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Top 15 AI Articles You Should Read This Month - July 2020

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Usually, every month we write an article about the best and most promising AI research papers from that month. In addition to that, we list fifteen AI articles we have found amazing that month. This collection of articles should give you an overview of what happened that month in the AI industry both from technical, business and from an ethical perspective. Are you afraid that AI might take your job? Make sure you are the one who is building it.


Neural Networks Need Naps, Just Like You

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Neural networks would like a day off. Between powering facial recognition systems, filtering email spam, and even aiding in cancer research, the specialized branch of machine learning deserves a bit of rest and relaxation. Yijing Watkins, a computer scientist at Los Alamos, said her team had been studying "spiking neural networks," or systems that learn as much as our own living brains do, when they became inspired to try something a bit unusual. The robots are coming--are you prepared? Learn how to master machines and take control of your world.


Leading the Intelligent Enterprise

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Artificial intelligence (AI) and machine learning offer new ways to boost productivity, develop talent, and drive organizational change by enhancing managers' ability to make the right calls in complex situations. Augmented intelligence tools have already made an impact for many companies, but the next revolution will happen when every aspect of a business, from top to bottom, is designed with AI in mind. Call this new construct the intelligent enterprise. Like other major revolutions in management, it's poised to transform industries and organizations for decades to come. To prepare for this next phase, leaders will need to harness machine intelligence for decision-making across the business, assemble the right talent, and recognize the benefits and limitations of AI to shape organizational strategy.


Artificial Intelligence in the Pharmaceutical Industry - An Overview of Innovations

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Ayn serves as AI Analyst at Emerj - covering artificial intelligence use-cases and trends across industries. She previously held various roles at Accenture. Several factors have contributed to the advancement of AI in the pharmaceutical industry. These factors include the increase in the size of and the greater variety of types of biomedical datasets, as a result of the increased usage of electronic health records. This article intends to provide business leaders in the pharmacy space with an idea of what they can currently expect from Ai in their industry.


Can Artificial Intelligence detect the cause of diseases?

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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".


AI Model Mimics Brain Neurons to Reduce Energy Costs

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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.


Decoding Practical Problems and Business Implications of Machine Learning

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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 Algorithms For Beginners with Code Examples in Python

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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.


Council Post: How Machine Learning Is Powering A New Generation Of App Development

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Ever since the introduction of computers, the primary objective of their evolution has been to take arduous calculations off our plates. It meant automating tasks that would otherwise take us a long time. Over the past few years, the computing capabilities of mobile devices have reached a point where it's now easy to deploy machine learning natively. Artificial intelligence is a term that gets thrown around a lot, but it's machine learning that's making automation possible. When we talk about artificial intelligence, we actually refer to its branch called machine learning, which is the way computers learn and perform tasks without being explicitly programmed.


AI algorithm for detecting prostate cancer shows more than 98% sensitivity, 97% specificity in study - MedCity News

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An Israeli startup developing a digital pathology system based around artificial intelligence has published what it calls "outstanding outcomes" in a clinical validation study. Tel Aviv-based Ibex Medical Analysis said Tuesday that it had published data on Galen Prostate, its AI-based system for use by pathologists to detect and measure prostate cancer, in The Lancet Digital Health. The company called it the first and only AI-based system used by pathologists in routine clinical practice. The study took place at the University of Pittsburgh Medical Center, led by Drs. According to the data, sensitivity measured for prostate cancer was 98.46%, and specificity was 97.33%, while the operating characteristic curve was 0.991.