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Top trends impacting artificial intelligence in 2020

Artificial intelligence will continue to gain popularity for both businesses and consumers alike this year. Looking ahead into the new decade, we will begin to see an acceleration of AI adoption as the lines between data and AI begin to blur.

New and simpler tools will be developed as new methods and models emerge. And, as with any new technology, new challenges arise. For example, AI-generated deepfakes will continue to create new machine deception and remain an obstacle – but the hope is that new automated detection methods will be developed as fast as new forms of machine deception are identified.

This article outlines some predicted advancements in automation, hardware, tools and model development that will shape, or even accelerate, AI in 2020.

AI Adoption: Accelerated

The AI space is poised for an acceleration in adoption, which is driven by more sophisticated AI models being put in production, specialized hardware that increases AI’s capacity to provide quicker results based on larger datasets, simplified tools that democratize access to the entire AI stack, small tools that enables AI on nearly any device, and cloud access to AI tools that allow access to AI resources from anywhere.

Integrating data from many sources, complex business and logic challenges, and competitive incentives to make data more useful all combine to elevate AI and automation technologies from optional to required. And AI processes have unique capabilities that can address an increasingly diverse array of automation tasks—tasks that defy what traditional procedural logic and programming can handle, for example, image recognition, summarization, labeling, complex monitoring, and response.

In fact, according to one of O’Reilly’s 2019 surveys, over half of the respondents say AI (deep learning, specifically) will be part of their future projects and products—and a majority of companies are starting to adopt machine learning.

The Line Between Data and AI will Become Blurred

Access to the amount of data necessary for AI, proven use cases for both consumer and enterprise AI, and more-accessible tools for building applications have grown dramatically, spurring new AI projects and pilots.

To stay competitive, data scientists need to at least dabble in machine and deep learning. At the same time, current AI systems rely on data-hungry models, so AI experts will require high-quality data and a secure and efficient data pipeline. As these disciplines merge, data professionals will need a basic understanding of AI, and AI experts will need a foundation in solid data practices, and, likely, a more formal commitment to data governance.

New Development of Simpler Tools, Infrastructures, and Hardware

We’re in a highly empirical era for machine learning. Tools for machine learning development need to account for the growing importance of data, experimentation, model search, model deployment, and monitoring. At the same time, managing the various stages of AI development is getting easier with the growing ecosystem of open source frameworks and libraries, cloud platforms, proprietary software tools, and SaaS.

New Emerging Models and Methods

While deep learning continues to drive a lot of interesting research, most end-to-end solutions are hybrid systems.

In 2020 we‘ll hear more about the essential role of other components and methods—including Bayesian and other model-based methods, tree search, evolution, knowledge graphs, simulation platforms, and others. We also expect to see new use cases for reinforcement learning emerge. And we just might begin to see exciting developments in machine learning methods that aren’t based on neural networks.

New Developments will Enable New Applications

Developments in computer vision and speech/voice (“eyes and ears”) technology help drive the creation of new products and services that can make personalized, custom-sized clothing, drive autonomous harvesting robots, or provide the logic for proficient chatbots. Work on robotics (“arms and legs”) and autonomous vehicles is compelling and closer to market.

There’s also a new wave of startups targeting “traditional data” with new AI and automation technologies. This includes text (new NLP and NLU solutions; chatbots), time series and temporal data, transactional data, and logs.

And both traditional enterprise software vendors and startups are rushing to build AI applications that target specific industries or domains. This is in line with findings in a recent McKinsey survey: enterprises are using AI in areas where they’ve already invested in basic analytics.

Handling Fairness: All Data has Built-In Biases

Taking a cue from the software quality assurance world, those working on AI models need to assume their data has built-in or systemic bias and other issues related to fairness—like the assumption that bugs exist in software—and that formal processes are needed to detect, correct, and address those issues

Detecting bias and ensuring fairness doesn’t come easy and is most effective when subject to review and validation from a diverse set of perspectives. That means building in intentional diversity to the processes used to detect unfairness and bias—cognitive diversity, socioeconomic diversity, cultural diversity, physical diversity—to help improve the process and mitigate the risk of missing something critical.

Machine Deception will be a Serious Challenge

Deepfakes have telltale signs that automated detection systems can look for to detect their presence: unnatural blinking patterns, inconsistent lighting, facial distortion, inconsistencies between mouth movements and speech, and the lack of small but distinct individual facial movements (how Donald Trump purses his lips before answering a question, for example).

But deepfakes are getting better. With 2020 a US election year, automated detection methods will have to be developed as fast as new forms of machine deception are launched. But automated detection may not be enough. Detection models themselves can be used to stay ahead of the detectors. Within a couple months of the release of an algorithm that spots unnatural blinking patterns for example, the next generation of deepfake generators had incorporated blinking into their systems.

Programs that can automatically watermark and identify images when taken or altered or using blockchain technology to verify content from trusted sources could be a partial fix, but as deepfakes improve, trust in digital content diminishes. Regulation may be enacted, but the path to effective regulation that doesn’t interfere with innovation is far from clear.

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