- New Computing Paradigm for AI: Processing-in-Memory (PIM) Architecture - Oct 15, 2021.
As larger deep neural networks are trained on the latest and fastest chip technologies, an important challenge remains that bottlenecks performance -- and it is not compute power. You can try to calculate a DNN as fast as possible, but there is data -- and it has to move. Data pipelines on the chip are expensive and new solutions must be developed to advance capabilities.
- My AI Plays Piano for Me - Oct 6, 2021.
Training an RNN with a Combined Loss Function.
- KDnuggets™ News 21:n38, Oct 6: Build a Strong Data Science Portfolio; Surpassing Trillion Parameters with Switch Transformers — a path to AGI? - Oct 6, 2021.
How to Build Strong Data Science Portfolio as a Beginner; Surpassing Trillion Parameters and GPT-3 with Switch Transformers — a path to AGI?; How Deep Is That Data Lake?; Data Science Process Lifecycle; Use These Unique Data Sets to Sharpen Your Data Science Skills; How to Auto-Detect the Date/Datetime Columns and Set Their Datatype When Reading a CSV File in Pandas
- Will Data Analysts be Replaced by AI? - Oct 5, 2021.
It's the question so many are asking: will data analysts be replaced by AI? Read this well-reasoned and concise opinion by someone with insight into the matter.
- Teaching AI to Classify Time-series Patterns with Synthetic Data - Oct 1, 2021.
How to build and train an AI model to identify various common anomaly patterns in time-series data.
- Scale and Govern AI Initiatives with ModelOps - Sep 30, 2021.
AI/ML model life cycle automation and orchestration ensures reliable model operations and governance at scale. The path to production for each enterprise model can vary, along with different monitoring, continuous improvement, retirement needs. Organizations must now consider ModelOps as a fundamental capability towards operational excellence and immediate ROIs.
- Computer Vision in Agriculture - Sep 27, 2021.
Deep learning isn’t just for placing ads or identifying cats anymore. Instead, a slew of young startups have started to incorporate the advances in computer vision made possible through larger and larger neural networks to real working robots in the fields.
- GitHub Copilot and the Rise of AI Language Models in Programming Automation - Sep 22, 2021.
Read on to learn more about what makes Copilot different from previous autocomplete tools (including TabNine), and why this particular tool has been generating so much controversy.
- Nine Tools I Wish I Mastered Before My PhD in Machine Learning - Sep 22, 2021.
Whether you are building a start up or making scientific breakthroughs these tools will bring your ML pipeline to the next level.
- Free virtual event: Big Data and AI Toronto - Sep 21, 2021.
This year’s Big Data and AI Toronto conference and expo, held virtually Oct 13-14, will provide attendees with a 360° view of the industry through a unique 4-in-1 experience: Artificial intelligence, big data, cloud, and cybersecurity.
- How Many AI Neurons Does It Take to Simulate a Brain Neuron? - Sep 13, 2021.
A new research shows some shocking answers to that question.
- Smart Ingestion: Using ontology-driven AI - Sep 8, 2021.
Imagine data that organizes itself to power your decision-making.
- Math 2.0: The Fundamental Importance of Machine Learning - Sep 8, 2021.
Machine learning is not just another way to program computers; it represents a fundamental shift in the way we understand the world. It is Math 2.0.
- KDnuggets™ News 21:n34, Sep 8: Do You Read Excel Files with Python? There is a 1000x Faster Way; Hypothesis Testing Explained - Sep 8, 2021.
Do You Read Excel Files with Python? There is a 1000x Faster Way; Hypothesis Testing Explained; Data Science Cheat Sheet 2.0; 6 Cool Python Libraries That I Came Across Recently; Best Resources to Learn Natural Language Processing in 2021
- Five Key Facts About Wu Dao 2.0: The Largest Transformer Model Ever Built - Sep 6, 2021.
The record-setting model combines some clever research and engineering methods.
- Future Says Series | Discover the Future of AI - Sep 1, 2021.
This innovative project brings together industry thought leaders from top tech companies such as Google, PwC, King, DNB, Piab, Scania, Telefonica, and more to discuss what the future holds for data and AI. Watch Future Says Series as industry experts discuss real-life examples how they are scaling AI successfully within their organizations.
