The 2020 AI Trends That Will Shape The Technology Landscape And Beyond

The 2020 AI Trends That Will Shape The Technology Landscape And Beyond

The evolution of AI is reshaping the world’s technological landscape. And with technology increasingly permeating our individual, social and professional lives, AI trends influence almost every economic sector.

And their influence doesn’t stop there, extending into geo-politics. The social media-based infiltration of global political processes has largely been made possible by AI-algorithms masterminding campaigns designed to target groups of individuals that answer to a specific profile. AI-powered ‘deep fakes’ also have the potential to take ‘false news’ to a new level.

And the evolution of the AI used by technology platforms to collate and make use of personal data is a double-edged sword, balancing the positives that can result from the personalisation of various products and services with major privacy concerns.

Managed in the right way, the positive influence of increasingly sophisticated AI should prove revolutionary over coming years. Industrial and manufacturing efficiencies, scientific and medical breakthroughs and the automation of low-skilled, monotonous and poorly paid jobs, as long as they are replaced by new employment opportunities, hold the promise of improving global living standards.

Finding the right balance, which probably means effectively regulating the use of powerful AI capabilities, is key. But most experts are united in their belief that the evolution of AI is on the verge of triggering the biggest leap forward for humanity since the Industrial Revolution. This will happen over coming decades and not overnight. And the dangers that are also inherent in how AI is used must be carefully managed.

But noticeable AI-powered change is already happening and the pace of growth in its influence is expected to quickly accelerate from now. 2020 will almost certainly prove to be a landmark year for AI developments. Here are the 2020 AI trends technology giants Philips believe, based on interviewing some of the world’s top engineers and AI thought leaders, will be the most significant over the coming year.

AI-Led Healthcare Improvements

At the top of the list, where AI’s influence is forecast to be most apparent in 2020 is the healthcare sector. Joroen Tas, who holds the position of Chief Innovation and Strategy Officer at Philips, comments:

“AI’s main impact in 2020 will be transforming healthcare workflows to the benefit of patients and healthcare professionals alike, while at the same time reducing costs. Its ability to acquire data in real-time from multiple hospital information flows – electronic health records, emergency department admissions, equipment utilization, staffing levels etc. – and to interpret and analyse it in meaningful ways will enable a wide range of efficiency and care enhancing capabilities.”

Some of the specific applications of AI in healthcare where change is already happening include the optimisation of scheduling, automated reporting and automated initialisation of equipment settings. The result will be healthcare becoming far more personalised and adapted to:

“An individual clinician’s way of working and an individual patient’s condition – features that improve the patient and staff experience, result in better outcomes, and contribute to lower costs.”

“There is tremendous waste in many healthcare systems related to complex administration processes, lack of preventative care, and over- and under-diagnosis and treatment. These are areas where AI could really start to make a difference. Further out, one of the most promising applications of AI will be in the area of ‘Command Centers’ which will optimize patient flow and resource allocation.”

Much of these developments will rely on the smooth integration of AI-based software into existing workflows in healthcare.

“AI-enabled systems will track, predict, and support the allocation of patient acuity and availability of medical staff, ICU beds, operating rooms, and diagnostic and therapeutic equipment.”

AI In Drug Discovery

AI is expected to usher in an era of massive breakthroughs in drug discovery. Algorithms are already being used by pharmaceuticals and biotech companies and improving all the time. The result will not only be new drug-based treatments being uncovered but a much more personalised approach to the drugs given to treat individual patients. They will take into considerations specifics of a single patient’s condition as well as their genetic make-up and external factors unique to their circumstances.

Chooch CEO Emrah Gultekin explains how he sees the application of AI in drugs discovery continuing to develop in 2020:

“We predict drug discovery will be vastly improved in 2020 as manual visual processes are automated because visual AI will be able to monitor and detect cellular drug interactions on a massive scale. Currently, years are wasted in clinical trials because drug researchers are taking notes, then entering those notes in spreadsheets and submitting them to the FDA for approval. Instead, highly accurate analysis driven by AI can lead to radically faster drug discoveries.”

