One important data type which includes time series, digital signals and any sequential observations is still mainly processed with rather standard mathematical and algorithmic routines. In this talk, we will review, what are the main sources of time series in the world, what are the "basic" algorithms and how exactly they might be improved and replaced with different neural network architectures. Apart from the models' details, we will also study the typical tasks that have to be solved while working with time series: classification, prediction, anomaly detection, simulation and others and exactly deep learning can be leveraged to solve them on the state-of-the-art level. Some previous experience with time series/signal processing is useful for getting the most out of this session, but not required. Alex Honchar is developing production-ready AI solutions for small and medium businesses for the last 5 years, giving public speeches in Europe and blogging about ML and AI recent advances.

In the months since the novel coronavirus emerged in Wuhan, China, last December, almost 2,000 research papers have been published on the health effects of the new virus, possible treatments, and the dynamics of the resulting pandemic. This outpouring of research is a testament to the speed with which science can tackle big problems. But it also presents a headache for anyone wanting to stay up to date with the literature, or hoping to mine it for insight about the virus, its behavior, or possible treatments. Naturally, some believe that artificial intelligence may help. Monday, the White House announced a project in collaboration with tech companies and academics to make a huge amount of coronavirus research accessible to AI researchers and their algorithms for the first time.

Artificial Intelligence, or AI, is expanding rapidly at the moment. With so many changes happening in the space, it can feel impossible to keep up. Let's take a look at AI trends in March 2020, so you can get a handle on everything. The role of #ArtificialIntelligence in #Healthcare is intriguing, would be an understatement. IT budgets are falling, but large data volumes are still coming from the sector.

The powerful technology, developed by a team led by engineering researchers at the University of Waterloo, uses digital images of tissue samples to match new cases of suspected cancer with previously diagnosed cases in a database. In tests using the largest publicly available archive in the world – comprised of about 30,000 digitized slides from almost 11,000 patients – the technology achieved up to 100-per-cent accuracy for 32 forms of cancer in 25 organs and body parts. "AI can help us tap into our medical wisdom, which at the moment is just sitting in archives," said Hamid Tizhoosh, director of the Laboratory for Knowledge Inference in Medical Image Analysis (KIMIA Lab) at Waterloo. "When you use AI like this, its performance is astounding." The system utilizes AI to search digital images of biopsies from confirmed cancer cases for those most similar to a new digital image in an undiagnosed case.

Investment in artificial intelligence (AI) has been a priority for governments and businesses across the world. In Southeast Asia, the emphasis on AI-leadership is more pronounced with China's Premier incentivizing research efforts in the technology, and technology giants such as Alibaba and Tencent making massive strides in their quest for AI excellence. Research cited by Standards Australia, a standard setting non-profit, found that since 2017, 14 of the world's most advanced economies have announced over US$55 billion (AUD86 billion) in focused AI programs and activities. Australia, despite its government's efforts to help take the its agencies and businesses on a digital journey, hasn't made significant progress with AI. Standards Australia aims to change that.

In all of the emerging of these computers, let is not forget the Big Daddy of computers. Microsoft act also so has its messaging platforms like Skype where customers can possibly interact for businesses. Our Microsoft bot development experience will bring you the technical expertise it takes to build a robust and interactive Microsoft chatbot. There are certain industries which require only a standard set of questions and specific answers that will help the process get more efficient and the customer's inch closer to the conversion. We specialize in creating chatbots that are industry-specific and customized to suit your prospects and customers.

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This is Part I of a two-part series on "How to assess Artificial Intelligence (AI) startups". Check out Part II here. During the past few years, we've witnessed a massive growth in the number of so-called Artificial Intelligence (AI) startups. Both sides of the table, investors and entrepreneurs, have jumped into the frenzy with intent. This new trend responds to significant advancements in AI research.

So-called'soft skills' would seem to be the most important foundation to build upon. These include things like the ability to communicate and work well with others, solve problems, and think outside of the box. Most of the universities and private colleges in Australia are now offering short courses, online or on campus, to develop these critical skills. Courses include critical thinking and problem solving, negotiation and interpersonal skills, and effective communication. We can't predict what all the job roles will be in the 21st century, but we do know that human skills will be in demand.

Proteins are the building blocks of life, and consequently, scientists have long studied how they can improve proteins and design completely new proteins that perform new functions and processes. Traditionally, new proteins are created by either mimicking existing proteins or manually editing the amino acids that make up the proteins. This process, however, is time-consuming, and it is difficult to predict the impact of changing any one of the amino acid components of a given protein. In this week's APL Bioengineering, researchers in the United States and Taiwan explore how to create new proteins by using machine learning to translate protein structures into musical scores, presenting an unusual way to translate physics concepts across disparate domains. Each of the 20 amino acids that make up proteins has a unique vibrational frequency.