- Are Tera Operations Per Second (TOPS) Just hype? Or Dark AI Silicon in Disguise? - May 27, 2020.
This article explains why TOPS isn’t as accurate a gauge as many people think, and discusses other criteria that should be considered when evaluating a solution to a real application.
- What is the most important question for Data Science (and Digital Transformation) - Dec 31, 2019.
With so many buzzwords surrounding AI and machine learning, understanding which can bring business value and which are best left in the lab to mature is difficult. While machine learning offers significant power in driving digital transformations, a business must start with the right questions and leave the math to the development teams.
- Accuracy Fallacy: The Media’s Coverage of AI Is Bogus - Dec 6, 2019.
Such as the gross exaggerations Stanford researchers broadcasted about their infamous "AI gaydar" project, there exists a prevalent "accuracy fallacy" in relation to AI from the media. Find out more about how the press constantly misleads the public into believing that machine learning can reliably predict psychosis, heart attacks, sexuality, and much more.
- On the sensationalism of artificial intelligence news - Nov 15, 2019.
With artificial intelligence and machine learning now a mainstay of our daily awareness, news organizations have been seen to overstate the reality behind progress in the field. Learn more about recent examples of media hyperbole and explore why this may be happening.
- Artificial Intelligence Is Not Intelligence – Interview With Andy Cotgreave (Keynote Speaker at Crunch Conf) - Aug 20, 2019.
Crunch is coming to Budapest, Hungary on 16-18 Oct. Use code KDNuggets to save on Data Science, Data Engineering, or BI tracks. But first, read this interview with keynote speaker Andy Cotgreave.
- OpenAI’s GPT-2: the model, the hype, and the controversy - Mar 4, 2019.
OpenAI recently released a very large language model called GPT-2. Controversially, they decided not to release the data or the parameters of their biggest model, citing concerns about potential abuse. Read this researcher's take on the issue.
- KDnuggets™ News 19:n05, Jan 30: Your AI skills are worth less than you think; 7 Steps to Mastering Basic Machine Learning - Jan 30, 2019.
Also: Logistic Regression: A Concise Technical Overview; AI is a Big Fat Lie; How To Fine Tune Your Machine Learning Models To Improve Forecasting Accuracy; Airbnb Rental Listings Dataset Mining; Data Science Project Flow for Startups
- The year in AI/Machine Learning advances: Xavier Amatriain 2018 Roundup - Jan 11, 2019.
A summary of the main machine learning advances from 2018, including AI hype cooling down, interpretability, deep learning, NLP, and more.
- 4 Myths of Big Data and 4 Ways to Improve with Deep Data - Jan 9, 2019.
There is a fundamental misconception that bigger data produces better machine learning results. However bigger data lakes / warehouses won’t necessarily help to discover more profound insights. It is better to focus on data quality, value and diversity not just size. "Deep Data" is better than Big Data.
- AI, Data Science, Analytics Main Developments in 2018 and Key Trends for 2019 - Dec 3, 2018.
Review of 2018 and Predictions for 2019 from our panel of experts, including Meta Brown, Tom Davenport, Carla Gentry, Bob E Hayes, Cassie Kozyrkov, Doug Laney, Bill Schmarzo, Kate Strachnyi, Ronald van Loon, Favio Vazquez, and Jen Underwood.
- Things you should know when traveling via the Big Data Engineering hype-train - Oct 8, 2018.
Maybe you want to join the Big Data world? Or maybe you are already there and want to validate your knowledge? Or maybe you just want to know what Big Data Engineers do and what skills they use? If so, you may find the following article quite useful.
- Google’s AutoML: Cutting Through the Hype - Jul 31, 2018.
In today’s post, I want to look specifically at Google’s AutoML, a product which has received a lot of media attention, and address "What is Google's AutoML?" and more.
- The Current Hype Cycle in Artificial Intelligence - Feb 28, 2018.
Over the past decade, the field of artificial intelligence (AI) has seen striking developments. As surveyed in, there now exist over twenty domains in which AI programs are performing at least as well as (if not better than) humans.
- The New Neural Internet is Coming - Feb 23, 2018.
