Goto

Collaborating Authors

Results


Intellectual property and investment in Artificial Intelligence

#artificialintelligence

Patents provide third-party opinions on the uniqueness of the technology and a'saleable asset insurance' in the event that the company ceases trading


Three opportunities of Digital Transformation: AI, IoT and Blockchain

#artificialintelligence

Koomey's law This law posits that the energy efficiency of computation doubles roughly every one-and-a-half years (see Figure 1–7). In other words, the energy necessary for the same amount of computation halves in that time span. To visualize the exponential impact this has, consider the face that a fully charged MacBook Air, when applying the energy efficiency of computation of 1992, would completely drain its battery in a mere 1.5 seconds. According to Koomey's law, the energy requirements for computation in embedded devices is shrinking to the point that harvesting the required energy from ambient sources like solar power and thermal energy should suffice to power the computation necessary in many applications. Metcalfe's law This law has nothing to do with chips, but all to do with connectivity. Formulated by Robert Metcalfe as he invented Ethernet, the law essentially states that the value of a network increases exponentially with regard to the number of its nodes (see Figure 1–8).


Artificial intelligence

#artificialintelligence

Deep learning[133] uses several layers of neurons between the network's inputs and outputs. The multiple layers can progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces.[134] Deep learning has drastically improved the performance of programs in many important subfields of artificial intelligence, including computer vision, speech recognition, image classification[135] and others. Deep learning often uses convolutional neural networks for many or all of its layers.


Maverick dialog was all AI; Police want your happy childhood photos; An AI model Created Its Own Secret Language; Web3 Is Going to Get People Hurt.

#artificialintelligence

Thanks to more the 750,000 readers and supporters of this newsletter. I hope that you enjoy the latest AI news and insights, make sure to check the Web3 section at the end! [BGR] Val Kilmer’s Top Gun: Maverick dialog was all AI since he can no longer speak: Thanks to a special voice AI program, Kilm


Interesting Ways AI Impacts Software Development and Testing – QA Valley

#artificialintelligence

Artificial intelligence is transforming modern business and the modern way of life. Along with machine learning and continuous human support, artificial intelligence is seeing vast adoption rates across entire industries and sectors. Software development and software testing are definitely two of the best examples of AI application as well as the use of machine learning and deep learning systems to achieve short, mid, and long-term goals. Software developers and dev agencies can nowadays lean on AI and machine learning to streamline their processes and ensure better output and performance. Let's take a closer look at some of the most interesting ways software developers can leverage AI and complementary technologies like machine learning to enhance software development and testing.


An Enhanced Secure Deep Learning Algorithm for Fraud Detection in Wireless Communication

#artificialintelligence

In today’s era of technology, especially in the Internet commerce and banking, the transactions done by the Mastercards have been increasing rapidly. The card becomes the highly useable equipment for Internet shopping. Such demanding and inflation rate causes a considerable damage and enhancement in fraud cases also. It is very much necessary to stop the fraud transactions because it impacts on financial conditions over time the anomaly detection is having some important application to detect the fraud detection. A novel framework which integrates Spark with a deep learning approach is proposed in this work. This work also implements different machine learning techniques for detection of fraudulent like random forest, SVM, logistic regression, decision tree, and KNN. Comparative analysis is done by using various parameters. More than 96% accuracy was obtained for both training and testing datasets. The existing system like Cardwatch, web service-based fraud detection, needs labelled data for both genuine and fraudulent transactions. New frauds cannot be found in these existing techniques. The dataset which is used contains transaction made by credit cards in September 2013 by cardholders of Europe. The dataset contains the transactions occurred in 2 days, in which there are 492 fraud transactions out of 284,807 which is 0.172% of all transaction.


Data Scientist

#artificialintelligence

Elastic is a free and open search company that powers enterprise search, observability, and security solutions built on one technology stack that can be deployed anywhere. From finding documents to monitoring infrastructure to hunting for threats, Elastic makes data usable in real-time and at scale. Thousands of organizations worldwide, including Barclays, Cisco, eBay, Fairfax, ING, Goldman Sachs, Microsoft, The Mayo Clinic, NASA, The New York Times, Wikipedia, and Verizon, use Elastic to power mission-critical systems. Founded in 2012, Elastic is a distributed company with Elasticians around the globe. The Machine Learning team is responsible for developing and integrating statistical tools and machine learning models in ElasticSearch and Kibana.


Deepfake attacks can easily trick facial recognition

#artificialintelligence

In brief Miscreants can easily steal someone else's identity by tricking live facial recognition software using deepfakes, according to a new report. Sensity AI, a startup focused on tackling identity fraud, carried out a series of pretend attacks. Engineers scanned the image of someone from an ID card, and mapped their likeness onto another person's face. Sensity then tested whether they could breach live facial recognition systems by tricking them into believing the pretend attacker is a real user. So-called "liveness tests" try to authenticate identities in real-time, relying on images or video streams from cameras like face recognition used to unlock mobile phones, for example.


Real-time Analytics News for Week Ending April 30 - RTInsights

#artificialintelligence

In this week's real-time analytics news: HPE launched HPE Swarm Learning, a privacy-preserving, decentralized machine learning framework for the edge. Keeping pace with news and developments in the real-time analytics market can be a daunting task. We want to help by providing a summary of some of the important news items our staff came across this week. Hewlett Packard Enterprise (HPE) announced the launch of HPE Swarm Learning, an AI solution to accelerate insights at the edge, from diagnosing diseases to detecting credit card fraud, by sharing and unifying AI model learnings without compromising data privacy. HPE Swarm Learning is a privacy-preserving, decentralized machine learning framework for the edge or distributed sites.


Machine Learning in Financial Crime Control

#artificialintelligence

Yesterday I was approached multiple times about this FD article and Sygno's experience with regulators and Machine Learning in Transaction Monitoring. And, due to the lawsuit, with special interest in how the Dutch central bank (DNB) operates in these matters. Though I don't know the details of this case other than those presented in the media, it seems we have a vastly different experience with (Dutch) regulators. We see a regulator that actively promotes Machine Learning and usage of Data. That doesn't mean that all Machine Learning initiatives pass their scrutiny.