President Trump reacts to the media and Big Tech's role in politics in a'Sunday Morning Futures' exclusive. The year 2020 proved to be a pivotal one in tech, as companies provided essential services during the coronavirus pandemic and unveiled 5G telecom technology while facing unprecedented antitrust scrutiny and accusations of censorship amid an intense election and social justice movement. "I think sometimes we hear that … U.S. innovation is slowing down, and I think the last year has shown that that's not really the case," Neil Chilson, senior research fellow for tech and innovation at the Charles Koch Institute, told Fox News. Chilson gave examples of the country's rapid COVID-19 vaccine development, SpaceX's astronaut launch in May and autonomous driving company Waymo's recent announcement that its self-driving cars will be completely autonomous in trials in Phoenix. "I'm pretty excited about the future. I think 2020 shows that the U.S. is still the world leader in tech and innovation, and we should continue to maintain our cultural appreciation for innovation and a regulatory environment that enables it," he said.

This year saw automation become a mainstream tech trend, with the COVID pandemic causing nation-wide shutdowns. As employees were isolated in their houses, IT companies switched to the work from home model. However, the same could not be replicated in the manufacturing and other secondary industries. This is why, business leaders had to adopt automation practices to sustain their businesses and enable smoother operation of organizational practices (including IT). This trend will not end here.

London (CNN)A fake Queen Elizabeth danced across TV screens on Christmas as part of a "deepfake" speech aired by a British broadcaster. The real British monarch traditionally delivers a Christmas Day speech aired around the world. But her speech on Friday at 3 p.m. was followed by a digitally-created fake of the Queen, aired on Channel 4 and voiced by an actor, warning viewers to question "whether what we see and hear is always what it seems." Channel 4 said the video was created as a "stark warning" about technology and the proliferation of fake news. The broadcaster said the video was supposed to offer "a stark warning about the advanced technology that is enabling the proliferation of misinformation and fake news in a digital age."

Artificial Intelligence (AI) is not just a buzzword, but a crucial part of the technology landscape. AI is changing every industry and business function, which results in increased interest in its applications, subdomains and related fields. This makes AI companies the top leaders driving the technology swift. AI helps us to optimise and automate crucial business processes, gather essential data and transform the world, one step at a time. From Google and Amazon to Apple and Microsoft, every major tech company is dedicating resources to breakthroughs in artificial intelligence. As big enterprises are busy acquiring or merging with other emerging inventions, small AI companies are also working hard to develop their own intelligent technology and services. By leveraging artificial intelligence, organizations get an innovative edge in the digital age. AI consults are also working to provide companies with expertise that can help them grow. In this digital era, AI is also a significant place for investment. AI companies are constantly developing the latest products to provide the simplest solutions. Henceforth, Analytics Insight brings you the list of top 100 AI companies that are leading the technology drive towards a better tomorrow. AEye develops advanced vision hardware, software, and algorithms that act as the eyes and visual cortex of autonomous vehicles. AEye is an artificial perception pioneer and creator of iDAR, a new form of intelligent data collection that acts as the eyes and visual cortex of autonomous vehicles. Since its demonstration of its solid state LiDAR scanner in 2013, AEye has pioneered breakthroughs in intelligent sensing. Their mission was to acquire the most information with the fewest ones and zeros. This would allow AEye to drive the automotive industry into the next realm of autonomy. Algorithmia invented the AI Layer.

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In this article, two types of methods from different perspectives based on spectral normalization are described for ensuring the stability of the system controlled by a neural network. The first one is that the L2 gain of the feedback system is bounded less than 1 to satisfy the stability condition derived from the small-gain theorem. While explicitly including the stability condition, the first method may provide an insufficient performance on the neural network controller due to its strict stability condition. To overcome this difficulty, the second one is proposed, which improves the performance while ensuring the local stability with a larger region of attraction. In the second method, the stability is ensured by solving linear matrix inequalities after training the neural network controller. The spectral normalization proposed in this article improves the feasibility of the a-posteriori stability test by constructing tighter local sectors. The numerical experiments show that the second method provides enough performance compared with the first one while ensuring enough stability compared with the existing reinforcement learning algorithms.

Weight decay (WD) is a traditional regularization technique in deep learning, but despite its ubiquity, its behavior is still an area of active research. Golatkar et al. have recently shown that WD only matters at the start of the training in computer vision, upending traditional wisdom. Loshchilov et al. show that for adaptive optimizers, manually decaying weights can outperform adding an $l_2$ penalty to the loss. This technique has become increasingly popular and is referred to as decoupled WD. The goal of this paper is to investigate these two recent empirical observations. We demonstrate that by applying WD only at the start, the network norm stays small throughout training. This has a regularizing effect as the effective gradient updates become larger. However, traditional generalizations metrics fail to capture this effect of WD, and we show how a simple scale-invariant metric can. We also show how the growth of network weights is heavily influenced by the dataset and its generalization properties. For decoupled WD, we perform experiments in NLP and RL where adaptive optimizers are the norm. We demonstrate that the primary issue that decoupled WD alleviates is the mixing of gradients from the objective function and the $l_2$ penalty in the buffers of Adam (which stores the estimates of the first-order moment). Adaptivity itself is not problematic and decoupled WD ensures that the gradients from the $l_2$ term cannot "drown out" the true objective, facilitating easier hyperparameter tuning.

Data beamed back to Earth from NASA's InSight lander suggests Mars' crust is composed of three cake-like layers. Anchored near Mars' equator, the robotic lander's super-sensitive seismometer, known as SEIS, has recorded hundreds of'marsquakes' in the past two years. Each quake emits two sets of seismic waves and analyzing the differences in how those waves move has allowed researchers to begin calculating the size and composition of the planet's crust, mantle and core. 'We have enough data to start answering some of these big questions,' Jet Propulsion Laboratory scientist Bruce Banerdt told Nature. Launched in 2018, the InSight mission marks the first time scientists have peered inside a planet other than Earth.

Prediction #6: The U.S. federal government will adopt a more proactive policy approach to AI in 2021 ... [ ] under President Biden. Below are 10 bold predictions about what will unfold in the world of artificial intelligence in 2021, from academic research to startups to capital markets to regulation. To keep ourselves honest, we will revisit these predictions in December 2021 to grade how we did. Autonomous vehicle developers like Waymo and Cruise have massive ongoing cash needs. Public market investors are thirsty for IPOs.

Given a natural language statement, how to verify whether it is supported, refuted, or unknown according to a large-scale knowledge source like Wikipedia? Existing neural-network-based methods often regard a sentence as a whole. While we argue that it is beneficial to decompose a statement into multiple verifiable logical points. In this paper, we propose LOREN, a novel approach for fact verification that integrates both Logic guided Reasoning and Neural inference. The key insight of LOREN is that it decomposes a statement into multiple reasoning units around the central phrases. Instead of directly validating a single reasoning unit, LOREN turns it into a question-answering task and calculates the confidence of every single hypothesis using neural networks in the embedding space. They are aggregated to make a final prediction using a neural joint reasoner guided by a set of three-valued logic rules. LOREN enjoys the additional merit of interpretability -- it is easy to explain how it reaches certain results with intermediate results and why it makes mistakes. We evaluate LOREN on FEVER, a public benchmark for fact verification. Experiments show that our proposed LOREN outperforms other previously published methods and achieves 73.43% of the FEVER score.