Time-series forecasting is one of the most widely dealt with machine learning problems ever. Time series forecasting finds crucial applications in various fields including signal communication, climate, space science, healthcare, financial and marketing industries. Deep learning models outshine in time series analysis nowadays with great performance in various public datasets. The key idea of deep learning models is to learn the inter-sample relationships to predict the future. However, intra-temporal relationships among different features within a sample are hardly dealt with.

Machine learning and deep learning has shown very good results when applied to various computer vision applications such as detection of plant diseases in agriculture (Kamilaris & Prenafeta-Boldú, 2018), fault diagnosis in industrial engineering (Wen et al., 2018), brain tumor recognition from MR images (Chen et al., 2018a), segmentation of endoscopic images for gastric cancer (Hirasawa et al., 2018), or skin lesion recognition (Li & Shen, 2018) and even autonomous vehicles (Alam et al., 2019). As our daily life increasingly depends on sitting work and the opportunities for physical exercising (in the context of COVID-19 pandemic associated restrictions and lockdowns are diminished), many people are facing various medical conditions directly related to such sedentary lifestyles. One of the frequently mentioned problems is back pain, with bad sitting posture being one of the compounding factors to this problem (Grandjean & Hünting, 1977; Sharma & Majumdar, 2009). Inadequate postures adopted by office workers are one of the most significant risk factors of work-related musculoskeletal disorders. The direct consequence may be back pain, while indirectly it has been associated with cervical disease, myopia, cardiovascular diseases and premature mortality (Cagnie et al., 2006).

Note: In our second tutorial on neural networks, we dive in-depth into the limitations and advantages of using neural networks. We show how to implement neural nets with hidden layers and how these lead to a higher accuracy rate on our predictions, along with implementation samples in Python on Google Colab. Neural networks form the base of deep learning, which is a subfield of machine learning, where the structure of the human brain inspires the algorithms. Neural networks take input data, train themselves to recognize patterns found in the data, and then predict the output for a new set of similar data. Therefore, a neural network can be thought of as the functional unit of deep learning, which mimics the behavior of the human brain to solve complex data-driven problems.

Doctored videos or deepfakes have been one of the key weapons used in propaganda battles for quite some time now. Donald Trump taunting Belgium for remaining in the Paris climate agreement, David Beckham speaking fluently in nine languages, Mao Zedong singing'I will survive' or Jeff Bezos and Elon Musk in a pilot episode of Star Trek… all these videos have gone viral despite being fake, or because they were deepfakes. Last year, Marco Rubio, the Republican senator from Florida, said deepfakes are as potent as nuclear weapons in waging wars in a democracy. "In the old days, if you wanted to threaten the United States, you needed 10 aircraft carriers, and nuclear weapons, and long-range missiles. Today, you just need access to our Internet system, to our banking system, to our electrical grid and infrastructure, and increasingly, all you need is the ability to produce a very realistic fake video that could undermine our elections, that could throw our country into tremendous crisis internally and weaken us deeply," Forbes quoted him as saying.

This story has begun with JukeBox, a music model developed by OpenAI and trained on more than a million songs and music pieces. It makes use of transformers (like GPT-3), influencing the music piece coherency, with inner logic, specific style, and full-audio generation. Normally, I use music written by JukeBox as a soundtrack for my movies (like Empty Room or Bloomsday MMXX). Unique sounds, never played before and also free to use in your film (better than inflationary applied stock music), always appealed to a movie maker in me. But this one was different. The soundtrack itself told a story in an unknown language, an unusual narrative generated by machines but touching the human heart.

Deep Learning isn't deep enough unless it copies from the brain interview with Jeff Hawkins, author of'A Thousand Brains: A New Theory of Intelligence' IEEE Spectrum Could an AI really understand why humans do the things they do if it doesn't understand the fear of death? Hawkins: "I've had cats, and I don't think my cats really understood my emotional states. And I didn't really understand theirs. But we still got along quite well."

Each year scientists from around the world publish thousands of research papers in AI but only a few of them reach wide audiences and make a global impact in the world. Below are the top-10 most impactful research papers published in top AI conferences during the last 5 years. The ranking is based on the number of citations and includes major AI conferences and journals. Explaining and Harnessing Adversarial Examples, Goodfellow et al., ICLR 2015, cited by 6995 One of the first fast ways to generate adversarial examples for neural networks and introduction of adversarial training as a regularization technique. Impact: Exposed an interesting phenomenon where performance of any accurate machine learning model can be significantly reduced by an attacker applying a tiny modification to the input.

Before he joined the University of Texas at Arlington as an Assistant Professor in the Department of Computer Science and Engineering and founded the Robotic Vision Laboratory there, William Beksi interned at iRobot, the world's largest producer of consumer robots (mainly through its Roomba robotic vacuum). To navigate built environments, robots must be able to sense and make decisions about how to interact with their locale. Researchers at the company were interested in using machine and deep learning to train their robots to learn about objects, but doing so requires a large dataset of images. While there are millions of photos and videos of rooms, none were shot from the vantage point of a robotic vacuum. Efforts to train using images with human-centric perspectives failed.

Soft-bodied robots are an extremely important tool in the wider field of robotics, as traditional and rigid-bodied robots are not able to complete the same types of tasks. The former interacts with humans more safely, and they can do things like fit into tight spaces.

"Weak AI" is Likely to Never Become "Strong AI", So What is its Greatest Value for us? Abstract AI has surpassed humans across a variety of tasks such as image classification, playing games (e.g., go, "Starcraft" and poker), and protein structure prediction. However, at the same time, AI is also bearing serious controversies. Many researchers argue that little substantial progress has been made for AI in recent decades. In this paper, the author (1) explains why controversies about AI exist; (2) discriminates two paradigms of AI research, termed "weak AI" and "strong AI" (a.k.a.