"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
President Donald Trump threatened Monday to pull the Republican National Convention out of North Carolina if the state's Democratic governor doesn't immediately sign off on allowing a full-capacity gathering in August despite the ongoing COVID-19 pandemic. Trump's tweets about the RNC, planned for Charlotte, come just two days after North Carolina recorded its largest daily increase in positive cases yet. On Friday, Gov. Roy Cooper moved the state into a second phase of gradual reopening by loosening restrictions on hair salons, barbers and restaurants. But he said the state must continue to closely watch virus trends and has ordered indoor entertainment venues, gyms and bars to remain closed for several more weeks. "Unfortunately, Democrat Governor, @RoyCooperNC is still in Shutdown mood & unable to guarantee that by August we will be allowed… full attendance in the Arena," Trump tweeted Monday.
Even though you may not realize it, machine learning-powered matchmaking is present everywhere in our daily lives, from the type of content shown on our Facebook news feeds to the suggested TV shows that come up on Netflix, and even to the matches suggested on dating sites/apps like Match.com and Tinder. As machine learning continues to advance, it will start to make its way to the hiring process, driving efficiencies in connecting employers and candidates, especially for technical jobs. Analyzing large amounts of data on candidates will become increasingly important during the hiring process for many companies. Today, matching algorithms use strings and keywords in resumes to filter candidates. This enables companies to get more accurate results, quicker, during the hiring process.
In 10 years, the circular economy will be the only economy, replacing wasteful linear economies, predicts Gartner. According to Gartner, circular economic business models encourage continuous reuse of materials to minimise waste and the demand for additional natural resource consumption. "The circular economy creates an ecosystem of materials," notes Sarah Watt, senior director analyst at Gartner. "What was previously viewed as waste now has value. However those ecosystems are complex, and include many interdependencies and feedback loops."
For kinase inhibitors, X-ray crystallography has revealed different types of binding modes. Currently, more than 2000 kinase inhibitors with known binding modes are available, which makes it possible to derive and test machine learning models for the prediction of inhibitors with different binding modes. We have addressed this prediction task to evaluate and compare the information content of distinct molecular representations including protein–ligand interaction fingerprints (IFPs) and compound structure-based structural fingerprints (i.e., atom environment/fragment fingerprints). IFPs were designed to capture binding mode-specific interaction patterns at different resolution levels. Accurate predictions of kinase inhibitor binding modes were achieved with random forests using both representations.
This lecture discusses how decision trees can be used to represent predictor functions. Variations of the basic decision tree model provide some of the most powerful machine learning methods curren... Alexander Jung uploaded a video 1 week ago Classification Methods - Duration: 46 minutes. Our focus is on linear regression methods which can be expanded by feature constructions. Guest lecture of Prof. Minna Huotilainen on learning processes in human brains. Alexander Jung subscribed to a channel 3 weeks ago Playing For Change - Channel PFC is a movement created to inspire and connect the world through music. The idea for this project came from a common belief that music has the power to break down boundaries and overcome distances SubscribeSubscribedUnsubscribe1.9M This video explains how network Lasso can be used to learn localized linear models that allow "personalized" predictions for individual data points within a network.
Sickle cell disease (SCD) is a major public health priority throughout much of the world, affecting millions of people. In many regions, particularly those in resource-limited settings, SCD is not consistently diagnosed. In Africa, where the majority of SCD patients reside, more than 50% of the 0.2–0.3 million children born with SCD each year will die from it; many of these deaths are in fact preventable with correct diagnosis and treatment. Here, we present a deep learning framework which can perform automatic screening of sickle cells in blood smears using a smartphone microscope. This framework uses two distinct, complementary deep neural networks.
AI is taking off in all areas of business and in our daily lives – from improving agriculture and predicting where forest fires might erupt to determining who is likely to return to a hospital after discharge. With advanced GPUs that can crunch more data faster and growing demand from companies looking to increase competitive advantage, machine learning and other forms of AI are expected to become more pervasive. Today, many companies are relying on smart apps to provide the insight needed to make decisions that can affect people's lives, such as who qualifies for a mortgage or who will be insured. Because of this responsibility, it's more important than ever that data professionals don't inadvertently automate any biases into the AI algorithm because of the data they use or don't use, and how they use it. While AI should be regulated to ensure the fair and ethical use of data, particularly as it impacts decision-making and people's lives, unfortunately, we still have a long way to go before this happens.
The machine learning approach works well when these new cases are similar to the examples in the training data. The ability of machine learning algorithms to identify subtle patterns in the training data can allow it to make a faster and possibly better predictions than a human. However, if the new cases are radically different from the training data, and especially if we are playing by a whole new rulebook, then the patterns in the training data will no longer be a useful basis for prediction. Some algorithms are designed to continuously add new training data and therefore update the algorithm, but with large changes this gradual updating will not be sufficient. To learn completely new rules, machine learning algorithms need large amounts of new data.
Artificial Intelligence is everywhere, opportunities are in abundance for cognitive enterprises. What do we mean by cognitive enterprises? Millions of ideas and think pieces are waiting to grow luxuriantly and cognitive AI technologies will play a bigger role in turning your ideas into a live piece of work. It is expected that AI will bring simplicity to complex business issues and deliver more useful, engaging, intuitive, and profitable solutions, and this is what we say a cognitive approach for enterprises. According to a report published by IDC a market research firm states that global spending on cognitive AI systems will reach $57.6 billion by 2021.
The Austrian Research Promotion Agency (FFG) has officially announced to support the development of an AI (Artificial intelligence) forecasting project with MarineXchange (MXP), which provides enterprise software solutions to the cruise industry. "Accurate supply chain forecasting is a huge challenge for the cruise industry," MXP said, in a press release ."Because Consumption patterns depend on guest profiles, seasonal items, substitutions and menu cycles. Vendor performance, freight and currency exchange rates need to be considered. The overall CO2 impact, waste reduction and fair trade are additional factors. To aid cruise companies in this complex decision-making process, more accurate forecasts are needed."