Data Frames are a way to represent tabular data, that is widely used and useful for Statistical Learning. Basically, a Data Frame Tabular data Named columns, and there are different implementations of this data structure, notably in R, Python and Apache Spark. The querier exposes a query language to retrieve data from Python pandas Data Frames, inspired from SQL's relational databases querying. There are 9 types of operations available in the querier, with no plan to extend that list much further (to maintain a relatively simple mental model). These verbs will look familiar to dplyr users, but the implementation (numpy, pandas and SQLite3 are used) and functions' signatures are different: Contributions/remarks are welcome as usual, you can submit a pull request on Github.

Machine learning and deep learning have been widely embraced, and even more widely misunderstood. In this article, I'd like to step back and explain both machine learning and deep learning in basic terms, discuss some of the most common machine learning algorithms, and explain how those algorithms relate to the other pieces of the puzzle of creating predictive models from historical data. Recall that machine learning is a class of methods for automatically creating models from data. Machine learning algorithms are the engines of machine learning, meaning it is the algorithms that turn a data set into a model. Which kind of algorithm works best (supervised, unsupervised, classification, regression, etc.) depends on the kind of problem you're solving, the computing resources available, and the nature of the data.

The artificial voices of Siri, Alexa, or Google, and their often awkward responses, leave no room for doubt that we are not talking to a real person. The latest technological breakthroughs that combine artificial intelligence with deceptively realistic human voices now make it possible for bots to pass themselves off as humans. This has led to new ethical issues: Is bots' impersonation of humans a case of deception? Previous research has shown that humans prefer not to cooperate with intelligent bots. But if people do not even notice that they are interacting with a machine and cooperation between the two is therefore more successful, would it not make sense to maintain the deception in some cases?

The artificial voices of Siri, Alexa, or Google, and their often awkward responses, leave no room for doubt that we are not talking to a real person. The latest technological breakthroughs that combine artificial intelligence with deceptively realistic human voices now make it possible for bots to pass themselves off as humans. This has led to new ethical issues: Is bots' impersonation of humans a case of deception? Previous research has shown that humans prefer not to cooperate with intelligent bots. But if people do not even notice that they are interacting with a machine and cooperation between the two is therefore more successful, would it not make sense to maintain the deception in some cases?

Artificial General Intelligence won't be with us for at least another 100 years, but former Baidu chief scientist and Google Brain cofounder Andrew Ng argues that AI will radically alter most industries within the coming decades. Ng joins Azeem Azhar to discuss the progress of AI and how it's altering businesses and the future of work. HBR Presents is a network of podcasts curated by HBR editors, bringing you the best business ideas from the leading minds in management. The views and opinions expressed are solely those of the authors and do not necessarily reflect the official policy or position of Harvard Business Review or its affiliates.

"Computable Biomedical Knowledge, or CBK, is the result of an analytic and/or deliberative process about human health, or affecting human health, that is explicit and machine-executable, and therefore can be represented and reasoned upon using logic, formal standards, and mathematical approaches." More information can be found in the MCBK Manifesto. It is no longer sufficient to represent health-related knowledge solely in human-readable forms, such as words and images disseminated via books and journal articles. The rapid rate of scientific discovery, the growth of health informatics, and the increasing importance of models and guidelines require health knowledge that is represented in computable forms, as machine-executable code. Computable knowledge unleashes the potential of information technology to generate and deliver relevant health advice to individuals and organizations with great speed on a world-wide scale.

The pace of technology innovation is undeniably moving faster, growing more intelligent, and becoming inescapable. With each advancement, midsize companies are expanding the scope of their digital transformation efforts – from integrating technology into their business to fundamentally changing their workplace culture, organizational operation, and customer experiences. No organization understands this reality better than finance. The IDC InfoBrief, "The Finance Role in Best-Run Midsize Companies: Improving Decision-Making Using Intelligent Technologies," sponsored by SAP, recently revealed that finance organizations in best-run companies are embracing digital transformation and intelligent technologies such as artificial intelligence, predictive analytics, and machine learning. And in return, most of them are improving timely decision-making, running more-efficient and less error-prone operational processes, and empowering knowledge workers to engage in higher-value business activities.

LivePerson's conversational commerce platform LiveEngage enables customer experience hubs to leverage AI to manage both bot and associate conversations from any messaging channel, including the billions of customers using SMS, Facebook Messenger, Apple Business Chat, WeChat, WhatsApp, and more. TTEC is operationalising this platform with its conversational messaging centre of excellence, consisting of conversational designers, AI/bot developers, conversation analysts and tuners, data scientists, solution architects and brand ambassadors in concert with clients at all of TTEC's 100 customer experience hubs across six continents.

In India, healthcare is an issue not because it's hard for people of lower economic status to afford. Often times, it's hard to even find a doctor or hospital in a remote rural area. That's why this particular piece of new technology could be incalculably valuable. Shivanshu Mathur and Raghav Jain, two students pursuing their BTech in Computer Science Engineering at Lovely Professional University, have won the second prize at the NEC India Hackathon 2019 organized by HackerEarth. They received a cash prize of Rs. 1.5 lakh for developing a software they call Medikare.

What does this mean in real terms? Well, it ability that as AI turns into greater educated at examining and decoding mountains of data, and doing so faster than ever before, companies have the chance to build better, more personalised and worthwhile campaigns. It means that as AI apps harvest patterns and insights from text, numbers, nevertheless photographs and video, you can higher apportion budget and resources based on the insights gleaned from the advancing field of artificial Genius and deep learning. As properly as supporting to improve decision-making, AI integration offers the manageable for automated decision-making, helpfully taking decisions out of the palms of patron service reps, consultants or in-house strategists. Of course, it is necessary to offer transparency to clients and to reveal judgements and make certain they are, well, higher than these which would be taken by way of personnel members.