Trends and Predictions 2020: Artificial Intelligence

Stephen DeAngelis

January 13, 2020

Already widespread, artificial intelligence (AI) is poised to make a huge impact in the world. Lori Webber (@lfwebber), chief marketing officer of IBM Watson Supply Chain, states AI is “perhaps the most transformative [technology] of our era.”[1] AI’s impact will be the result of its ubiquity and its capabilities. Paul Pilotte (@ppilotte) and Bruce Tannenbaum, marketing managers at MathWorks, report, “MathWorks predicts that 2020 will see artificial intelligence play an increasingly visible role in a wide range of industries from industrial automation and medical devices to automotive and aerospace.”[2] They continue, “AI is everywhere. It’s not just powering applications like smart assistants, machine translation, and automated driving, it’s also giving engineers and scientists a set of techniques for tackling common tasks in new ways. We see more and more companies exploring AI in systems they create in industries like automotive, aeronautics, industrial machinery, oil and gas, and electric utilities.” Lance Lambert (@NewsLambert) reports, “The promise of artificial intelligence — that it can turn masses of fuzzy data into money-making moves — is becoming less sci-fi and more business as usual. Nearly 80% of chief information officers at U.S. companies plan to increase the use of artificial intelligence and machine learning over the next 12 months.”[3]

Artificial intelligence trends

Journalist Paramita (Guha) Ghosh reports on several of the most notable AI and machine learning (ML) trends for business analytics in 2020.[4] They include:

Embedded analytics platforms are proliferating. “Embedded analytics with built-in ML models will become a mainstream capability of all BI platforms in 2020.”

Digital assistants are becoming smarter. “AI and natural language processing (NLP) will jointly contribute to the creation of voice-activated assistants, similar to Alexa or Siri. These handy assistants will magically transcribe human language into data for further analysis.”

Cognitive augmentation is becoming the norm. “AI, ML, and NLP will provision tools for interacting with business data ‘organically.’ Businesses will no longer have to rely on data scientists and advanced data analytics platforms because augmented analytics will enable AI tools to interact with data directly to provide key, actionable insights to business users.”

Cognitive technologies continue to improve decision-making. “Business analytics platforms will have built-in AI and ML procedures to control the creation, dissemination, and sharing of analytics content.”

The Digital Age is maturing. “AI, ML, and DL technologies will collectively reshape the business ecosystem to a more data-centric and data-lucrative world.”

Artificial intelligence predictions

A number of companies and individuals have released their predictions about the future of artificial intelligence. For example, Pilotte and Tannenbaum predict AI will become increasingly complex. They explain, “As AI is trained to work with more sensor types (IMUs, Lidar, Radar, and so on), engineers are driving AI into a wide range of systems, including autonomous vehicles, aircraft engines, industrial plants, and wind turbines. These are complex, multidomain systems where behavior of the AI model has a substantial impact on the overall system performance. In this world, developing an AI model is not the finish line, it is merely a step along the way.” Other predictions include:

AI gets a seat at the table. Michael Feindt (@M_Feindt), founder of Blue Yonder and JDA’s chief scientific advisor, predicts the rise of AI will demand a new breed of business leader. He explains, “A new breed of leader [is required who] both understands AI and the business needs and challenges to drive [business] strategy. Data scientists can’t do it all; there needs to be an intermediary with the business acumen and technical AI knowledge that can help the company drive its initiatives forward. This leader will drive change management and trust at the executive level as that is still the biggest roadblock to AI adoption.”

Reinforcement learning moves from gaming to real world. Pilotte and Tannenbaum predict, “In 2020, reinforcement learning will go from playing games to enabling real-world industrial applications particularly for automated driving, autonomous systems, control design, and robotics.” Bobby Vohra adds, “By 2020, reinforcement learning is going to shift from gaming to real-world applications especially in control design, automated driving, robotics, and autonomous systems. There will be a high success rate seen wherever reinforcement learning (RL) is used to improve a larger system.”[6]

AI becomes easier to deploy to low power, low cost embedded devices. Pilotte, Tannenbaum, and Vohra all agree on this prediction. Pilotte and Tannenbaum explain, “AI has typically used 32-bit floating-point maths as available in high performance computing systems, including GPUs, clusters, and data centers. … Recent advances in software tools now support AI inference models with different levels of fixed-point maths. This enables the deployment of AI on low power, low cost devices and opens up a new frontier for engineers to incorporate AI in their designs.”

