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) …
Other organizations can leverage business data to drive data-informed project management, allowing business leaders to more accurately determine how long certain operations may take and will cost. The fundamentals of these technologies are rooted in data-driven algorithms that enable machines to develop learned responses or predictive capabilities. As a result, with AI and machine learning comes data--big data--that requires resources to be allocated, not only specialists like programmers, but additional on-premises resources such as storage, server CPUs, networking bandwidth, and cloud-hosted storage services. As businesses look to develop their digital transformation strategies and create unique competitive advantage, AI and machine learning are increasingly considered the keys to unlocking the value of an organization's accumulated data.
First of all, I want to highlight something that seems to go missing in the current hype. There is a lot more to machine learning than deep convolutional neural nets and recurrent nets. I don't want to diminish the contribution of those methods, there have been some really cool breakthroughs in these domains. However, they rely on very large data and massive computational capabilities. However, many problems involve less data and even when there is large data it can be the case that the computational demands of conv-nets and recurrent networks are too high (just like they were 20 years ago when these methods were first proposed!).
Machine Learning is most often considered a branch of the broad pursuit of Artificial Intelligence in which it is used to process unstructured data, such as text. But there is an even greater potential for its application in enhancing analytics of structured numerical data. In this domain, we predict Machine Learning capabilities will continue to offer further insights by discovering patterns in our extensive data set of more than 4.2 billion observations of software development revisions. Machine Learning offers an extension of the sophistication of data analytics, from automating analyses that our statisticians carry out, to discovering patterns that humans cannot. For example, our data scientists recognise that a software application that is no longer being worked on is likely to be no longer in use and can be retired.
Artificial intelligence and machine learning is suddenly all the rage, and for good reason. It is the future of this, and every other industry. If you've been paying attention to the evolution of technology over the past 2.6 million years, you knew it was coming. Wherever the bulk of the effort has been shouldered by human beings, we have always sought to replace us with technology that could do the job better, faster, more efficiently and, since the invention of capital, cheaper. It began with the most basic, brute force physical tasks and has progressively involved more nuanced, cognitive processes.
For most of the businesses, the role of data in planning, operations and strategy is not just about competitive differentiator, but more about competitive necessity to survive in today's cutthroat business ecosystem. Computer and data driven (predictive) analytics are extensively powering most of the critical business decisions in finance, marketing, customer support and sales. However, data analytics today doesn't come into action when it comes to managing people and making decisions as how we attract, grow, retain and motivate our people. Also, many companies refrain from using data for addressing critical concerns like which team is likely to have performance problems and the reasons behind those issues? How to improve managerial efficiency?
If popular culture is an accurate gauge of what's on the public's mind, it seems everyone has suddenly awakened to the threat of smart machines. Several recent films have featured robots with scary abilities to outthink and manipulate humans. In the economics literature, too, there has been a surge of concern about the potential for soaring unemployment as software becomes increasingly capable of decision making. Yet managers we talk to don't expect to see machines displacing knowledge workers anytime soon -- they expect computing technology to augment rather than replace the work of humans. In the face of a sprawling and fast-evolving set of opportunities, their challenge is figuring out what forms the augmentation should take.
Using machine learning to predict (& prevent!) injuries in endurance athletes: Part 1 Alan Couzens, M.Sc. I've talked about different ways that we can assess the individual'dose- response' relationship or, more specifically, how we can work out just "what it takes" for a given athlete to reach a given performance level. I have suggested that, given recent advances in machine learning, the current models are largely out-dated and that we can find more accuracy in models that look at the independent impact of volume & intensity rather than wrapping these variables into one'training stress' metric. But there is another addition to the current performance models that is far more important and has the potential to be even more powerful in its application than load- fitness modeling: Turning the focus of our models to those things that prevent us from ultimately doing more load! This is the flipside of the'more is better' dose- response model.
One mantra we chant frequently is "trust the data". In the context that we use this expression it is often wise. For example: when requested for the facility to adjust the rules of a robustly tested machine-learnt model so that it better jibes with intuition; or when tempted to cherry-pick fields and features which one assumes (be it through years of domain experience or otherwise) enshrine the relevant information. This doesn't mean that the data is always right of course. Certainly weeding out certain kinds of systematic error from the data is essential: a common example of this might be wholesale differences between records created before and after the point that a database was migrated or a new process was introduced.
With growing interest in neural networks and deep learning, individuals and companies are claiming ever-increasing adoption rates of artificial intelligence into their daily workflows and product offerings. Coupled with breakneck speeds in AI-research, the new wave of popularity shows a lot of promise for solving some of the harder problems out there. That said, I feel that this field suffers from a gulf between appreciating these developments and subsequently deploying them to solve "real-world" tasks. A number of frameworks, tutorials and guides have popped up to democratize machine learning, but the steps that they prescribe often don't align with the fuzzier problems that need to be solved. This post is a collection of questions (with some (maybe even incorrect) answers) that are worth thinking about when applying machine learning in production.
The number of sophisticated cognitive technologies that might be capable of cutting into the need for human labor is expanding rapidly. But linking these offerings to an organization's business needs requires a deep understanding of their capabilities. If popular culture is an accurate gauge of what's on the public's mind, it seems everyone has suddenly awakened to the threat of smart machines. Several recent films have featured robots with scary abilities to outthink and manipulate humans. In the economics literature, too, there has been a surge of concern about the potential for soaring unemployment as software becomes increasingly capable of decision making.