Artificial Intelligence and Data Science Advances in 2018 and Trends for 2019
We recap some of the major highlights in data science and AI throughout 2018, before looking at the some of the potential newest trends and technological advances for the year ahead.
Reinforcement learning: internet advertising as one of the numerous applications
Reinforcement learning (RL) is a machine learning technique that entails training an agent (algorithm) in an interactive environment. The agent learns on its own by trial and error: It gets rewards for performing a task correctly and penalties for making mistakes. In other words, the agent uses feedback based on its experiences and actions to define the optimal way of solving a problem while getting a maximum cumulative reward.
The principle of reinforcement learning. Source: freeCodeCamp
Aleksander Konduforov notes that this year introduced major improvements in reinforcement learning: “Big technology companies are getting good results with deep reinforcement learning, which can influence robotics, drone-based delivery, gaming, and will potentially also create new types of weapons, unfortunately.”
Internet advertising is another use case for reinforcement learning that will bring more and more profits to those who use it, thinks machine learning and data science expert Oleksandr Khryplyvenko: “Reinforcement learning is being welcomed by lots of companies. They were afraid of using it first, but the desire to optimize costs defeats the fear of adopting new technologies and taking risks.”
RL is used to improve real-time bidding strategy to dynamically allocate the advertisement campaign budget “across all the available impressions on the basis of both the immediate and future rewards.” During real-time bidding, an advertiser bids on an impression (ad view), and if they win an auction, their ad is displayed on a publisher’s platform.
The Alibaba Group, for instance, applies RL to optimize bidding on the eCommerce platform Taobao with a MARL (multi-agent reinforcement learning) algorithm. The linked research paper was published in September 2018. The results are solid: Data scientists achieved 340 percent ROI with 99.51 percent of spend budget with RL-based bidding. With manual bidding, ROI was 100 percent with 99.52 percent of budget spent.
AI ethics: using personal data with respect to privacy
The pervasive use of personal data by big corporations would have caused concerns about data security and privacy among individuals, governments, and organizations sooner or later. As a result, the General Data Protection Regulation (GDPR) has been in place since May 25, 2018; the California Consumer Privacy Act was approved and will take effect on January 1, 2020. These regulations put increased responsibility on companies and entities as they have to comply with both. “Since data science relies on data, specialists must take these regulations into account for their activities and solutions,” notes Aleksander Konduforov.
The discussion continues. In November 2018, the International Association of Privacy Professionals (IAPP) and UN Global Pulse have published the Building Ethics into Privacy Frameworks for Big Data and AI report. The European Commission’s High-Level Expert Group on Artificial Intelligence (AI HLEG) published the AI Ethics Guidelines draft a month after, on December 18.
Obviously, the attention to data ethics will continue growing in 2019.
Computer vision and artificial image generation
Computer vision, a branch of computer science aimed at enabling machines to see, analyze, and provide observation results, made some news in 2018.
Aleksander Konduforov notes that many of the computer vision advances were about image or video generation. For instance, BigGANs developed by DeepMind and Heriot-Watt University (UK) researchers introduced a new level of quality in image generation. BigGAN (generative adversarial network) is a neural network with a larger number of nodes (artificial neurons, its elementary units) trained on more images than usual.
“We demonstrate that GANs benefit dramatically from scaling, and train models with two to four times as many parameters and eight times the batch size compared to prior art,” the researchers note in the paper.
Artificial image samples generated by BigGAN. Source: Large Scale GAN Training for High Fidelity Natural Image Synthesis
You can read more about the experiment in Large Scale GAN Training for High Fidelity Natural Image Synthesis published on arXiv.
NVIDIA became another newsmaker in the area of image generation. The company created a style-based generator architecture for adversarial generation networks (GANs) which provides an easy way to control and change the appearance of a generated image. According to NVIDIA researchers, adversarial generation networks can automatically learn about particular elements of images without human supervision. These generators perceive each picture as a collection of ‘styles’ (i.e. face shape, hair color, age), and each style controls the effect at a particular scale.
Once trained, a GAN can generate new, real-looking photos of humans, animals, or any objects based on specific characteristics of provided pictures.
Generated realistic images by a style-based generator
This development can have business potential.
Deepfake videos became very popular and caused a lot of discussion in media and the public. Deepfake is a technique that allows for combining and superimposing (placing) one video or image on top of other image or video content. Simply put, it’s face-swapping. The technique may exploit generative adversarial networks (GANs).
