and automatically avoid people who walked into their path. The machines use Intel’s RealSense camera module, which weighs just 8g and is less than 4mm thick. This level of collision avoidance will usher in a future of shared airspace, with many drones flying in proximity to humans and operating in and near the built environment to perform a multitude of tasks. Drones are essentially robots operating in three, rather than two, dimensions; advances in next-generation robotics technology will accelerate this trend.
Flying vehicles will never be risk-free, whether operated by humans or as intelligent machines. For widespread adoption, sense and avoid drones must be able to operate reliably in the most difficult conditions: at night, in blizzards or dust storms. Unlike our current digital mobile devices (which are actually immobile, since we have to carry them around), drones will be transformational as they are self-mobile and have the capacity of flying in the three-dimensional world that is beyond our direct human reach. Once ubiquitous, they will vastly expand our presence, productivity and human experience.
9. Neuromorphic technology
Computer chips that mimic the human brain
Even today’s best supercomputers cannot rival the sophistication of the human brain. Computers are linear, moving data back and forth between memory chips and a central processor over a high-speed backbone. The brain, on the other hand, is fully interconnected, with logic and memory intimately cross-linked at billions of times the density and diversity of that found in a modern computer. Neuromorphic chips aim to process information in a fundamentally different way from traditional hardware, mimicking the brain’s architecture to deliver a huge increase in a computer’s thinking and responding power.
Miniaturization has delivered massive increases in conventional computing power over the years, but the bottleneck of shifting data constantly between stored memory and central processors uses large amounts of energy and creates unwanted heat, limiting further improvements. In contrast, neuromorphic chips can be more energy efficient and powerful, combining data-storage and data-processing components into the same interconnected modules. In this sense, the system copies the networked neurons that, in their billions, make up the human brain.
Neuromorphic technology will be the next stage in powerful computing, enabling vastly more rapid processing of data and a better capacity for machine learning. IBM’s million-neuron TrueNorth chip, revealed in prototype in August 2014, has a power efficiency for certain tasks that is hundreds of times superior to a conventional CPU (Central Processing Unit), and more comparable for the first time to the human cortex. With vastly more compute power available for far less energy and volume, neuromorphic chips should allow more intelligent small-scale machines to drive the next stage in miniaturization and artificial intelligence.
Potential applications include: drones better able to process and respond to visual cues, much more powerful and intelligent cameras and smartphones, and data-crunching on a scale that may help unlock the secrets of financial markets or climate forecasting. Computers will be able to anticipate and learn, rather than merely respond in pre-programmed ways.
10. Digital genome
Healthcare for an age when your genetic code is on a USB stick
While the first sequencing of the 3.2 billion base pairs of DNA that make up the human genome took many years and cost tens of millions of dollars, today your genome can be sequenced and digitized in minutes and at the cost of only a few hundred dollars. The results can be delivered to your laptop on a USB stick and easily shared via the internet. This ability to rapidly and cheaply determine our individual unique genetic make-up promises a revolution in more personalized and effective healthcare.
Many of our most intractable health challenges, from heart disease to cancer, have a genetic component. Indeed, cancer is best described as a disease of the genome. With digitization, doctors will be able to make decisions about a patient’s cancer treatment informed by a tumour’s genetic make-up. This new knowledge is also making precision medicine a reality by enabling the development of highly targeted therapies that offer the potential for improved treatment outcomes, especially for patients battling cancer.
Like all personal information, a person’s digital genome will need to be safeguarded for privacy reasons. Personal genomic profiling has already raised challenges, with regard to how people respond to a clearer understanding of their risk of genetic disease, and how others – such as employers or insurance companies – might want to access and use the information. However, the benefits are likely to outweigh the risks, because individualized treatments and targeted therapies can be developed with the potential to be applied across all the many diseases that are driven or assisted by changes in DNA.
This list was compiled by the Meta-Council on Emerging Technologies, who would like to thank: Justine Cassell, Professor, Human-Computer Interaction, Carnegie Mellon University; Paolo Dario, Director, The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa; Julia Greer, Professor of Materials Science and Mechanics, California Institute of Technology (Caltech); and Jennifer Lewis, Hansjorg Wyss Professor at the Harvard School of Engineering and Applied Sciences, from theNetwork of Global Agenda Councils; Michael Pellini, President and Chief Executive Officer, Foundation Medicine Inc., from the Technology Pioneers; and William “Red” Whittaker, Professor at Carnegie Mellon University, for their invaluable contributions to the creation of this list.
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