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) …
What are the biggest machine learning trends of 2019 so far, and where are we heading? The most notable trend right now in machine learning is the rapid growth in machine learning developer tooling and how that changes the process of building, deploying and managing machine learning. On one end of the spectrum, we have the growth of AutoML like tools which provides powerful machine learning models as a plug and play solution without the need for deep machine learning expertise. This would rapidly bring the power of machine learning to more and more industries. On the other end of the spectrum, there are numerous tools and products that standardize and provide powerful abstractions for different aspects of machine learning development that lets the data scientists to focus exclusively on their core competencies.
At some point, the machine demonstrates that it gets the difference between two dots and ten dots--regardless of how those dots appear on the screen. This ability to abstract quantities and distinguish between the resulting numbers may not seem like a big deal because it's so easy for humans. But new research shows that machines now have this ability to conceptualize numbers--something we might think of as "artificial numerosity." Numerosity is the ability to recognize specific quantities. It's the ability to tune our perception of objects so that we sense how many of them there are.
Of all cancers worldwide, lung cancer is the deadliest. It takes more than 1.7 million lives per year -- more than breast, prostate and colorectal cancer combined. Part of the problem is that the majority of cancers aren't caught until later stages, when interventions tend to be less successful. Google is determined to change that, and with its new AI-based tool, it hopes to make lung cancer prediction more accurate and more accessible. To screen for lung cancer, radiologists typically view hundreds of images from a single CT scan.
Today's post is based on a project I recently did in work. I was really excited to implement it and to write it up as a blog post as it gave me a chance to do some data engineering and also do something that was quite valuable for my team. Not too long ago, I discovered that we had a relatively large amount of user log data relating to one of our data products stored on our systems. Remember that a blockchain is an immutable, sequential chain of records called Blocks. They can contain transactions, files or any data you like, really.
D.A., A.P.K., S.B. and B.C. developed the network architecture and data/modeling infrastructure, training and testing setup. D.A. and A.P.K. created the figures, wrote the methods and performed additional analysis requested in the review process. D.P.N. and J.J.R. provided clinical expertise and guidance on the study design. G.C and S.S. advised on the modeling techniques. M.E., S.S., J.J.R., B.C., W.Y. and D.A. created the datasets, interpreted the data and defined the clinical labels.
"It began three and a half billion years ago in a pool of muck, when a molecule made a copy of itself and so became the ultimate ancestor of all earthly life. It began four million years ago, when brain volumes began climbing rapidly in the hominid line. In less than thirty years, it will end." Jaan Tallinn stumbled across these words in 2007, in an online essay called "Staring into the Singularity." The "it" is human civilization.
Facebook is trying to develop artificial intelligence models that will allow robots–including walking hexapods, articulated arms, and robotic hands fitted with tactile sensors–to learn by themselves, and to keep getting smarter as they encounter more and more tasks and situations. In the case of the spider-like hexapod ("Daisy") I saw walking around a patio at Facebook last week, the researchers give a goal to the robot and task the model with figuring out by trial and error how to get there. The goal can be as simple as just moving forward. In order to walk, the spider has to know a lot about its balance, location, and orientation in space. It gathers this information through the sensors on its legs.
Deep Learning is really starting to establish itself as a major new tool in visual effects. Currently the tools are still in their infancy but they are changing the way visual effects can be approached. Instead of a pipeline consisting of modelling, texturing, lighting and rendering, these new approaches are hallucinating or plausibly creating imagery that is based on training data sets. Machine Learning, the superset of Deep Learning and similar approaches have had great success in image classification, image recognition and image synthesis. At fxguide we covered Synthesia in the UK, a company born out of research first published as Face2Face.
You know about Cortana, Siri and Google Assistant, right? Have you ever imagined that you can make your own virtual personal assistant and customize it as you want? Today, we'll be doing it here. We'll be building a personal assistant from scratch in python. Oh, Before getting into it, let me tell you that by no means it's an AI but just a powerful example of what AI can do, and how versatile and amazing python is.
Israeli radiology startup Aidoc has received FDA clearance for its AI-based product meant to help identify potential cases of pulmonary embolism in chest CT scans. Pulmonary embolism (PE) – which occurs when a blood clot gets lodged in the lung – is considered a silent killer that causes up to 200,000 deaths a year in the United States. The condition often strikes with little to no warning and diagnosis of a case can be extremely time-sensitive. Aidoc's technology doesn't require dedicated hardware and runs continuously on hospital systems, automatically ingesting radiological images. The 70-person company focuses on workflow optimization in radiology to help triage high risk patients for additional and faster review.