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Building a Modern Enterprise with Artificial Intelligence


To stay competitive and relevant in their industries, enterprises increasingly need to become AI-driven. AI is a new key to improving business processes, making better decisions, monetizing data, increasing security and more. The growing importance of AI in the enterprise is a point that industry observers now emphasize. Just consider this view from the global consulting firm Deloitte: "As AI technologies standardize across industries, becoming an AI-fueled organization will likely be table stakes for survival. And that means rethinking the way humans and machines interact within working environments."1

New Army 'Accelerated Multi-Node Attack' enabled by AI at Project Convergence 2020

FOX News

Fox News Flash top headlines are here. Check out what's clicking on Gray Eagle drones were armed with HELLFIRE missiles and GBU-69 glide bombs, 155mm artillery weapons fired rounds 60km (37.3 miles) to destroy SA-22 enemy air defenses and armored ground combat vehicles directly hit multiple T-72 tanks during the Army's Project Convergence 2020 at Yuma Proving Grounds, Ariz. The real story, however, according to senior Army leaders attending the service's transformational combat experiment, was about data sharing, networked targeting and a cutting edge AI system called FIRESTORM. "The bullet flying through the air and exploding is interesting, but that is not what is compelling about Project Convergence. It is everything that happens before the trigger is pulled. We did not come out here for a precision-fires exercise, what we came out here to do is increase the speed of information between sensing the target and passing that information to the effector," Brig.

Artificial Intelligence In Healthcare -- Everything Artificial Intelligence + Robotics + IoT +


Artificial intelligence (AI), Machine learning, NLP, Robotics, and Automation are increasingly prevalent in all aspects and are being applied to healthcare as well. These technologies have the potential to transform all aspects of health care from patient care to the development and production of new experimental drugs that can have a faster roll-out date than traditional methods. There are numerous research studies suggesting that AI can outperform humans at key healthcare tasks, such as diagnosing ailments. Here is a great example, AI'outperforms' doctors diagnosing breast cancer¹. Artificial intelligence is a collection of technologies that come together form artificial intelligence. Tech firms and startups are also working assiduously on the same issues.

Top 10 Applications of Machine Learning in Healthcare - FWS


Healthcare is an important industry which offers value-based care to millions of people, while at the same time becoming top revenue earners for many countries. Today, the Healthcare industry in the US alone earns a revenue of $1.668 trillion. The US also spends more on healthcare per capita as compared to most other developed or developing nations. Quality, Value, and Outcome are three buzzwords that always accompany healthcare and promise a lot, and today, healthcare specialists and stakeholders around the globe are looking for innovative ways to deliver on this promise. Technology-enabled smart healthcare is no longer a flight of fancy, as Internet-connected medical devices are holding the health system as we know it together from falling apart under the population burden.

Classification Algorithms Explained in 30 Minutes -


In the Machine Learning terminology, the process of Classification can be defined as a supervised learning algorithm that aims at categorizing a set of data into different classes. In other words, if we think of a dataset as a set of data instances, and each data instance as a set of features, then Classification is the process of predicting the particular class that that individual data instance might belong to, based on its features. Unlike regression where the target variable (i.e., the predicted value) belongs to a continuous distribution, in case of classification, the target variable is discrete. It can only be one of the various target classes in a given problem. For example, let's say you are working on a cat-dog-classifier model that predicts whether the animal in a given image is a cat or a dog.

How to Automate Machine Learning Tasks Using Autogluon?


Machine Learning is a part of Artificial Intelligence, which consists of algorithms and improving automatically with time. In order to apply machine learning to different datasets, we need to clean the data and prepare it for the machine learning phase. Also, we need to identify the data or problem whether it is Regression, Classification, etc. There are many machine learning algorithms that we can use for our prediction, regression, classification, etc. problems. But we need to call them individually and pass our data into them as parameters.

Neural network trained to control anesthetic doses, keep patients under during surgery


Academics from the Massachusetts Institute of Technology (MIT) and Massachusetts General Hospital have demonstrated how neural networks can be trained to administer anesthetic during surgery. Over the past decade, machine learning (ML), artificial intelligence (AI), and deep learning algorithms have been developed and applied to a range of sectors and applications, including in the medical field. In healthcare, the potential of neural networks and deep learning has been demonstrated in the automatic analysis of large medical datasets to detect patterns and trends; improved diagnosis procedures, tumor detection based on radiology images, and more recently, an exploration into robotic surgery. Now, neural networking may have new, previously-unexplored applications in the surgical and drug administration areas. A team made up of MIT and Mass General scientists, as reported by Tech Xplore, have developed and trained a neural network to administrator Propofol, a drug commonly used as general anesthesia when patients are undergoing medical procedures.

There's no data science unicorn - building a data team at HSBC


HSBC is one of the world's largest financial institutions, serving more than 40 million customers globally. One of its largest divisions, Wealth and Personal Banking, supports individuals, families, business owners, investors and entrepreneurs. It provides products and services that include current accounts, credit cards, personal loans and mortgages, as well as savings, investments, insurance and wealth management. At the centre of the Wealth and Personal Banking division is a data analytics group, which is responsible for providing data-tailored services to HSBC teams and customers all around the world. Rahul Boteju, Global Head of Data Analytics at HSBC, was speaking this week at the Big Data LDN event, where he shed some light on what it takes to build an effective data science team that can scale.

Introducing Our Low Code Machine Learning Platform


We are very excited to release the free tier of dunnhumby Model Lab this as part of our partnership with Microsoft. We make it easy to connect your data, clean your data, and run your machine learning pipeline within minutes. You can then take that output and copy right into a notebook for further refinement if needed. You can create new projects, reference datasets, and create multiple experiments in just a few clicks! You can also follow the progress of your machine learning experiments as they update in real-time.

Can artificial intelligence encourage good behaviour among Internet users?


Hostile and hateful remarks are thick on the ground on social networks in spite of persistent efforts by Facebook, Twitter, Reddit and YouTube to tone them down. Now researchers at the OpenWeb platform have turned to artificial intelligence to moderate Internet users' comments before they are even posted. The method appears to be effective because one third of users modified the text of their comments when they received a nudge from the new system, which warned that what they had written might be perceived as offensive. The study conducted by OpenWeb and Perspective API analysed 400,000 comments that some 50,000 users were preparing to post on sites like AOL, Salon, Newsweek, RT and Sky Sports. Some of these users received a feedback message or nudge from a machine learning algorithm to the effect that the text they were preparing to post might be insulting, or against the rules for the forum they were using.