- The Significance of Data-centric AI - Aug 27, 2021.
How a systematic way of maintaining data quality can do wonders to your model performance.
- Coding Ethics for AI & AIOps: Designing Responsible AI Systems - Aug 26, 2021.
AI ops has taken Human machine collaboration to the next level where humans and machines are not just coexisting but are collaborating and working together like team members.
- Jurassic-1 Language Models and AI21 Studio - Aug 23, 2021.
AI21 Labs’ new developer platform offers instant access to our 178B-parameter language model, to help you build sophisticated text-based AI applications at scale.
- Demystifying AI: The prejudices of Artificial Intelligence (and human beings) - Aug 20, 2021.
AI models are necessarily trained on historical data from the real-world--data that is generated from the daily goings on of society. If social-based biases are inherent in the training data, then will the AI predictions highlight these same biases? If so, what should we do (or not do) about making AI fair?
- AI in Real Life - Aug 10, 2021.
What do you need to get started on your AI journey? Putting together a combination of the right project, people and infrastructure is no easy task. SAS and MIT SMR have collaborated to provide a comprehensive set of resources to guide you from conception to implementation. Learn from experts that successfully launched AI projects.
- Including ModelOps in your AI strategy - Aug 9, 2021.
The strategic power of AI has been established thoroughly across many industries and companies, leading to surges in model creation. Investments in the people, processes, and tools for operationalizing models, referred to as ModelOps, lag. This function of operationalizing, integrating, and deploying AI models in line with businesses value expectations is growing into a core business capability as global use of AI matures.
- Artificial Intelligence vs Machine Learning in Cybersecurity - Aug 5, 2021.
Artificial Intelligence and Machine Learning are the next-gen technology used in various fields. With the rise in online threats, it has become essential to include these technologies in cybersecurity. In this post, we will know what roles do AI and ML play in cybersecurity.
- How DeepMind Trains Agents to Play Any Game Without Intervention - Aug 4, 2021.
A new paper proposes a new architecture and training environment for generally capable agents.
- Towards a Responsible and Ethical AI - Jul 30, 2021.
It is not the technology at fault, but the intention.
- GitHub Copilot Open Source Alternatives - Jul 29, 2021.
GitHub's Copilot code generation tool is currently only available via approved request. Here are 4 Copilot alternatives that you can use in your programming today.
- An AI-Based Framework Solution to Address Email Management Challenges - Jul 28, 2021.
Expert.ai’s Edge NL API is an on-premise API that can perform NLU tasks with no required training or extra work, offering advanced, out-of-the-box capabilities that address common use cases and can be easily customized to your specific needs.
- ARTIFICIAL INTELLIGENCE (AI), A TEXTBOOK - Jul 27, 2021.
This book covers the broader field of AI, carefully balancing coverage between classical AI (logic or deductive reasoning) and modern AI (inductive learning and neural networks).
- How to Create Unbiased Machine Learning Models - Jul 16, 2021.
In this post we discuss the concepts of bias and fairness in the Machine Learning world, and show how ML biases often reflect existing biases in society. Additionally, We discuss various methods for testing and enforcing fairness in ML models.
- AGI and the Future of Humanity - Jul 13, 2021.
The possibilities for humanity's future very likely includes at least one in which computers will exceed human abilities. Artificial General Intelligence (AGI) does not necessarily have to be all doom and gloom. However, we must begin now to understand how this technical evolution might progress and consider what actions to take now to prepare.
- GitHub Copilot: Your AI pair programmer – what is all the fuss about? - Jul 5, 2021.
GitHub just released Copilot, a code completion tool on steroids dubbed your "AI pair programmer." Read more about it, and see what all the fuss is about.
- Ethics, Fairness, and Bias in AI - Jun 30, 2021.
As more AI-enhanced applications seep into our daily lives and expand their reach to larger swaths of populations around the world, we must clearly understand the vulnerabilities trained machine leaning models can exhibit based on the data used during development. Such issues can negatively impact select groups of people, so addressing the ethical decisions made by AI--possibly unknowingly--is important to the long-term fairness and success of this new technology.
- An introduction to Explainable AI (XAI) and Explainable Boosting Machines (EBM) - Jun 16, 2021.
