Only data collected from the "wrist" is used in our experiment.

Only data collected from the "wrist" is used in our experiment.

The pursuit of sustainable enterprise growth has accompanied the rising interest in optimizing the use of global resources. Companies know that the proper management of global resources today isn't strictly a question of ethics, but can also have a very real impact on building competitiveness. Sustainable growth is becoming a leading trend in the business world, one that can be supported by AI and advanced data analysis. In business, basing decisions on reliable, real data can be a key means to achieving sustainable growth. AI has made it possible to monitor and analyze huge amounts of data, from various sources, more or less in real time.

There is no question that the enterprises that successfully leverage AI's potential will be the ones to get ahead. While businesses are more and more certain about the goals they want to use AI for, how to execute and deploy it successfully remains a difficult question. In today's world, the dynamics and efficiency of operations are crucial factors, and data is the key to flourishing in the rapidly changing business reality. Data provides insights from various areas of business activity. Solutions based on artificial intelligence or machine learning not only support the analysis of huge amounts of data, but also provide a new approach to optimizing and automating core processes.

Recent developments in computer vision provide tools to the data scientists to automate a broader range of tasks. Yet companies keep wondering how best to recruit machine learning for their particular niche. One of the most common problems is recognizing how a machine learning model will perform the task uniquely than an individual would. Computer vision is an integrative field that enables computers to recognize, process, and analyse images. It uses algorithms that can process both static images and videos.

While making predictions may be easy, delivering accurate ones is an altogether different story. That's why in this column we won't just be looking at the most important trends of 2020, but we'll also look at how the ideas we highlighted last year have developed. In summarizing the trends of 2020, one conclusion we've come to is that society is getting increasingly interested in AI technology, in terms of both the threats it poses and common knowledge about other problems that need to be addressed. In our AI Trends 2019 blogpost we chronicled last year's most important trends and directions of development to watch. It was shortly after launching the AI Monthly Digest, a monthly summary of the most significant and exciting machine learning news.

As we predicted in our AI Trends 2020, NLP is the year's leading trend. But research on self-regenerating structures has likewise been both surprising and fascinating. February was rich in news on self-regenerating machines and NLP-related events and research. The tech behemoths are racing to build larger and more efficient language models, which are both costly and technologically challenging, if not daunting. Microsoft is a relatively new player in the game, and follows in the footsteps of Google with itsBERT model and OpenAI, which brought outGPT-2.

Computer vision enables machines to perform once-unimaginable tasks like diagnosing diabetic retinopathy as accurately as a trained physician or supporting engineers by automating their daily work. Recent advances in computer vision are providing data scientists with tools to automate an ever-wider range of tasks. Yet companies sometimes don't know how best to employ machine learning in their particular niche. The most common problem is understanding how a machine learning model will perform its task differently than a human would. Computer vision is an interdisciplinary field that enables computers to understand, process and analyze images.

As the recently launched AI Monthly digest shows, significant improvements, breakthroughs and game-changers in machine learning and AI are months or even weeks away, not years. It is, therefore, worth the challenge to summarize and show the most significant AI trends that are likely to unfold in 2019, as machine learning technology becomes one of the most prominent driving forces in both business and society. According to a recent Deloitte study, 82% of companies that have already invested in AI have gained a financial return on their investment. For companies among all industries, the median return on investment from cognitive technologies is 17%. AI is transforming daily life and business operations in a way seen during previous industrial revolutions.

With convolutional neural networks and state-of-the-art image recognition techniques it is possible to make old movie classics shine again. Neural networks polish the image, reduce the noise and apply colors to the aged images. The first movies were created in the late nineteenth century with celluloid photographic film used in conjunction with motion picture cameras. Skip ahead to 2018, when the global movie industry was worth $41.7 billion globally. Serving entertainment, cultural and social purposes, films are a hugely important heritage to protect.