Clifford Chance has become the latest UK law firm to adopt the use of artificial intelligence (AI) after partnering with software provider Kira Systems. The magic circle firm has teamed up with Kira Systems to improve the "speed, efficiency and quality" of its processes. In practice Kira works by searching and analysing text within contracts. The firm believes its use will reduce the time spent carrying out due diligence work and increase the number of documents that can be reviewed. It was chosen by Clifford Chance because the software can be easily modified by the firm's lawyers and is able to adapt to specific client requests.

Many advances in artificial intelligence are innovative and extraordinary, but some are downright creepy. Here are 10 of the eeriest ways people are using, or could use, AI. Nautilus is a supercomputer that can predict the future based on news articles. It is a self-learning supercomputer that was given information from millions of articles, dating back to the 1940s. It was able to locate Osama Bin Laden within 200km.

One of the most frustrating holiday experiences I ever had was when my bank decided to lock me out of my account the minute I touched down at my destination. In short, it decided my financial activity was suspicious, my attempt to withdraw cash in Italy was probably fraudulent, and took a whole load of expensive phone calls to convince it otherwise. This is precisely the kind of situation that Featurespace is helping organisations to limit. "You can't stop 100% false positives," – i.e., these untrue notifications of fraud – says the CEO Martina King, over the phone from the company headquarters in Cambridge, UK. "But we lead to a 70% reduction."

Maxime Fages Maxime's career spanned across the strategic aspects of value and risk, with a particular focus on trading behaviors and market microstructure over the past few years. He embraced a quantitative angle in M&A, fund management or currently corporate strategy and has always been an avid open-source software user. Derek began his career on the floor of the CBOT then moved upstairs to focus on proprietary trading and strategy development. He manages global multi strategy portfolios, focusing in the futures and options space. He is currently the Deputy Director of Systematic Trading at Foretrade Investment Co Ltd.

Machine learning and artificial intelligence have been of interest for a couple of years due to the great potential in automating tasks and even in creating a fully sentient artificial lifeform. Although we're years, or decades, away from the latter, current machine learning algorithms have been tasked with everything from winning professional Go championships to assisting oncologists with diagnostics. With the increased number of internet-connected devices, estimated at 6.4 billion in 2016, cybercrime will not only intensify and become more viral, but also reach new levels of complexity. Traditional security technologies and mechanisms will prove unable to adapt to the rapidly changing threat landscape. These algorithms can identify patterns in large amounts of data faster than humans, and they can also constantly learn.

People are hooked on messengers. Messenger apps are an alternative to home screens on the phones of millions of people. The big four messengers have overtaken the big four social networks in the number of active users. There are Line and WeChat in the Asian market. There are also Telegram, Slack, and Kik.

Human Intelligence: A leading researcher in human intelligence, suggests as a heuristic hypothesis'' that all normal humans have the same intellectual mechanisms and that differences in intelligence are related to quantitative biochemical and physiological conditions''. I see them as speed, short term memory, and the ability to form accurate and retrievable long term memories. What is artificial intelligence Artificial Intelligence: Computer programs have plenty of speed and memory but their abilities correspond to the intellectual mechanisms that program designers understand well enough to put in programs. Some abilities that children normally don't develop till they are teenagers may be in, and some abilities possessed by two year olds are still out. The matter is further complicated by the fact that the cognitive sciences still have not succeeded in determining exactly what the human abilities are.

Operational deployment in your business process is where AI, machine learning and predictive algorithms actually start generating measurable results and ROI for your organization. Therefore, the faster you are able deploy and use these "intelligent" models in your IT environment, the more your business will reap in the benefits of smarter decisions. In the past, the operational deployment of AI, machine learning and predictive algorithms used to be a tedious, labor- and time-intensive task. Predictive and machine learning models, once built by the data science team, needed to be manually re-coded for enterprise deployment in operational IT systems. Only then predictive models could be used to effectively score new data in real-time streaming or big data batch applications.

A recent trend in Deep Learning are Attention Mechanisms. In an interview, Ilya Sutskever, now the research director of OpenAI, mentioned that Attention Mechanisms are one of the most exciting advancements, and that they are here to stay. But what are Attention Mechanisms? Attention Mechanisms in Neural Networks are (very) loosely based on the visual attention mechanism found in humans. Human visual attention is well-studied and while there exist different models, all of them essentially come down to being able to focus on a certain region of an image with "high resolution" while perceiving the surrounding image in "low resolution", and then adjusting the focal point over time.

Data preparation is required when working with neural network and deep learning models. Increasingly data augmentation is also required on more complex object recognition tasks. In this post you will discover how to use data preparation and data augmentation with your image datasets when developing and evaluating deep learning models in Python with Keras. Like the rest of Keras, the image augmentation API is simple and powerful. Keras provides the ImageDataGenerator class that defines the configuration for image data preparation and augmentation.