Understanding why your AI-based models make the decisions they do is crucial for deploying practical solutions in the real-world. Here, we review some techniques in the field of Explainable AI (XAI), why explainability is important, example models of explainable AI using LIME and SHAP, and demonstrate how Explainable Boosting Machines (EBMs) can make explainability even easier.
- The Essential Guide to Transformers, the Key to Modern SOTA AI - Jun 10, 2021.
You likely know Transformers from their recent spate of success stories in natural language processing, computer vision, and other areas of artificial intelligence, but are familiar with all of the X-formers? More importantly, do you know the differences, and why you might use one over another?
- The 7 Best Open Source AI Libraries You May Not Have Heard Of - Jun 9, 2021.
AI researchers today have many exciting options for working with specialized tools. Although starting original projects from scratch is often not necessary, knowing which existing library to leverage remains a challenge. This list of generally unknown yet awesome, open-source libraries offers an interesting collection to consider for state-of-the-art research that spans from automatic machine learning to differentiable quantum circuits.
- 5 Tips for Picking an Edge AI Platform - Jun 8, 2021.
Edge Analytics isn’t just coding and tools. The different environment outside the datacenter or cloud means a purpose built platform is the best way to deliver consistent results. We discuss 5 different considerations for an edge platform to support your training and deployment.
- Beyond Brainless AI with a Feature Store - Jun 4, 2021.
AI-powered products that are limited to the data available within its application are like jellyfish: its autonomic system makes it functional, but it lacks a brain. However, you can evolve your models with data enriched "brains" through the help of a feature store.
- AI Books you should read in 2021 - May 27, 2021.
As of late, every year seems to be a "break-out" year for AI. So, it's time for you to get ready for the future in the age of automation. This collection of books will help you prepare for the many opportunities to come, many of which may not have yet been imagined.
- Budgeting For Your AI Training Data: Consider These 3 Factors - May 26, 2021.
Before you even plan to procure the data, one of the most important considerations in determining how much you should spend on your AI training data. In this article, we will give you insights to develop an effective budget for AI training data.
- What Makes AI Trustworthy? - May 11, 2021.
This blog pertains to the importance of why AI needs to be trustworthy as well as what makes it trustworthy. AI predictions/suggestions should not just be taken at face value, but rather delved into at a deeper level. We need to understand how an AI system makes its predictions to put our trust in it. Trust should not be built on prediction accuracy alone.
- The Machine Learning Research Championed by the Biggest AI Labs in the World - May 5, 2021.
How Google, Microsoft, Facebook, DeepMind, OpenAI, Amazon and IBM think about the future of AI.
- Disentangling AI, Machine Learning, and Deep Learning - May 4, 2021.
The field of Artificial Intelligence is extremely broad and captures a winding history through the evolution of various sub-fields that experienced many ups and downs over the years. Appreciating AI within its historical contexts will enhance your communication with the public, colleagues, and potential hiring managers, as well as guide your thinking as you progress in the application and study of state-of-the-art techniques.
- FluDemic – using AI and Machine Learning to get ahead of disease - Apr 30, 2021.
We are amidst a healthcare data explosion. AI/ML will be more vital than ever in the prevention and handling of future pandemics. Here, we walk you through the different facets of modeling infectious diseases, focusing on influenza and COVID-19.
- Best Podcasts for Machine Learning - Apr 28, 2021.
Podcasts, especially those featuring interviews, are great for learning about the subfields and tools of AI, as well as the rock stars and superheroes of the AI world. Here, we highlight some of the best podcasts today that are perfect for both those learning about machine learning and seasoned practitioners.
- Getting Started with Reinforcement Learning - Apr 26, 2021.
Demystifying some of the main concepts and terminologies associated with Reinforcement Learning and their association with other fields of AI.
- What is Adversarial Neural Cryptography? - Apr 22, 2021.
The novel approach combines GANs and cryptography in a single, powerful security method.
- Shaping the new digital age – with SAS and Microsoft - Apr 13, 2021.
Join technology experts, partners and analysts in the industry for this webinar series to see how SAS Viya can help you make the most of AI, analytics and the cloud for faster decisions and trusted results.
- 10 Real-Life Applications of Reinforcement Learning - Apr 12, 2021.
In this article, we’ll look at some of the real-world applications of reinforcement learning.
