AI & Machine Learning

Artificial intelligence is widely viewed as likely to usher in the next big step-change in computing, but a recent interesting development in the field has particular implications for open source. It concerns the rise of “ethical” AI.  It’s long been accepted that the creators of open-source projects cannot stop their code from being used for purposes with which they may not agree or even strongly condemn—that’s why it’s called free software. What exactly does the rise of “ethical” AI imply for the Open Source world, and how should the community respond?

Matplotlib is one of the most used python libraries to visualize and explore data. It enables you to draw different types of graphs, like line, scattered, bar, and so on. You could say that it is the de-facto standard library for plotting data with python. Matplotlib can be used in Python scripts, Python and IPython shell’s, Jupiter notebook’s, web application servers, and four graphical user interface toolkits. It enables you to generate a visual representation of data with just a few lines of code.

In computer-aided processing of natural languages, shall the concept of natural language processing give way to natural language understanding? Or is the relation between the two concepts subtler and more complicated that merely linear progressing of a technology? Though sometimes used interchangeably, they are actually two different concepts that have some overlap. NLP and NLU are opposites of a lot of other data mining techniques. In this post, we’ll scrutinize over the concepts of NLP and NLU and their niches in the AI-related technology.

We used to build confidence in technology via testing and certification. In order to be convincing to your customers, CEO, board, employees, and social responsibility leaders, testing and an ethical framework will be required. While ethical frameworks can seem daunting, there are simple ways to begin. Autonomous and intelligent systems (A/IS) deployment is accelerating, and managing the risks will be fundamentally different from previous technology waves. Technology adoption in business is routine. But A/IS is fundamentally different from prior generations of technology.

With the incorporation of smart and intelligent methodologies in financial management, the financial advisory industry is progressing towards getting entitled as smart financial services. This demand also is largely promoting growth in the global smart advisor market. There are many technologies that are supporting smart financial advisors in the smart advisors market. One such prominent technology is artificial intelligence or AI. The technology is making dramatic growth of efficiency and quality in the advisory business.

In farming, AI is usually short for “artificial insemination.” But a different kind of AI, artificial intelligence,  is showing great promise in solving some of agriculture’s most significant challenges, from the need to increase productivity and profits to overcoming labor shortages to protecting the environment. AI can help smaller farms be more profitable by scrutinizing plant data to create a “profit map” that tells farmers the most efficient ways to use a field to maximize profit and yield. Each step, from knowing when to plant, to all aspects of crop care, to knowing when to harvest can be automated.

Reinforcement learning, the special AI technique is considered by many the holy grail of artificial intelligence, because it can create autonomous systems that truly self-learn tasks without human intervention though things are a bit more complicated in reality. While machine learning and its more advanced subset deep learning, can solve many problems that were previously thought to be out of bounds for computers, they are dependent on vast amounts of quality, annotated training data. This makes their application limited in domains where labeled data is scarce. This is where reinforcement learning comes into play.

As more and more systems leverage ML models in their decision-making processes, it will become increasingly important to consider how malicious actors might exploit these models, and how to design defenses against those attacks. There’s a continual arms race between attacks and defenses. So what’s an average ML practitioner to do, who likely doesn’t have time to stay on the very cutting of ML security literature? The purpose of this post is to share some of my recent learnings on this topic.

Most information out there in the process of learning the ins-and-outs of machine learning is technical and aimed at developers or data scientists. I thought an explanation from a non-technical person might be of interest. AI and machine learning are fascinating but can be tricky at times. Machine learning is the branch of AI that explores ways to get computers to improve their performance based on experience. There are many different models that can be used in machine learning but they are typically grouped into three different types of learning.

Machine learning is different than other technological advancements; it is not a plug and play solution, at least not yet. Machine learning can be used to tackle a lot of situations and each situation requires a specific data set, model, and parameters to produce valuable results. Most businesses recognize that machine learning can generate exceptional value but many still wonder how, in what specific areas, and if the time is right to integrate it within their data strategy. Today, we explore what questions you should be asking to know if machine learning is right for your business.

Despite a great deal of lip service and a small amount of capital invested, most corporations are still not data-driven, nor do they use machine learning (ML) and artificial intelligence (AI) to guide their strategic investments in business models. Companies are finally embracing analytics, but still have shown little appetite to be data driven, let alone use ML and AI to help them understand the key drivers of value in today’s highly competitive environment: capital allocation and business model design.