Ever since I was a boy, I was fascinated by the idea of miniaturization. I read Isaac Asimov's Fantastic Voyage and then, when I finally got my hands on the movie, I probably watched it a dozen times. The premise was that a team of scientists were miniaturized to the point where they could be injected into a person and perform surgery from the inside. Another movie with a similar premise was InnerSpace, starring the incredibly well-matched team of Martin Short and Dennis Quaid. There was the whole Honey, I Shrunk the Kids series of movies and TV shows, and I ate them up as well.
Artificial Intelligence (AI) is often regarded as “Great and Powerful;” it can add tremendous value by transforming business workflows with faster, smarter decisions. At the same time, AI can be mysterious and even scary. In order to build trust, AI needs to be transparent and explainable: “out from behind the curtain” so to speak. As IBM’s recent study on AI Ethics found, corporate boards are looking to Data and Technology leaders to make that happen, and I couldn’t agree more. CDOs and CTOs can be instrumental in bringing forth both human value and human values in enterprise AI. Putting the human first To build trust in business AI, we must always put the value of the human first. This should happen at the data-provider level and the decision-maker level. At the provider level, building trust starts with data governance to ensure that the data itself can be trusted. In our organization, embedded within this is the IBM…
The novel coronavirus pandemic has fundamentally changed doctor-patient dynamics worldwide. On one hand, healthcare workers and doctors are tirelessly working to treat COVID patients, while on the other, elderly patients and those with chronic diseases who need routine medical check-ups are faced with increased vulnerability and the risk of contracting coronavirus during regular hospital visits. While all of this is going on, patients with milder symptoms are being encouraged to use telehealth platforms to alleviate the strain on hospital facilities. A part of the solution to all these problems is one word: telemedicine. Here's a look at what telemedicine is, and how Nvidia (NVDA) is supporting it as the broader digital health ecosystem.
Mexico's increasingly militarized crackdown of powerful drug cartels has left nearly 39,000 unidentified bodies languishing in the country's morgues – a grotesque symbol of the ever-burgeoning war on drugs and rampant violence. Investigative NGO Quinto Elemento Labs, in a recent report, found that an alarming number of people have been simply buried in common graves without proper postmortems, while others were left in funeral homes. The so-called war of drugs has claimed the lives of nearly 300,000 people over the last 14 years, while another 73,000 have gone missing. All the while, these cartels have yet to be designated formal terrorist organizations despite boasting well-documented arsenals of sophisticated weaponry to rival most fear-inducing militias on battlefields abroad. Just last month, reports surfaced that Mexico's Jalisco New Generation Cartel (CJNG) now possess bomb-toting drones – which The Drive's Warzone depicts as "small quadcopter-type drones carrying small explosive devices to attack its enemies."
Held virtually today on the sidelines of the 64th IAEA General Conference, the first ever IAEA meeting discussing the use of artificial intelligence (AI) for nuclear applications showcased the ways in which AI-based approaches in nuclear science can benefit human health, water resource management and nuclear fusion research. Open to the public, the event gathered over 300 people from 43 countries and launched a global dialogue on the potential of AI for nuclear science and the related implications of its use, including ethics and transparency. AI refers to a collection of technologies that combine numerical data, process algorithms and continuously increasing computing power to develop systems capable of tracking complex problems in ways similar to human logic and reasoning. AI technologies can analyse large amounts of data to "learn" how to complete a particular task, a technique called machine learning. "Artificial Intelligence is advancing exponentially," said Najat Mokhtar, IAEA Deputy Director General and Head of the Department of Nuclear Sciences and Applications.
The Insurance industry has been dealing with vast volumes of data for years, but analytics, Artificial Intelligence (AI) and Machine Learning (ML) techniques are increasingly being used to help insurance providers make faster data driven decisions. Given the exponential level of data available today with AI/ML, insurance providers can now efficiently extract new insights into their customer's needs and create stronger long-term value. Starting with how the market calculates premiums, the insurance sector now has access to thousands of data points to help them calculate premiums. Machine learning algorithms expedite the identification of the most predictive attributes driving claims losses – the most recent data points being historical cancellation data and gaps in cover. This helps insurers become more competitive, match their risks to the most appropriate pricing strategies and write the risks that meet their underwriting appetite.
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.
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.
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.