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) …
TensorFlow has become the most popular tool and framework for machine learning in a short span of time. It enjoys tremendous popularity among ML engineers and developers. According to the Hacker News Hiring Trends, May 2020, TensorFlow jobs are in great demand. Here are five reasons behind TensorFlow's popularity: TensorFlow is the only framework available for running machine learning models from the cloud to the tiniest microcontroller device. Models trained with TensorFlow can be optimized for CPU and GPU.
TEHRAN, Iran (AP) -- Iran's supreme leader on Saturday demanded the "definitive punishment" of those behind the killing of a scientist who led Tehran's disbanded military nuclear program, as the Islamic Republic blamed Israel for a slaying that has raised fears of reignited tensions across the Middle East. After years of being in the shadows, the image of Mohsen Fakhrizadeh suddenly was to be seen everywhere in Iranian media, as his widow spoke on state television and officials publicly demanded revenge on Israel for the scientist's slaying. Israel, long suspected of killing Iranian scientists a decade ago amid earlier tensions over Tehran's nuclear program, has yet to comment on Fakhrizadeh's killing Friday. However, the attack bore the hallmarks of a carefully planned, military-style ambush, the likes of which Israel has been accused of conducting before. The attack has renewed fears of Iran striking back against the U.S., Israel's closest ally in the region, as it did earlier this year when a U.S. drone strike killed a top Iranian general.
The hard part is diversifying the content. So if we just have the same character in an environment doing everything, it's not going to work, right? So how do you actually create hundreds or thousands of variations of that character model with different behavior and things like that? That's been really the core focus of how we're thinking about our technology. You're listening to Gradient Dissent, a show where we learn about making machine learning models work in the real world. Daeil Kim is the co-founder and CEO of AI.Reverie. A startup that specializes in creating high quality synthetic training data for computer vision algorithms. Before that he was a senior data scientist at the New York Times. And before that he got his PhD in computer science from Brown university, focusing on machine learning and Bayesian statistics. He's going to talk about tools that will advance machine learning progress, and he's going to talk about synthetic data. I'm super excited for this. I was looking at your LinkedIn and you have a little bit of an unusual path, right? You did a liberal arts undergrad. Can you say a little bit about... I feel like I come across people quite a lot that want to make career transitions into machine learning and related fields. What was that for you? What prompted you to do it?
'K-Nearest Neighbors (KNN) is a model that classifies data points based on the points that are most similar to it. It uses test data to make an "educated guess" on what an unclassified point should be classified as' We will be building our KNN model using python's most popular machine learning package'scikit-learn'. Scikit-learn provides data scientists with various tools for performing machine learning tasks. For our KNN model, we are going to use the'KNeighborsClassifier' algorithm which is readily available in scikit-learn package. Finally, we will evaluate our KNN model predictions using the'accuracy score' function in scikit-learn.
Healthcare cybersecurity is in triage mode. As systems are stretched to the limits by COVID-19 and technology becomes an essential part of everyday patient interactions, hospital and healthcare IT departments have been left to figure out how to make it all work together, safely and securely. Most notably, the connectivity of everything from thermometers to defibrillators is exponentially increasing the attack surface, presenting vulnerabilities IT professionals might not even know are on their networks. Get the whole story and DOWNLOAD the eBook now – on us!] The result has been a newfound attention from ransomware and other malicious actors circling and waiting for the right time to strike. Rather than feeling overwhelmed in the current cybersecurity environment, it's important for healthcare and hospital IT teams to look at security their networks as a constant work in progress, rather than a single project with a start and end point, according to experts Jeff Horne from Ordr and G. Anthony Reina who participated in Threatpost's November webinar on Heathcare Cybersecurity. "This is a proactive space," Reina said. "This is something where you can't just be reactive. You actually have to be going out there, searching for those sorts of things, and so even on the technologies that we have, you know, we're, we're proactive about saying that security is an evolving, you know, kind of technology, It's not something where we're going to be finished." Healthcare IT pros, and security professionals more generally, also need to get a firm handle on what lives their networks and its potential level of exposure. The fine-tuned expertise of healthcare connected machines, along with the enormous cost to upgrade hardware in many instances, leave holes on a network that simply cannot be patched. "Because, from an IT perspective, you cannot manage what you can't see, and from a security perspective, you can't control and protect what you don't know," Horne said. Threatpost's experts explained how healthcare organizations can get out of triage mode and ahead of the next attack. The webinar covers everything from bread and butter patching to a brand-new secure data model which applies federated learning to functions as critical as diagnosing a brain tumor. Alternatively, a lightly edited transcript of the event follows below. Thank you so much for joining. We have an excellent conversation planned on a critically important topic, Healthcare cybersecurity. My name is Becky Bracken, I'll be your host for today's discussion. Before we get started, I want to remind you there's a widget on the upper right-hand corner of your screen where you can submit questions to our panelists at any time. We encourage you to do that. You'll have to answer questions and we want to make sure we're covering topics most interesting to you, OK, sure. Let's just introduce our panelists today. First we have Jeff Horne. Jeff is currently the CSO at Ordr and his priors include SpaceX.
If there is one mantra of business operations, it is that nothing stands still. This means that business processes, which may have been valid on day one when they were first documented, evolve as soon as people start following them. Getting started on any intelligent business process initiatives requires an understanding of how business processes are currently run. Nelson Petracek, CTO at Tibco Software, says: "People have a tendency to find shortcuts if given the opportunity, or they may find creative ways of circumventing steps or tasks that are seen as unnecessary or as bottlenecks. Fraud and other undesirable activities may also influence process execution within or across organisations."
Recently, researchers from the Western Kentucky University proposed a multi-modal deep learning framework that has the capability to classify genres of video games based on the cover and textual description. The researchers claimed that this research is the first-ever attempt on automatic genre classification using a deep learning approach. Videos games have been one of the most widespread, profitable, and prominent forms of entertainment around the globe. Also, genre and its classification systems play a significant role in the development of video games. According to the researchers, video game covers and textual descriptions are usually the very first impression to its consumers, and they often convey important information about the video games.
In this podcast, I will be joined by UC Berkley professor Stuart Russell to explore the role of artificial intelligence in our world. As one of the world's leading thought leaders on the topic, we will talk about the latest AI innovations, the dangers that come with AI, as well as what this all m...
If I wanted to learn deep learning with Python again, I would probably start with PyTorch, an open-source library developed by Facebook's AI Research Lab that is powerful, easy to learn, and very versatile. When it comes to training material, however, PyTorch lags behind TensorFlow, Google's flagship deep learning library. There are fewer books on PyTorch than TensorFlow, and even fewer online courses. Among them is Deep Learning with PyTorch by Eli Stevens, Luca Antiga, and Thomas Viehmann, three engineers who have contributed to the project and have extensive experience developing deep learning solutions. Deep Learning with PyTorch is split across two main sections, first teaching the basics of deep learning and then delving into an advanced, real-world application of medical imaging analysis.
We will show you exactly how to succeed these applications, through Real World Business case studies. And for each of these applications we will build a separate AI to solve the challenge. In Part 1 - Optimizing Processes, we will build an AI that will optimize the flows in an E-Commerce warehouse. In Part 2 - Minimizing Costs, we will build a more advanced AI that will minimize the costs in energy consumption of a data center by more than 50%! Just as Google did last year thanks to DeepMind.