Simulation lowers a primary barrier to successful AI adoption – lack of data quality. Data is the lifeblood of AI platforms. Bad data is like getting a transfusion of tainted blood. Pilotte and Tannenbaum note, “Data quality is a top barrier to successful adoption of AI — per analyst surveys.” They go on to explain how simulation helps overcome the challenge of missing data. “Simulation will help lower this barrier in 2020. We know training accurate AI models requires lots of data. While you often have lots of data for normal system operation, what you really need is data from anomalies or critical failure conditions. This is especially true for predictive maintenance applications, such as accurately predicting remaining useful life for a pump on an industrial site. Since creating failure data from physical equipment would be destructive and expensive, the best approach is to generate data from simulations representing failure behavior and use the synthesized data to train an accurate AI model. Simulation will quickly become a key enabler for AI-driven systems.”

Trans-human augmentation. The Staff at SmartCitiesWorld boldly predicts, “Brain-computer interfaces will evolve beyond medical applications into commercial offerings by 2027. This will be ushered in as the technology to control devices using our brains improves. The interfaces will evolve to be used as in several commercial applications and their usage eventually spreads to consumer settings.” Humans have routinely augmented themselves with eyeglasses, prosthetics, etc.; however, brain augmentation takes trans-humanism to another level.

‘Deep fake’ detection technology will emerge by 2021. The SmartCitiesWorld staff also predicts we’ll finally get a handle on deep fakes. “These tools will be developed to counter doctored and convincingly realistic videos, created with AI, and released on social media. These videos and fake content bots, like Grover, take our challenges with fake news to a new level. In a landmark case, a celebrity will sue a news organization, hosting provider or software creator for damages caused by a fake video. Security and fake-detection systems, currently in research, will rapidly become commercial to tackle the ubiquity of these videos and the risks they pose to elections, governments and businesses.” Unfortunately, as we now know, knowing something is fake doesn’t stop people believing it.

Concluding thoughts

In the supply chain arena, Feindt sees the greatest benefits of AI emerging as cognitive platforms are shared with supply chain partners. He explains, “If AI is integrated horizontally across the company and its suppliers, the power is in the ability to plan better on both sides. For example, if the supplier knows what the retailer will order and vice versa, you will avoid having too much safety stock and plan better on both sides. AI will ultimately optimize the supply chain ecosystem — not just one company. This is where I see AI really taking off in terms of supply chains, driving the power of these decisions globally and breaking down silos.” Collaborative cognitive platforms will undoubtedly impact other areas of human activity as well.

[1] Staff, “The artificial intelligence revolution,” Supply Chain Quarterly, 14 November 2019.
[2] Paul Pilotte and Bruce Tannenbaum, “Five artificial intelligence trends for engineers and scientists,” Engineers Journal, 16 December 2019.
[3] Lance Lambert, “Why an Artificial Intelligence Wave Could Hit the Business World in 2020,” Fortune, 19 November 2019.
[4] Paramita (Guha) Ghosh, “Artificial Intelligence and Machine Learning Trends in 2020,” Dataversity, 3 December 2019.
[5] Staff, “7 AI Predictions for 2020,” Supply Chain Nation, 14 November 2019.
[6] Bobby Vohra, “Top AI Trends Every Data Scientists and Engineers Must Not Miss in 2020,” Datafloq, 3 December 2019.
[7] Staff, “Countering AI bias, domestic robots and detecting deep-fakes: 10 predictions for 2020 and beyond,” SmartCitiesWorld, 7 October 2019.