Machine learning has several applications in diverse fields, ranging from healthcare to natural language processing. Dr. Ragothanam Yennamalli, a computational biologist and Kolabtree freelancer, examines the applications of AI and machine learning in biology. Machine Learning and Artificial Intelligence -- these technologies have stormed the world and have changed the way we work and live. Advances in these areas have led to many either praising it or decrying it. However, for a computational person like me, they are not new words. AI and ML, as they're popularly called, have several applications and benefits across a wide range of industries.

AI terms are often used interchangeably, but they are not the same. Artificial intelligence, machine learning and deep learning are popular terms in enterprise IT, and sometimes used interchangeably, particularly when companies are trying to market their products. The terms, however, are not synonymous -- there are important distinctions. Here is a primer on artificial intelligence vs. machine learning vs. deep learning. The term AI has been around since the 1950s.

Artificial intelligence has become a focus of certain ethical concerns, but it also has some major sustainability issues. Last June, researchers at the University of Massachusetts at Amherst released a startling report estimating that the amount of power required for training and searching a certain neural network architecture involves the emissions of roughly 626,000 pounds of carbon dioxide. This issue gets even more severe in the model deployment phase, where deep neural networks need to be deployed on diverse hardware platforms, each with different properties and computational resources. MIT researchers have developed a new automated AI system for training and running certain neural networks. Results indicate that, by improving the computational efficiency of the system in some key ways, the system can cut down the pounds of carbon emissions involved -- in some cases, down to low triple digits.

Artificial intelligence has become a focus of certain ethical concerns, but it also has some major sustainability issues. Last June, researchers at the University of Massachusetts at Amherst released a startling report estimating that the amount of power required for training and searching a certain neural network architecture involves the emissions of roughly 626,000 pounds of carbon dioxide. This issue gets even more severe in the model deployment phase, where deep neural networks need to be deployed on diverse hardware platforms, each with different properties and computational resources. MIT researchers have developed a new automated AI system for training and running certain neural networks. Results indicate that, by improving the computational efficiency of the system in some key ways, the system can cut down the pounds of carbon emissions involved--in some cases, down to low triple digits.

Our comprehensive guide to how chatbots will develop in 2020 and beyond. Artificial intelligence is the hottest talking point for business users looking to improve their efficiency, deliver new ideas and take the next steps in the transition to a digital enterprise. AI and chatbots are helping democratise business, empower startups and help build new partnerships, something that every organisation needs to prepare for. "Every business is a technology business" was one of the mantras of the decade just concluded. Every company across every vertical and market started working and communicating with smartphones, using cloud services to open up their data and adopted as-a-service solutions to reduce the cost of doing business and broaden their business base and the opportunities for workers. Ten years ago, specialists were needed to manage databases and build websites. Now anyone with a plan can build an entire company out of off-the-shelf parts, sell across the world without leaving their desk. They can pick advice from a huge range of sources to grow the business and partner with a massive range of organisations to deliver whatever they sell. Now as we move into the 2020s, enterprises and startups alike are taking the next step, adopting AI and bringing smart services into their organisations. It has already started with chatbots and analytics tools, but is already expanding to business-enabling technology, using a mix of machine learning, deep learning, computer vision, natural language processing, machine reasoning (MR), and deep or strong AI. Companies will continue to deploy AI for intelligent robotic process automation, computer vision tasks, and machine learning applications.

The term AI has been around since the 1950s. In short, it depicts our struggle to build machines that can challenge what made humans the dominant lifeform on the planet: our intelligence. However, defining "intelligence" has turned out to be rather tricky, because what we perceive as intelligent changes over time. Early AIs were rule-based computer programs that could solve somewhat complex problems. Instead of hardcoding every decision the software was supposed to make, the program was divided into a knowledge base and an inference engine. Developers would fill out the knowledge base with facts, and the inference engine would then query those facts to arrive at results.

The term AI has been around since the 1950s. In short, it depicts our struggle to build machines that can challenge what made humans the dominant lifeform on the planet: our intelligence. However, defining "intelligence" has turned out to be rather tricky, because what we perceive as intelligent changes over time. Early AIs were rule-based computer programs that could solve somewhat complex problems. Instead of hardcoding every decision the software was supposed to make, the program was divided into a knowledge base and an inference engine. Developers would fill out the knowledge base with facts, and the inference engine would then query those facts to arrive at results.

Car manufacturers are using AI in every facet of the car-making process. AI-based systems are enabling robots to pick parts from the conveyor belt with a high rate of success. Using deep learning, the robot automatically determines which parts to pick, how to pick, and in what sequence. This can significantly help reduce the number of workforces, and, in turn, boost the accuracy level of the process.

The origin of machine and deep learning algorithms, which increasingly affect almost all aspects of our life, is the learning mechanism of synaptic (weight) strengths connecting neurons in our brain. Attempting to imitate these brain functions, researchers bridged between neuroscience and artificial intelligence over half a century ago. However, since then experimental neuroscience has not directly advanced the field of machine learning and both disciplines -- neuroscience and machine learning -- seem to have developed independently. In an article published today in the journal Scientific Reports, researchers reveal that they have successfully rebuilt the bridge between experimental neuroscience and advanced artificial intelligence learning algorithms. Conducting new types of experiments on neuronal cultures, the researchers were able to demonstrate a new accelerated brain-inspired learning mechanism.

April 22, 2020 -- Siemens Healthineers has received clearance from the Food and Drug Administration (FDA) for its AIDAN artificial intelligence technologies on the Biograph family of positron emission tomography/computed tomography (PET/CT) systems, which includes the Biograph Horizon, Biograph mCT, and Biograph Vision. AIDAN is built on a foundation of patient-focused bed design and proprietary AI deep-learning technology to enable four new features – FlowMotion AI, OncoFreeze AI, PET FAST Workflow AI, and Multiparametric PET Suite AI. Siemens Healthineers PET/CT systems with AIDAN offer enhanced protection against cyber threats via syngo Security – a security package for general regulatory security rules that enables compliance with the Health Insurance and Accountability Act (HIPAA). FlowMotion AI Because each patient's body habitus and presentation of disease is different, tailoring PET/CT protocols to produce the highest-quality diagnostic imaging information possible for each patient can be difficult and time-consuming. The standard one-size-fits-all protocol lacks personalization and is often of suboptimal quality.