"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.
The technological advancements in the global Healthcare industry are hurtling at light speed. As the medical industry is undergoing immense changes, Healthcare OEMs look forward to the growing technological trends to improve all aspects of patient care. Today, Artificial Intelligence (AI) play significant roles in the evolution of the healthcare industry, so much that algorithms can now predict and detect the root cause of a certain disease, making an accurate and timely diagnosis. For example, AI can detect the underlying cause of cancer, which can eventually help pharmaceutical scientists develop new drugs accordingly. In one recent study, published by Healthcare IT News, "Google and medical partners including Northwestern University have unveiled a new AI-based tool that can create a better model of a patient's lung from the CT scan images. This 3-D image gives better predictions about the malignancy of tumors and incorporates learning from previous scans, enabling the AI to help clinicians in spotting lung cancer in earlier stages when it is vastly more treatable".
Cutting-edge medical research is the talk of the town at the moment and this innovative discovery platform are growing their presents within the medical research field. AI and Machine Learning is at the full front of what they do and they're looking for an experienced (academic or commercial) Machine Learning Engineer to support their continued growth. You'll be working on architecting the AI powered GCP discovery platform, taking on big problems and doing lots of research! This vacancy will be closing application on 15th August 2020. If you have any questions or fancy a chat about the opportunity feel free to give George Bone a call or apply for the advert and George will be in contact.
Improvements in cloud technologies and processing power have provided a solid foundation for mainstream adoption of machine learning (ML). With the ability to analyze massive amounts of data to derive meaningful insights, ML can give business leaders new ways to innovate, create new revenue streams, improve operational efficiencies, and help all employees make faster, more informed decisions. In IDG's 2019 Digital Business Study, 78% of IT and business leaders said their organizations are considering or have already deployed machine learning technologies as part of their digital business strategy. "We've seen it day in and day out with customers we support, and organizations in general, that are benefiting by leveraging machine learning," says Sri Elaprolu, senior leader, Amazon Machine Learning Solutions Lab, a team of data scientists and machine learning experts that helps Amazon Web Services (AWS) customers successfully adopt ML. Amazon is a prime example of how ML can impact every area of the business.
Time series forecasting is something of a dark horse in the field of data science: It is one of the most applied data science techniques in business, used extensively in finance, in supply chain management and in production and inventory planning, and it has a well established theoretical grounding in statistics and dynamic systems theory. Yet it retains something of an outsider status compared to more recent and popular machine learning topics such as image recognition and natural language processing, and it gets little or no treatment at all in introductory courses to data science and machine learning. My original training is in neural networks and other machine learning methods, but I gravitated towards time series methods after my career led me to the role of demand forecasting specialist. In recent weeks, as part of my team's effort to expand beyond traditional time series forecasting capabilities and into a borader ML based approach to our business, I found myself having several discussions with experienced ML engineers, who were very good at ML in general, but didn't have much experience with times series methods. I realized from those discussions that there were several things specific to time series forecasting that the forecasting community takes for granted but are very surprising to other ML practioners and data scientists, especially when compared to the way standard ML problems are approached.
In partnership with the Harvard Global Health Institute, Google today released the COVID-19 Public Forecasts, a set of models that provide projections of COVID-19 cases, deaths, ICU utilization, ventilator availability, and other metrics over the next 14 days for U.S. counties and states. The models are trained on public data such as those from Johns Hopkins University, Descartes Labs, and the United States Census Bureau, and Google says they'll continue to be updated with guidance from its collaborators at Harvard. The COVID-19 Public Forecasts are intended to serve as a resource for first responders in health care, the public sector, and other affected organizations preparing for what lies ahead, Google says. They allow for targeted testing and public health interventions on a county-by-county basis, in theory enhancing the ability of those who use them to respond to the rapidly evolving COVID-19 pandemic. For example, health care providers could incorporate the forecasted number of cases as a datapoint in resource planning for PPE, staffing, and scheduling.
The state said it has no formal reporting process for tracking coronavirus outbreaks that have already cropped up in summer school programs, leaving teachers unions wondering how health officials plan to prevent outbreaks considered "inevitable" in the fall. "We are not formally tracking them, but we are trying to notice them as they pop up," said Department of Elementary and Secondary Education spokeswoman Jacqueline Reis. "There is no formal reporting process for schools." Reis said the DESE is still finalizing its guidance as schools shore up their plans for remote, in-person or hybrid learning once classes resume in September. "It's absurd and it's stunning but its also not a surprise," said Merrie Najimy, who leads the Massachusetts Teachers Association.
Deployed for AI, e-prop would require only 20 watts, approximately one-millionth the energy a supercomputer uses. Artificial intelligence models continue to grow in sophistication and complexity, adding to the need for more data, computation, and energy. To help combat increasing energy costs, researchers at TU Graz's Institute of Theoretical Computer Science have developed a new algorithm, called e-propagation (e-prop for short). E-prop mimics how neurons send electrical impulses to other neurons in our brain, which massively reduces the amount of energy human brains use, in comparison to machine learning. Deployed for AI, e-prop would require only 20 watts, approximately one-millionth the energy a supercomputer uses.
How to Build a Machine Learning Model A Visual Guide to Learning Data Science Jul 25 · 13 min read Learning data science may seem intimidating but it doesn't have to be that way. Let's make learning data science fun and easy. So the challenge is how do we exactly make learning data science both fun and easy? Cartoons are fun and since "a picture is worth a thousand words", so why not make a cartoon about data science? With that goal in mind, I've set out to doodle on my iPad the elements that are required for building a machine learning model.
Machine learning typically is used to solve a host of diverse problems within an organization, extracting predictive knowledge from both structured and unstructured data and using them to deliver value. The technology has already made its way into different aspects of a business ranging from finding data patterns to detect anomalies and making recommendations. Machine learning helps organizations gain a competitive edge by processing a voluminous amount of data and applying complex computations. With machine learning, companies can develop better applications according to their business requirements. This technology is mainly designed to make everything programmatic.
Machine learning (ML) is rapidly changing the world, from diverse types of applications and research pursued in industry and academia. Machine learning is affecting every part of our daily lives. From voice assistants using NLP and machine learning to make appointments, check our calendar and play music, to programmatic advertisements -- that are so accurate that they can predict what we will need before we even think of it. More often than not, the complexity of the scientific field of machine learning can be overwhelming, making keeping up with "what is important" a very challenging task. However, to make sure that we provide a learning path to those who seek to learn machine learning, but are new to these concepts.