There’s hope that AI algorithms can speed up the process of experimentation and data gathering in drug discovery, massively reducing the time involved in research and clinical trials, that can mean the whole process from beginning to end can take 12 years for a new drug. That would also mean a major fall in the costs associated with developing new drugs. Gultekin adds:

“Additionally, cell counting is a massive problem in biological research—not just in drug discovery. People are hunched over microscopes or sitting in front of screens with clickers in their hands counting cells. There are expensive machines that attempt to count, inaccurately. But visual AI platforms can perform this task in seconds, with 99% accuracy in just moments.”

Greater Focus On Trust And ‘Explainability’ Of AI Applications

AI has, it is fair to say, some trust issues. Some of them are justified and the result of the loose relationship tech companies have sometimes shown themselves to have with business ethics. The interests of users or the general public have not always been put ahead of commercial interests or ‘progress’.

But the tech world, also under pressure from users, shareholders, governments and regulators, is now realising that trust is important to the further development and integration of AI in our everyday lives.

Before we allow our data, even anonymised, to be used to ‘teach’ AI algorithms, we want to be confident offering access to it will not be misused. And we want to feel confident that AI-powered appliances, from home assistants to wearables, are not exploiting in any other way the access into our lives we must grant them before taking advantage of what they offer. Part of that trust building process will also be greater transparency around how AI algorithms work – the logic behind how AI informs a choice or course of action.

Karthik Ramakrishnan, Element AI’s Head of Advisory and AI Enablement, comments:

“2020 will be the year of AI trustability. 2019 saw the emergence of early principles for AI ethics and risk management, and there have been early attempts at operationalising these principles in toolkits and other research approaches. The concept of explainability (being able to explain the forces behind AI-based decisions) is also becoming increasingly well known.”

“In 2020, enterprises will pay closer attention to AI trust whether they’re ready to or not. Expect to see VCs pay attention, too, with new start-ups emerging to help with solutions.”

There was clearly a growing focus on AI ethics in 2019. Among the notable developments was the European Commission publishing a set of seven guidelines for the development of ethical AI. In October, Element AI, co-founded by Yoshua Bengio, one of the pioneers of deep learning, partnered with the Mozilla Foundation to create data trusts and campaign for the ethical use of AI. Big tech companies including Microsoft and Google have also taken steps toward making their AI development conformant to ethical norms.

Kate Saenko, Associate Professor at Boston University’s Department of Computer Science, adds:

“There will be more scrutiny of the reliability and bias behind these AI methods as they become more widely deployed in society, for example, more local governments considering a ban on AI-powered surveillance because of privacy and fairness concerns.”

AI Able To Do More With Less (Data)

Tied inextricably to trust issues around AI is the fact that deep learning techniques are so data hungry. They only work well when trained on huge volumes of data. But that is starting to change, say Rana el Kalioyby, CEO and co-founder of Affectiva:

“We’ll see a rise of data synthesis methodologies to combat data challenges in AI. Many researchers in the AI space are beginning to test and use emerging data synthesis methodologies to overcome the limitations of real-world data available to them. With these methodologies, companies can take data that has already been collected and synthesize it to create new data”.

“Take the automotive industry, for example. There’s a lot of interest in understanding what’s happening with people inside of a vehicle as the industry works to develop advanced driver safety features and to personalise the transportation experience. However, it’s difficult, expensive, and time-consuming to collect real-world driver data. Data synthesis is helping address that – for example, if you have a video of me driving in my car, you can use that data to create new scenarios, i.e., to simulate me turning my head, or wearing a hat or sunglasses.”

Advances is fields such as generative adversarial networks (GAN) mean the data used in training AI algos can now be synthesised. This doesn’t eliminate the need for real world data. But a lot less of it is beginning to be needed. A foundational real world data base can be augmented by synthesising additional layers of data from the ‘parent’ set.