- Interpretable Machine Learning: The Free eBook - Apr 9, 2021.
Interested in learning more about interpretability in machine learning? Check out this free eBook to learn about the basics, simple interpretable models, and strategies for interpreting more complex black box models.
- Deepfakes are now mainstream. What’s next? - Apr 9, 2021.
Deepfakes have become mainstream. Here we take a closer look at recent news about deepfakes, and what it all might mean for the future.
- What did COVID do to all our models? - Apr 2, 2021.
An interview with Dean Abbott and John Elder about change management, complexity, interpretability, and the risk of AI taking over humanity.
- Introduction to the White-Box AI: the Concept of Interpretability - Mar 31, 2021.
ML models interpretability can be seen as “the ability to explain or to present in understandable terms to a human.” Read this article and learn to go beyond the black box of AI, where algorithms make predictions, toward the underlying explanation remains unknown and untraceable.
- Teaching AI to See Like a Human - Mar 22, 2021.
DeepMind Generative Query Networks can infer knowledge as they navigate a visual environment.
- AI in Dating: Can Algorithms Help You Find Love? - Mar 19, 2021.
Can AI algorithms help us find love? Can they go a step further and replace a human being as a partner in a relationship? Here, we analyze how far technology has come in helping us meet "our" people, find love, and feel less lonely.
- AI Industry Innovation: Making the Invisible Visible - Mar 12, 2021.
AI Accelerator Festival: Hardware Acceleration for AI at the Edge . The world's only end-user led event dedicated to accelerating industries by harnessing the power of AI. March 16-19, 2021.
- DeepMind’s AlphaFold & the Protein Folding Problem - Mar 10, 2021.
Recently, DeepMind's AlphaFold made impressive headway in the protein structure prediction problem. Read this for an overview and explanation.
- KDnuggets™ News 21:n10, Mar 10: More Resources for Women in AI, Data Science, and Machine Learning; Speeding up Scikit-Learn Model Training - Mar 10, 2021.
More Resources for Women in AI, Data Science, and Machine Learning; Speeding up Scikit-Learn Model Training; Dask and Pandas: No Such Thing as Too Much Data; 9 Skills You Need to Become a Data Engineer; 8 Women in AI Who Are Striving to Humanize the World
- Is It Too Late to Learn AI? - Mar 9, 2021.
Have you missed the train on learning AI?
- 8 Women in AI Who Are Striving to Humanize the World - Mar 8, 2021.
Some exceptional female researchers and engineers are working on projects to make the world a better place with the help of AI, data science, and machine learning.
- More Resources for Women in AI, Data Science, and Machine Learning - Mar 8, 2021.
Useful resources to help more women enter and succeed in AI, Data Science, and Machine Learning fields.
- Adversarial Attacks on Explainable AI - Feb 9, 2021.
Are explainability methods black-box themselves?
- 2011: DanNet triggers deep CNN revolution - Feb 4, 2021.
In 2021, we are celebrating the 10-year anniversary of DanNet, which, in 2011, was the first pure deep convolutional neural network (CNN) to win computer vision contests. Read about its history here.
- Adversarial generation of extreme samples - Feb 2, 2021.
In order to mitigate risks when modelling extreme events, it is vital to be able to generate a wide range of extreme, and realistic, scenarios. Researchers from the National University of Singapore and IIT Bombay have developed an approach to do just that.
- Deep Learning Pioneer Geoff Hinton on his Latest Research and the Future of AI - Jan 26, 2021.
Geoff Hinton has lived at the outer reaches of machine learning research since an aborted attempt at a carpentry career a half century ago. He spoke to Craig Smith about his work In 2020 and what he sees on the horizon for AI.
- How to Use MLOps for an Effective AI Strategy - Jan 21, 2021.
The need to deal with the challenges and other smaller nuances of deploying machine learning models has given rise to the relatively new concept of MLOps. – a set of best practices aimed at automating the ML lifecycle, bringing together the ML system development and ML system operations.
- Going Beyond the Repo: GitHub for Career Growth in AI & Machine Learning - Jan 21, 2021.
Many online tools and platforms exist to help you establish a clear and persuasive online profile for potential employers to review. Have you considered how your go-to online code repository could also help you land your next job?
