Combining the biological data generated from Body-on-a-Chip with the computational power of machine learning and Artificial Intelligence should …

Researchers at the Indian Institute of Science (IISc) have developed a new graphic processing unit-based machine learning algorithm that may hold …

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This special issue highlights the applications, practices and theory of artificial intelligence in the domain of cyber security. In the past few decades there has been an exponential rise in the application of artificial intelligence technologies (such as deep learning, machine learning, block-chain, and virtualization etc.) for solving complex and intricate problems arising in the domain of cyber security. The versatility of these techniques have made them a favorite among scientists and researchers working in diverse areas. The primary objective of this topical collection is to bring forward thorough, in-depth, and well-focused developments of artificial intelligence technologies and their applications in cyber security domain, to propose new approaches, and to present applications of innovative approaches in real facilities. AI can be both a blessing and a curse for cybersecurity.

Splitting data means that we will divide it into subsets. For data science models, datasets are usually partitioned into two or three subsets: training, validation, and test. Each subset of data has a purpose, from creating a model to ensuring its performance. To decide on the size of each subset, we often see standard rules and ratios. There have been some discussions about what an optimal split might be, but in general, I would recommend keeping in mind that not having enough data, either on the training or validation set, will result in a model that is difficult to learn/train, or you will have difficulty determining whether this model actually performs well or not. It's worth noting that you don't always have to make three segments.

MLOps knowledge has become one of the major skills that one machine learning engineer can have. However, putting a machine learning model into production successfully is not an easy task. It requires a wide range of software development and DevOps abilities in addition to data science understanding. In a nutshell, in order to increase your value as a machine learning engineer, you must not only understand how to apply various Machine Learning and Deep Learning models to a specific problem, but also how to test, verify, and deploy them. Having someone who can put Machine Learning models into production has become a major benefit for any business. One of the final problems, when it comes to putting Machine Learning models into production, is verifying that API that is serving this model is having good performance.

People with arm paralysis might easily feed themselves in the future. Johns Hopkins University-led researchers have developed a new technique that let a partially paralyzed man feed himself using robotic arms connected through a brain-machine interface. He only had to make small movements with his fists at certain prompts (such as "select cut location") to have the fork- and knife-equipped arms cut food and bring it to his mouth. He could have dessert within 90 seconds, according to the researchers. The new method centers on a shared control system that minimizes the amount of mental input required to complete a task.

With the ability to revolutionize everything from self-driving cars to robotic surgeons, artificial intelligence is on the cutting edge of tech innovation. Two of the most widely recognized AI services are Microsoft's Azure Machine Learning and IBM's Watson. Both boast impressive functionality, but which one should you choose for your business? Azure Machine Learning is a cloud-based service that allows data scientists or developers to train, build and deploy ML models. It has a rich set of tools that makes it easy to create predictive analytics solutions. This service can be used to build predictive models using a variety of ML algorithms, including regression, classification and clustering.

A leaked Woolworths employee training module slide claims that it is using "artificial intelligence and facial mapping" in its stores -- but the company denies it is using the technology. This is from a Woolies training module from 2020." At the bottom of the slide, a box titled "Did You Know?" boasts about the company's use of technology to catch offenders: "Our high standard CCTV is already resulting in offenders being arrested by police. We are using technology like artificial intelligence and facial mapping to identify offenders!" Woolworths confirmed that the slide was real, but denied it is using either artificial intelligence or facial recognition to prevent theft.

We'll take a deeper look at this proprietary technique when we chat with its creator, in a later article on autoencoder-based deepfakes. However, results as impressive as these are difficult to obtain with standard open source deepfakes software; require expensive and powerful hardware; and usually entail very long training times to obtain very limited sequences. Machine learning models are trained and developed within the capacity of the VRAM and tensor cores on a single video card -- a prospect that becomes more and more challenging in the age of hyperscale datasets, and which presents some specific obstacles to improving deepfake quality. Approaches that shunt training cycles to the CPU, or divide the workload up among multiple GPUs via Data Parallelism or Model Parallelism techniques (we'll examine these more closely in a later article) are still in the early stages. For the near future, a single-GPU training setup remains the most common scenario.