The Generative Adversarial Networks (GANs) are the first step of neural networks technology learning creativity.
- The Birth of AI and The First AI Hype Cycle - Feb 13, 2018.
A dazzling review of AI History, from Alan Turing and Turing Test, to Simon and Newell and Logic Theorist, to Marvin Minsky and Perceptron, birth of Rule-based systems and Machine Learning, Eliza - first chatbot, Robotics, and the bust which led to first AI Winter.
- Industry Predictions: Main AI, Big Data, Data Science Developments in 2017 and Trends for 2018 - Dec 19, 2017.
Here is a treasure trove of analysis and predictions from 17 leading companies in AI, Big Data, Data Science, and Machine Learning: What happened in 2017 and what will 2018 bring?
- More than the Hype: Beyond Gartner’s Hype Cycle - Nov 3, 2017.
Gartner publishes hype cycles across different technologies and sectors. Here we conduct detailed analysis of Gartner’s Hype Cycles.
- Credible Sources of Accurate Information About AI - Oct 9, 2017.
I want to recommend several credible sources of accurate information. Most of the writing on this list is intended to be accessible to anyone—even if you aren’t a programmer or don’t work in tech.
- I built a chatbot in 2 hours and this is what I learned - Sep 7, 2017.
I set out to test two things: 1) building a bot is useless from a business perspective and 2) building bots is crazy tough. Here is what I learned.
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- When not to use deep learning - Jul 24, 2017.
Despite DL many successes, there are at least 4 situations where it is more of a hindrance, including low-budget problems, or when explaining models and features to general public is required.
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- Connecting with the Internet of Things - Jul 6, 2017.
If you’re like me, you've heard a lot about the Internet of Things (IoT) but are confused about what it really is.
- Putting Alexa to Work: Moving Conversational UI from Hype to Reality - Apr 19, 2017.
The rise of conversational UI signals exciting progress for the BI world but there are pitfalls to be avoided. This blog presents 3 considerations for guiding your conversational UI implementation to ensure success and maximize the value of your data analytics.
- AI is not at all like Mobile/Cloud/SaaS - Feb 10, 2017.
AI is a hard problem and will take much longer to solve in any scope. The sudden uptick in interest may revert back to normal, but the cycle of work will be longer, much more diverse, and interesting than Mobile/Cloud/SaaS.
- Machine Learning: Separating Hype From Reality - Jul 22, 2016.
When it comes to business value and ROI, does machine learning live up tot he claims? We’ll explore a pure machine learning approach through the lens of a typical enterprise use case.
- Deep Learning is not Enough - Feb 9, 2016.
Deep Learning has real successes, but is not enough to reach artificial intelligence, according to latest KDnuggets Poll. For more complex problems, should pure neural-net approaches be combined with symbolic, knowledge-based methods?
- KDnuggets™ News 16:n04, Feb 3: Is Deep Learning Overhyped? Businesses Will Need 1M Data Scientists - Feb 3, 2016.
New Poll: Deep Learning - does reality match the hype?; Is Deep Learning Overhyped?; Businesses Will Need One Million Data Scientists by 2018; KDnuggets New Responsive, Mobile-Friendly Design.
- New Poll: Deep Learning – does reality match the hype? - Jan 29, 2016.
New KDnuggets Poll looks at the very hot field of Deep Learning and asks: does reality match the hype? Please vote!
- Is Deep Learning Overhyped? - Jan 29, 2016.
With all of the success that deep learning is experiencing, the detractors and cheerleaders can be seen coming out of the woodwork. What is the real validity of deep learning, and is it simply hype?
- KDnuggets™ News 15:n29, Sep 2: How to become a Data Scientist for Free; Big Data Out, Machine Learning In - Sep 2, 2015.
How to become a Data Scientist for Free; Gartner 2015 Hype Cycle: Big Data is Out, Machine Learning is In; KDnuggets part-time internship in Data Science, Data Journalism; The one language a Data Scientist must master.
- Essays On Statistics Denial - May 20, 2015.
Statistics denial comes in waves as areas of application discover and rediscover the potential of data insights. We examine the statistics denial myths and where they come from.