- Top 5 Artificial Intelligence (AI) Trends for 2021 - Jan 21, 2021.
From voice and language driven AI to healthcare, cybersecurity and beyond, these are some of the key AI trends for 2021.
- Top 10 Computer Vision Papers 2020 - Jan 8, 2021.
The top 10 computer vision papers in 2020 with video demos, articles, code, and paper reference.
- 11 Industrial AI Trends that will Dominate the World in 2021 - Jan 7, 2021.
These trends broadly cover the three themes of: Where will businesses adopt AI in 2021? How will AI become more accessible? How will AI capabilities evolve?
- MLOps: Model Monitoring 101 - Jan 6, 2021.
Model monitoring using a model metric stack is essential to put a feedback loop from a deployed ML model back to the model building stage so that ML models can constantly improve themselves under different scenarios.
- 2020: A Year Full of Amazing AI Papers — A Review - Dec 28, 2020.
So much happened in the world during 2020 that it may have been easy to miss the great progress in the world of AI. To catch you up quickly, check out this curated list of the latest breakthroughs in AI by release date, along with a video explanation, link to an in-depth article, and code.
- Production Machine Learning Monitoring: Outliers, Drift, Explainers & Statistical Performance - Dec 21, 2020.
A practical deep dive on production monitoring architectures for machine learning at scale using real-time metrics, outlier detectors, drift detectors, metrics servers and explainers.
- Navigate the road to Responsible AI - Dec 18, 2020.
Deploying AI ethically and responsibly will involve cross-functional team collaboration, new tools and processes, and proper support from key stakeholders.
- Covid or just a Cough? AI for detecting COVID-19 from Cough Sounds - Dec 15, 2020.
Increased capabilities in screening and early testing for a disease can significantly support quelling its spread and impact. Recent progress in developing deep learning AI models to classify cough sounds as a prescreening tool for COVID-19 has demonstrated promising early success. Cough-based diagnosis is non-invasive, cost-effective, scalable, and, if approved, could be a potential game-changer in our fight against COVID-19.
- How The New World of AI is Driving a New World of Processor Development - Dec 14, 2020.
Blaize’s novel stream processor for Edge AI offers a case study of new opportunities for smaller companies to leverage semiconductor industry resources in pursuit of their goals.
- Facebook Open Sources ReBeL, a New Reinforcement Learning Agent - Dec 14, 2020.
The new model tries to recreate the reinforcement learning and search methods used by AlphaZero in imperfect information scenarios.
- Artificial Intelligence in Modern Learning System : E-Learning - Dec 9, 2020.
There has been a considerable shortage in the supply and demand of AI professionals. If you are looking to learn AI or learn machine learning, you can opt for free online courses offered by Great Learning.
- Main 2020 Developments and Key 2021 Trends in AI, Data Science, Machine Learning Technology - Dec 9, 2020.
Our panel of leading experts reviews 2020 main developments and examines the key trends in AI, Data Science, Machine Learning, and Deep Learning Technology.
- AI registers: finally, a tool to increase transparency in AI/ML - Dec 9, 2020.
Transparency, explainability, and trust are pressing topics in AI/ML today. While much has been written about why they are important and what you need to do, no tools have existed until now.
- AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2020 and Key Trends for 2021 - Dec 3, 2020.
2020 is finally coming to a close. While likely not to register as anyone's favorite year, 2020 did have some noteworthy advancements in our field, and 2021 promises some important key trends to look forward to. As has become a year-end tradition, our collection of experts have once again contributed their thoughts. Read on to find out more.
- Roadmaps to becoming a Full-Stack AI Developer, Data Scientist, Machine Learning Engineer, and more - Dec 2, 2020.
As the fields related to AI and Data Science expand, they are becoming complex with more options and specializations to consider. If you are beginning your journey toward becoming an expert in Artificial Intelligence, this roadmap will guide you to find your path along what to learn next while steering clear of the latest hype.
- KDnuggets™ News 20:n45, Dec 2: TabPy: Combining Python and Tableau; Learn Deep Learning with this Free Course from Yann LeCun - Dec 2, 2020.
