A crash course in neural networks for beginners You know the difference between a multilayer perceptron and a convolutional neural network. You will be able to program your own neural network in python. Description What is machine learning / ai? How to learn machine learning in practice? "From my personal experience I can tell you that companies will actively searching for you if you aquire some skills in the data science field. Diving into this topic can not only immensly improve your career opportunities but also your job satisfaction!"

GPT-3, an advanced language-processing artificial intelligence algorithm developed by OpenAI, is really good at what it does -- churning out humanlike text. But Yann LeCun, the Chief AI Scientist at Facebook who's been called a "godfather of AI," trashed the algorithm in a Tuesday Facebook post, writing that "people have completely unrealistic expectations about what large-scale language models such as GPT-3 can do." LeCun cites a recent experiment by the medical AI firm NABLA, which found that GPT-3 is woefully inadequate for use in a healthcare setting because writing coherent sentences isn't the same as being able to reason or understand what it's saying. "It's entertaining, and perhaps mildly useful as a creative help," LeCun wrote. "But trying to build intelligent machines by scaling up language models is like [using] high-altitude airplanes to go to the Moon. You might beat altitude records, but going to the Moon will require a completely different approach."

Computer vision is an important Artificial Intelligence application that will transform many industries and many business processes. Also known as machine vision technology, this data-driven innovation allows machines to interpret the world visually. This visual data can be in the form of photos, videos, or feed from infrared and thermal cameras too. As a way of imitating the human visual system, the researchers in the field of computer vision intend to develop machines that can automate tasks that require visual cognition. One of the most commonly known examples of this technology is facial recognition.

In October 2020 the US House Antitrust Subcommittee, chaired by Congressman David Cicilline, published its report on competition in digital markets. It conducted a full review of the market from top to bottom, focusing on the dominance of the giants in the industry: Facebook, Amazon, Apple and Google. The report zeroes in on their business practices, and how these could potentially amount to monopolies. They found that each platform had become, in one way or another, in direct and singular control of channels of mass distribution. They are no longer disruptive and innovative start-ups, but now resemble business monoliths akin to the oil barons and railroad tycoons of the past, controlling their respective industries, absorbing or removing competitors with ease.

Open Deep Learning and Reinforcement Learning lectures from top Universities like Stanford University, MIT, UC Berkeley. This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and panel of industry sponsors. Experience in Python is helpful but not necessary.

In the winter of 2011, Daniel Yamins, a postdoctoral researcher in computational neuroscience at the Massachusetts Institute of Technology, would at times toil past midnight on his machine vision project. He was painstakingly designing a system that could recognize objects in pictures, regardless of variations in size, position and other properties -- something that humans do with ease. The system was a deep neural network, a type of computational device inspired by the neurological wiring of living brains. "I remember very distinctly the time when we found a neural network that actually solved the task," he said. It was 2 a.m., a tad too early to wake up his adviser, James DiCarlo, or other colleagues, so an excited Yamins took a walk in the cold Cambridge air. "I was really pumped," he said. It would have counted as a noteworthy accomplishment in artificial intelligence alone, one of many that would make neural networks the darlings of AI technology over the next few years.

Domain adaptation (DA) arises as an important problem in statistical machine learning when the source data used to train a model is different from the target data used to test the model. Recent advances in DA have mainly been application-driven and have largely relied on the idea of a common subspace for source and target data. To understand the empirical successes and failures of DA methods, we propose a theoretical framework via structural causal models that enables analysis and comparison of the prediction performance of DA methods. This framework also allows us to itemize the assumptions needed for the DA methods to have a low target error. Additionally, with insights from our theory, we propose a new DA method called CIRM that outperforms existing DA methods when both the covariates and label distributions are perturbed in the target data. We complement the theoretical analysis with extensive simulations to show the necessity of the devised assumptions. Reproducible synthetic and real data experiments are also provided to illustrate the strengths and weaknesses of DA methods when parts of the assumptions of our theory are violated.

Amortized approaches to clustering have recently received renewed attention thanks to novel objective functions that exploit the expressiveness of deep learning models. In this work we revisit a recent proposal for fast amortized probabilistic clustering, the Clusterwise Clustering Process (CCP), which yields samples from the posterior distribution of cluster labels for sets of arbitrary size using only O(K) forward network evaluations, where K is an arbitrary number of clusters. While adequate in simple datasets, we show that the model can severely underfit complex datasets, and hypothesize that this limitation can be traced back to the implicit assumption that the probability of a point joining a cluster is equally sensitive to all the points available to join the same cluster. We propose an improved model, the Attentive Clustering Process (ACP), that selectively pays more attention to relevant points while preserving the invariance properties of the generative model. We illustrate the advantages of the new model in applications to spike-sorting in multi-electrode arrays and community discovery in networks. The latter case combines the ACP model with graph convolutional networks, and to our knowledge is the first deep learning model that handles an arbitrary number of communities.

Knowledge graph reasoning is the fundamental component to support machine learning applications such as information extraction, information retrieval and recommendation. Since knowledge graph can be viewed as the discrete symbolic representations of knowledge, reasoning on knowledge graphs can naturally leverage the symbolic techniques. However, symbolic reasoning is intolerant of the ambiguous and noisy data. On the contrary, the recent advances of deep learning promote neural reasoning on knowledge graphs, which is robust to the ambiguous and noisy data, but lacks interpretability compared to symbolic reasoning. Considering the advantages and disadvantages of both methodologies, recent efforts have been made on combining the two reasoning methods. In this survey, we take a thorough look at the development of the symbolic reasoning, neural reasoning and the neural-symbolic reasoning on knowledge graphs. We survey two specific reasoning tasks, knowledge graph completion and question answering on knowledge graphs, and explain them in a unified reasoning framework. We also briefly discuss the future directions for knowledge graph reasoning.

As a result of the worldwide transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) has evolved into an unprecedented pandemic. Currently, with unavailable pharmaceutical treatments and vaccines, this novel coronavirus results in a great impact on public health, human society, and global economy, which is likely to last for many years. One of the lessons learned from the COVID-19 pandemic is that a long-term system with non-pharmaceutical interventions for preventing and controlling new infectious diseases is desirable to be implemented. Internet of things (IoT) platform is preferred to be utilized to achieve this goal, due to its ubiquitous sensing ability and seamless connectivity. IoT technology is changing our lives through smart healthcare, smart home, and smart city, which aims to build a more convenient and intelligent community. This paper presents how the IoT could be incorporated into the epidemic prevention and control system. Specifically, we demonstrate a potential fog-cloud combined IoT platform that can be used in the systematic and intelligent COVID-19 prevention and control, which involves five interventions including COVID-19 Symptom Diagnosis, Quarantine Monitoring, Contact Tracing & Social Distancing, COVID-19 Outbreak Forecasting, and SARS-CoV-2 Mutation Tracking. We investigate and review the state-of-the-art literatures of these five interventions to present the capabilities of IoT in countering against the current COVID-19 pandemic or future infectious disease epidemics.