Neural Networks Improving Accuracy & Efficiency

The neural networks used in many areas of AI, based on the biology of how our own brains work, are improving. Kate Saenko, Associate Professor at Boston University’s Department of Computer Science, believes the improvement in neural networks will be an influential theme in how AI develops over 2020:

“Neural network architectures will continue to grow in size and depth and produce more accurate results and become better at mimicking human performance on tasks that involve data analysis. At the same time, methods for improving the efficiency of neural networks will also improve, and we will see more real-time and power-efficient networks running on small devices.”

One issue this is expected to lead to is the increasing realism of deepfakes. AI will be better able to manipulate text, images, video and audio in ways that become harder and harder to detect. This is provoking the development of AI designed to spot deepfakes, with the cat-and-mouse between the two sides intensifying.

AI Creating New AI

2020 is expected to see significant development in the space of automated AI development. Essentially, AI software able to create new AI software. Sriram Raghavan, VP of IBM Research AI calls this ‘AI for AI’ and explains:

“Using AI to help automate the steps and processes involved in the life cycle of creating, deploying, managing, and operating AI models to help scale AI more widely into the enterprise.”

“In addition, we will begin to see more examples of the use of neurosymbolic AI which combines statistical data-driven approaches with powerful knowledge representation and reasoning techniques to yield more explainable & robust AI that can learn from less data.”

Most of the big tech companies and AI specialists are pouring resources into this area of A R&D.

Google has developed AutoML, a tool that simplifies the process of creating machine learning models and makes the technology accessible to a wider audience. IBM has also launched AutoAI, a platform for automating data preparation, model development, feature engineering, and hyperparameter optimization.

AI In Manufacturing

Neurala CEO and co-founder Massimiliano Versace, believes 2020 will be the year manufacturers leverage AI in making production lines more efficient. And overcome data sensitivity and cost issues.

“2020 will be the year that the manufacturing industry embraces AI to modernize the production line. For the manufacturing industry, one of the biggest challenges is quality control. Product managers are struggling to inspect each individual product and component while also meeting deadlines for massive orders.”

“In the same way that the power drill changed the way we use screwdrivers, AI will augment existing processes in the manufacturing industry by reducing the burden of mundane and potentially dangerous tasks, freeing up workers’ time to focus on innovative product development that will push the industry forward.”

Versace also forecasts manufacturers will overcome the significant expense of cloud services involved in leveraging AI in production processes by moving data processing to ‘the edge’. An Edge Cloud architecture is used to decentralise processing power to the edges (clients/devices) of a network. Within the cloud model, tasks are performed by servers so they can be transferred to other devices with less computing power.

“New routes to training AI that can be deployed and refined at the edge will become more prevalent. As we move into the new year, more and more manufacturers will begin to turn to the edge to generate data, minimize latency problems and reduce massive cloud fees. By running AI where it is needed (at the edge), manufacturers can maintain ownership of their data.”

The Geopolitical AI Arms Race

This is certainly a trend that will continue to develop well beyond 2020, but AI has become a pressing question of national and economic security and governments are investing heavily in what is quickly becoming an AI arms race. Ishan Manaktala, CEO of Symphony AyasdiAI comments:

“Already, governments are investing heavily in AI as a possible next competitive front. China has invested over $140 billion, while the UK, France, and the rest of Europe have plowed more than $25 billion into AI programs. The U.S., starting late, spent roughly $2 billion on AI in 2019 and will spend more than $4 billion in 2020”.

“But experts urge more investment, warning that the U.S. is still behind. A recent National Security Commission on Artificial Intelligence noted that China is likely to overtake U.S. research and development spending in the next decade. The NSCAI outlined five points in its preliminary report: invest in AI R&D, apply AI to national security missions, train and recruit AI talent, protect U.S. technology advantages, and marshal global coordination.”

As with most major new technologies, AI has the potential to be both a hugely positive influence, as well as to hold risks. The trick will be leveraging AI in science, research, industry and administration, while safeguarding against its potential to be used irresponsibly or by malicious actors. It will be a journey and not one that starts in 2020. But 2020 does promise to be a very significant stage in that journey. Quite possibly the most significant to date.

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