Combine Python and Tableau with TabPy; Learn Deep Learning with this Free Course from Yann LeCun; Find 15 Exciting AI Project Ideas for Beginners; Read about the Rise of the Machine Learning Engineer; See How to Incorporate Tabular Data with HuggingFace Transformers
- Remembering Pluribus: The Techniques that Facebook Used to Master World’s Most Difficult Poker Game - Dec 1, 2020.
Pluribus used incredibly simple AI methods to set new records in six-player no-limit Texas Hold’em poker. How did it do it?
- 15 Exciting AI Project Ideas for Beginners - Nov 23, 2020.
There are many branches to AI to learn, but a project-based approach can keep things interesting. Here is a list of 15 such projects you can get started on implementing today.
- AI and Automation meets BI - Nov 19, 2020.
Organizations use a variety of BI tools to analyze structured data. These tools are used for ad-hoc analysis, and for dashboards and reports that are essential for decision making. In this post, we describe a new set of BI tools that continue this trend.
- Compute Goes Brrr: Revisiting Sutton’s Bitter Lesson for AI - Nov 19, 2020.
"It's just about having more compute." Wait, is that really all there is to AI? As Richard Sutton's 'bitter lesson' sinks in for more AI researchers, a debate has stirred that considers a potentially more subtle relationship between advancements in AI based on ever-more-clever algorithms and massively scaled computational power.
- AI Is More Than a Model: Four Steps to Complete Workflow Success - Nov 17, 2020.
The key element for success in practical AI implementation is uncovering any issues early on and knowing what aspects of the workflow to focus time and resources on for the best results—and it’s not always the most obvious steps.
- How to use AI & analytics now to prepare for resiliency in 2021 - Nov 11, 2020.
Emerge with Resiliency 2020 is a no-cost virtual event presented by the IBM Planning Analytics and Cognos Community taking place on Nov 18. This one-day event includes 8 expert sessions, during which you’ll learn how IBM solutions can help enhance business continuity, reduce risk from emerging threats, and help you prepare for and manage disruption.
- Overcoming the Racial Bias in AI - Oct 30, 2020.
The results of any AI developed today is entirely dependent on the data on which it trains. If the data is distributed--intentionally or not--with a bias toward any category of data over another, then the AI will display that bias. What is a better way forward to handle this possibility toward bias when the datasets involve human beings?
- Explaining the Explainable AI: A 2-Stage Approach - Oct 29, 2020.
Understanding how to build AI models is one thing. Understanding why AI models provide the results they provide is another. Even more so, explaining any type of understanding of AI models to humans is yet another challenging layer that must be addressed if we are to develop a complete approach to Explainable AI.
- An Introduction to AI, updated - Oct 28, 2020.
We provide an introduction to key concepts and methods in AI, covering Machine Learning and Deep Learning, with an updated extensive list that includes Narrow AI, Super Intelligence, and Classic Artificial Intelligence, as well as recent ideas of NeuroSymbolic AI, Neuroevolution, and Federated Learning.
- Can AI Learn Human Values? - Oct 27, 2020.
OpenAI believes that the path to safe AI requires social sciences.
- The Ethics of AI - Oct 21, 2020.
Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California about a very important subject - the ethics of AI.
- Cartoon: Cloud Dating - Oct 17, 2020.
New KDnuggets cartoon looks at how AI can transform love and romance.
- Getting Started in AI Research - Oct 5, 2020.
A guide on how to contribute to confirming the reproducibility of some of the most recent papers and join open-search research.
- Top KDnuggets tweets, Sep 23-29: An Introduction to #AI – updated for 2020; Master using Pandas for time series analysis - Sep 30, 2020.
An Introduction to #AI - updated for 2020; Free From MIT: Intro to Computer Science and Programming in Python; The Most Complete Guide to #PyTorch for Data Scientists; (Good) Data Cleaning is just reusable Data Transformations
- AI in Healthcare: A review of innovative startups - Sep 30, 2020.
The AI innovation in healthcare has been overwhelming with the Global Healthcare AI Market accounting for $0.95 billion in 2017, and is expected to reach $19.25 billion by 2026. What drives this vibrant growth?
- How AI is Driving Innovation in Astronomy - Sep 29, 2020.
In this blog, we look at a disruptive AI program - Morpheus - developed by Researchers at UC Santa Cruz that can analyze astronomical image data and classify galaxies and stars with surgical precision. If you're reading this with "starry" eyes, we bet we've got you hooked.
