If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Microsoft's ongoing partnership with San Francisco-based artificial intelligence research company OpenAI now includes a new exclusive license on the AI firm's groundbreaking GPT-3 language model, an auto-generating text program that's emerged as the most sophisticated of its kind in the industry. The two companies have been entwined for years through OpenAI's use of the Azure cloud computing platform, with Azure being how OpenAI accesses the vast computing resources it needs to train many of its models. Last year, Microsoft made a major $1 billion investment to become OpenAI's exclusive cloud provider, a deal that now involves being the exclusive licensee for GPT-3. OpenAI released GPT-3, the third iteration of its ever-growing language model, in July, and the program and its prior iterations have helped create some of the most fascinating AI language experiments to date. It's also inspired vigorous debate around the ethics of powerful AI programs that may be used for more nefarious purposes, with OpenAI initially refusing to publish research about the model for fear it would be misused.
Financial crime as a wider category of cybercrime continues to be one of the most potent of online threats, covering nefarious actives as diverse as fraud, money laundering and funding terrorism. Today, one of the startups that has been building data intelligence solutions to help combat that is announcing a fundraise to continue fueling its growth. Ripjar, a UK company founded by five data scientists who previously worked together in British intelligence at the Government Communications Headquarters (GCHQ, the UK's equivalent of the NSA), has raised $36.8 million (£28 million) in a Series B, money that it plans to use to continue expanding the scope of its AI platform -- which it calls Labyrinth -- and scaling the business. Labyrinth, as Ripjar describes it, works with both structured and unstructured data, using natural language processing and an API-based platform that lets organizations incorporate any data source they would like to analyse and monitor for activity. It automatically and in real time checks these against other data sources like sanctions lists, politically exposed persons (PEPs) lists and transaction alerts.
Machine learning is a field of study in the broad spectrum of artificial intelligence (AI) that can make predictions using data without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as recommendation engines, computer vision, spam filtering and so much more. They perform extraordinary well where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks. While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data-- over and over, faster and faster -- is a recent development. One of the most overwhelmingly represented machine learning techniques is a neural network.
Most companies recognize that aggressive adoption of digital technologies is increasingly critical to being competitive. Our research shows that the top 10% of early adopters of digital technologies have grown at twice the rate of the bottom 25%, and that they are using cloud systems -- not legacy systems -- to enable adoption, a trend we expect to accelerate among industry leaders over the coming five years. Many laggard and middle-of-the-pack companies, by comparison, are dramatically underestimating the cloud resources they will need in order to access, power, or train a new generation of intelligent applications presaged by breakthroughs like GPT-3, a state-of-the-art natural language processing (NLP) tool. The big breakthroughs in AI will be about language. The 2010s produced breakthroughs in vision-enabled technologies, from accurate image searches on the web to computer vision systems for medical image analysis or for detecting defective parts in manufacturing and assembly, as we described extensively in our book and research.
NLP - Natural Language Processing with Python Learn to use Machine Learning, Spacy, NLTK, SciKit-Learn, Deep Learning, and more to conduct Natural Language Processing Bestseller What you'll learn Welcome to the best Natural Language Processing course on the internet! This course is designed to be your complete online resource for learning how to use Natural Language Processing with the Python programming language. In the course we will cover everything you need to learn in order to become a world class practitioner of NLP with Python. We'll start off with the basics, learning how to open and work with text and PDF files with Python, as well as learning how to use regular expressions to search for custom patterns inside of text files. Afterwards we will begin with the basics of Natural Language Processing, utilizing the Natural Language Toolkit library for Python, as well as the state of the art Spacy library for ultra fast tokenization, parsing, entity recognition, and lemmatization of text.
Time series analysis and forecasting is one of the key fields in statistical programming. Due to modern technology the amount of available data grows substantially from day to day. They also know that decisions based on data gained in the past, and modeled for the future, can make a huge difference. Proper understanding and training in time series analysis and forecasting will give you the power to understand and create those models. This can make you an invaluable asset for your company/institution and will boost your career!
The vision of smart autonomous robots in the indoor environment is becoming a reality in the current decade. This vision is now becoming a reality because of emerging technologies of Sensor Fusion and Artificial Intelligence. Sensor fusion is aggregating informative features from disparate hardware resources. Just like autonomous vehicles, the robotic industry is quickly moving towards automatic smart robots for handling indoor tasks. Now the major question arises.
From cancelled conferences to disrupted supply chains, not a corner of the global economy is immune to the spread of COVID-19. Customer service has been under enormous pressure, and financial services firms such as Nationwide Building Society in the UK and the Royal Bank of Canada have launched chatbots to deal with the unusually high volume of requests. However, digital teams in financial services firms should remain wary of deploying chatbots and voice assistants faster than their customers are ready for them, or than their systems can support. To better understand chatbot capabilities in financial services, we evaluated the chatbot offering of over 150 global financial services firms. We also analyzed consumer sentiment and adoption of chatbots in Europe and North America.
It's a sci-fi trope: A universal translator that allows instantaneous communication between speakers of different languages. The TARDIS does it in Doctor Who, the babble fish serves that function in The Hitchhiker's Guide to the Galaxy, and of course the linguist Hoshi Sato had one on the Enterprise. Cheetah Mobile, a Chinese mobile technology company that's had some bumps in the U.S. market, is coming out with a new version of an existing translator device, this one promising instantaneous two-way communication in 73 languages thanks to a standalone piece of kit that's smaller than a smart phone. Powered by Microsoft's automatic speech recognition software and OrionStar AI Technology, the device is meant to provide users with instant two-way translation in 73 languages while displaying text on a 1.54" IPS-LCD touch-screen, which offers a text-to-speech function as well. Conversations can be recorded for up to two hours in HD audio, and noise cancelling microphones enable accurate translation even in noisy environments.
Having learned a neural network from data, it can be used for prediction. Since the top layers of the network have been trained in a supervised manner to perform a particular classification or prediction task, the top layers are really useful only for that task. A network trained to detect stop signs is useless for detecting handwritten digits or cats. A fascinating result is obtained by taking the pre-trained bottom layers and studying what the features they have learned look like. This can be achieved by generating images that activate a certain set of neurons in the bottom layers.