programmer


Inside the AI developer's toolbox

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Jos Martin, senior engineering manager at data analysis and simulation software company MathWorks, defines an artificial intelligence framework as something for programmers that abstracts elements of complexity. In a post on the Computer Weekly Developer Network (CWDN) blog, Martin says: "As a user of deep learning you don't usually need to go and write training algorithms." For instance, Google's Brain team has developed TensorFlow, Facebook has created PyTorch, Microsoft provides its Cognitive Toolkit, while Amazon Web Services (AWS) offers the MXNet deep learning framework as a service. Martin says there are many different intelligence layer types already implemented in openly available libraries, and as such, developers should really view any single layer of intelligence as a whole set of computing neurons connected together. What these frameworks have in common is that they aim to make it easier for programmers to create AI-powered applications.


Deep Learning Algorithms: The Future of Financial Investment?

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This article was written by Harry Chiang, a financial analyst at I Know First. Most humans would understand, perhaps even intuitively, that when he or she runs there is a certain path of movement and way in which he or she is interacting with the environment. The athlete will follow the curvature of the track. They dictate how his or her body moves and how his or her feet must move along the rubber. A machine, however, would struggle to understand all these small details.


Why Artificial Intelligence developers should be more responsible

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When people hear the word'Artificial Intelligence' (AI), the first thoughts and images that come to mind are those fed to us by Hollywood movies of the past few decades and recently by Netflix web series such as Black Mirror. This fictional representation paints a dire picture of AI destroying or enslaving humanity, but this vision of AI is too farfetched and distorts what is really happening in the field. At a recently concluded conference and exhibition on the subject conducted by the Confederation of Indian Industries (CII), AI was talked about as more of a tool and the terms used were Augmented Intelligence and Machine Learning rather than AI. CII has initiated three outcome-based AI task forces on skill development, education and agriculture. Many pilot projects by these task forces are currently being conducted at various sites.


AI Algorithms and the Black Box (Video)

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In his session at KMWorld 2018, titled "Rethinking KM for an Age of AI & IoT," Serious Insights founder and principal analyst Daniel W. Rasmus explored the impact of AI and IoT on KM as machines get better and better at sensing the environment, comprehending content, interpreting signals, and anticipating needs. With newer AI systems that can deal with insights that change based on conditions that may occur in microseconds, it can be difficult for people to be comfortable with decision-making processes that they don't understand and the choices those automated processes result in. "And for knowledge management people that should make you very uncomfortable," said Rasmus. "And it also makes you very uncomfortable if you look at what has happened recently with Twitter and Facebook, etc., in the context of our elections. They have had to bring in people to start looking at what their algorithms are doing, and saying: Are they catching everybody that's doing things that are anti-election-oriented?" Companies want programmers to use the most efficient method to get to decisions, he observed.


Python code completion gets an assist from machine learning

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The makers of a new programmer's assistant for Python developers are tapping machine learning technology to build new kinds of programming tools. Kite, billed by its creators as "the AI copilot for Python programmers," is a code-completion system designed to go beyond the conventional auto-suggest algorithms found in IDEs. Kite integration is available for most every major code editor--Atom, PyCharm/IntelliJ, Sublime Text, Microsoft Visual Studio Code, and Vim. Right now, Kite supports only Python, but the Kite development team plans to support other languages as well. Kite's code completion is powered by a machine learning model created by scanning publicly available Python code on GitHub.


Getting a grasp on AI and Machine Learning in cyber security

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If you follow technology at all, it's pretty hard to avoid the hearing about "AI" and "machine learning." And it can be almost as difficult to understand what is actually being discussed when these words are used. "The term'AI' is thrown around so readily, and I think for many people, it conjures up the image of'artificial general intelligence,' or some form of self-thinking software," Andy Patel, Senior Researcher at F-Secure's Artificial Intelligence Center of Excellence, told me. "This, combined with massive hype and over-sensationalized or over-exaggerated headlines in the news, and claims from marketers, has caused a general lack of understanding of what machine learning really is right now, and what it is and isn't capable of." Andy attributes this common confusion about the use of data analysis for automated model building known as "machine learning" to a simple fact: most people get their information on the subject from the news.


Behind the scenes: powering AI transformation with High Performance Computing

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It's no exaggeration to say that as a disruptive technology, Artificial Intelligence (AI) is set to change the world – and is already making a positive mark on industries such as healthcare, automotive, gaming and manufacturing. So much so that in this year's Founders' Letter, Google co-founder Sergey Brin described "the new spring in artificial intelligence" as "the most significant development in computing" in his lifetime. Its proliferation is an inevitable consequence of years of ongoing research, refinement and application. Underpinning all of this change – and often overlooked – is High Performance Computing (HPC) and the programmers who use it to power their AI and Machine Learning (ML) applications. AI is not without its doubters, and much of the conversation around its increasing adoption has so far focused on its potentially negative impact on employment.


AI and the Young Attorney: What to Prepare for and How to Prepare

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In business today, AI is a shorthand used to refer to technological processes that automate services. Attorneys will generally encounter two kinds of AI: reactive and limited memory. Reactive AIs respond to human input using predetermined algorithms, like playing chess against a computer. Limited memory AIs rely on both preprogrammed inputs and the AI's own observations over time, like self-driving cars, natural language processors (e.g., Siri), and machine learning. The most popular among them, and the most disruptive for the legal industry, is machine learning.


Why a computer may be your next manager

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There's a new drinking game that is sweeping across after-work corporate watering holes. Everyone takes turns guessing how long it will be until their job is automated out of existence. After every guess, everyone drinks. There is a steady drumbeat of news and analysis that predict a certain demise of much of modern work. You could even put my last CIO article, "The'future of work' in the digital era may not be what you think," in that category.


10 Reasons to Learn Python in 2019 - DZone Big Data

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If you follow my blog regularly then you may be wondering why am I writing an article to tell people to learn Python? Didn't I ask you to prefer Java over Python a couple of years ago? Well, things have changed a lot since then. In 2016, Python replaced Java as the most popular language in colleges and universities and has never looked back. Python is growing big time.