The term appeared after the Reddit user Deepfakes shared face-swapping sexually explicit videos in November 2017 (according to some sources, in December.) The Redditor claimed that he used Keras and TensorFlow frameworks for his creations. In January 2018, Deepfakes launched FakeApp, a face-swapping application. The guy was banned from Reddit later in February.
“Because of deepfakes, video footage is no longer the most reliable information source. It’s difficult to apply this technology today. However, it will definitely evolve and become easier and faster to use in the coming years. Given that we are already living in an information space with a lot of text, image, and sound fakes, having video fakes without being able to quickly fact-check their origin will worsen a situation,” says Alexander Konduforov.
What about self-driving cars? The automotive industry players will likely concentrate on smaller automation tasks with the help of deep learning and classic ML models, thinks Dmytro Fedyukov. Self-parking is an example of the developments that can be put into production.
BMW demonstrates self-driving and self-parking features in one of its 7 Series models
The expert doubts that self-driving cars will become a commodity in 2019 or 2020.
DS and AI adoption across industries
Year after year, the number of companies and organizations applying AI and DS to improve operations is on the rise. Forty-seven percent of 2135 respondents surveyed by McKinsey&Company for the 2018 Global Survey AI Adoption by Industry and Function said they have implemented at least one AI technology. Another 30 percent reported using AI on a trial basis. By comparison, 20 percent of 2017 study participants were using AI in a key part of their business and/or at scale.
AI adoption for different business purposes across industries. Source: McKinsey&Company
According to the visualization, purposes for AI utilization differ across industries: 52 percent of retailers use AI for marketing and sales while only 13 percent of them apply it for product/service development. In contrast, 59 percent of high tech companies develop services and/or products using AI.
Forecasts for AI and DS application for industries. Sectors that adopted AI better than others and those experimenting with various AI-driven solutions will obviously proceed with AI utilization in 2019, in Alexander’s opinion. “Agriculture is also very interested in AI, especially in different types of automated field analysis and crop prediction,” the data scientist adds.
Dmytro Fedyukov predicts that pharma and medical industries will be the leaders in DS adoption using more precise research tools (i.e., for medical imaging) and large volumes of data on various groups of people. The active use of the Internet of things (IoT) is also beneficial for pharma.
According to Jérôme Louradour of Wolfram Research, the automotive industry, smartphone industry, and home robotics will obviously continue using developments in deep learning-based computer vision and audio processing. Reinforcement learning will have a great impact on robotics in the years to come as well. “There is also a lot of potential of deep learning for text analytics, with applications such as conversational agents (chatbots), text mining, and knowledge management,” the computer research scientist adds.
The demand for data science expertise keeps growing. AutoML tools are here to help non-experts harness insights from their data and facilitate decision-making. However, headhunting for data scientists that can handle more sophisticated tasks intensifies.
Companies across the UK are also looking for data scientists to join their workforce or consider such a move. MHR Analytics has surveyed 200 business decision-makers for its Data Surge research report and found out that 80 percent of midsize and large UK companies plan to become data-wise due to economic and political uncertainties.
However, 47 percent of UK business leaders admit their employees are limited in the technical skills required to drive and maintain the digital transformation of their companies. So that’s another challenge for businesses to overcome.
AI understanding is becoming as important as computer skills. “It is actually hard to find industries where data science does not have the potential for bringing huge impact,” says Booking.com principal data scientist Lucas Bernardi. “It reminds me of that article titled IT Doesn’t matter which author makes the point that IT brought competitive advantage that was quickly lost as soon as IT commoditized. I think data science is a bit like IT, and we are in the middle of that transition from competitive advantage for a few to a technology you cannot ignore if you want to run a business.”
Dmytro Fedyukov notes that data science becomes a necessary component of numerous processes: “People are more conscious of applying data science during software and product development cycles.” Talking about his team, Dmytro notes that data scientists already realize that they must put much time and effort to gain domain knowledge.
The experts are optimistic about the opportunities for artificial intelligence and data science across industries. And constantly increasing demand for data science expertise in the global job market shows there is good reason for such optimism. DS and AI have a broad scope of application, from customer support automation to product recommendations to medical image analysis.
Nevertheless, one of the challenges that will remain for researchers and developers is to ensure that state-of-the-art technologies can be successfully applied to solving real-life problems.
Original. Reposted with permission.
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