- Artificial Intelligence for Precision Medicine and Better Healthcare - Sep 23, 2020.
In this article, we will focus on various machine learning, deep learning models, and applications of AI which can pave the way for a new data-centric era of discovery in healthcare.
- Can Neural Networks Show Imagination? DeepMind Thinks They Can - Sep 16, 2020.
DeepMind has done some of the relevant work in the area of simulating imagination in deep learning systems.
- Online Certificates/Courses in AI, Data Science, Machine Learning from Top Universities - Sep 16, 2020.
We present the online courses and certificates in AI, Data Science, Machine Learning, and related topics from the top 20 universities in the world.
- Big Data and AI Toronto Goes Virtual - Sep 14, 2020.
The Big Data and AI Toronto Conference and Expo returns on September 29-30, 2020 with a brand new format and will be held exclusively online. KDnuggets readers get a 25% discount on all-access passes with promo code BDTORONTO-25. Register now.
- AI Papers to Read in 2020 - Sep 10, 2020.
Reading suggestions to keep you up-to-date with the latest and classic breakthroughs in AI and Data Science.
- 8 AI/Machine Learning Projects To Make Your Portfolio Stand Out - Sep 9, 2020.
If you are just starting down a path toward a career in Data Science, or you are already a seasoned practitioner, then keeping active to advance your experience through side projects is invaluable to take you to the next professional level. These eight interesting project ideas with source code and reference articles will jump start you to thinking outside of the box.
- Showcasing the Benefits of Software Optimizations for AI Workloads on Intel® Xeon® Scalable Platforms - Sep 1, 2020.
The focus of this blog is to bring to light that continued software optimizations can boost performance not only for the latest platforms, but also for the current install base from prior generations. This means customers can continue to extract value from their current platform investments.
- A Curious Theory About the Consciousness Debate in AI - Aug 31, 2020.
Dr. Michio Kaku has formulated a very interesting theory of consciousness that applies to AI systems.
- Beyond the Turing Test - Aug 28, 2020.
With more advancements in AI, it might be time to replace the age-old Turing Test with something better to determine if a machine is thinking. Specifically, a more modern approach might include standard questions designed to probe various facets of intelligence, and comparing the computer to a spectrum of human respondents of different ages, sexes, backgrounds, and abilities.
- DeepMind’s Three Pillars for Building Robust Machine Learning Systems - Aug 24, 2020.
Specification Testing, Robust Training and Formal Verification are three elements that the AI powerhouse believe hold the essence of robust machine learning models.
- Rapid Python Model Deployment with FICO Xpress Insight - Aug 20, 2020.
The biggest hurdle in the use of data to create business value, is indeed the ability to operationalize analytics throughout the organization. Xpress Insight is geared to reduce the burden on IT and address their critical requirements while empowering business users to take ownership of decisions and change management.
- Top Google AI, Machine Learning Tools for Everyone - Aug 18, 2020.
Google is much more than a search company. Learn about all the tools they are developing to help turn your ideas into reality through Google AI.
- Are Computer Vision Models Vulnerable to Weight Poisoning Attacks? - Aug 17, 2020.
A recent paper has explored the possibility of influencing the predictions of a freshly trained Natural Language Processing (NLP) model by tweaking the weights re-used in its training. his result is especially interesting if it proves to transfer also to the context of Computer Vision (CV) since there, the usage of pre-trained weights is widespread.
- KDnuggets™ News 20:n30, Aug 5: What Employers are Expecting of Data Scientist Role; I have a joke about… - Aug 5, 2020.
Know What Employers are Expecting for a Data Scientist Role in 2020; I have a joke about …; First Steps of a Data Science Project; Why You Should Get Google's New Machine Learning Certificate; Awesome Machine Learning and AI Courses
- Awesome Machine Learning and AI Courses - Jul 30, 2020.
Check out this list of awesome, free machine learning and artificial intelligence courses with video lectures.
- The Bitter Lesson of Machine Learning - Jul 15, 2020.
Since that renowned conference at Dartmouth College in 1956, AI research has experienced many crests and troughs of progress through the years. From the many lessons learned during this time, some have needed to be re-learned -- repeatedly -- and the most important of which has also been the most difficult to accept by many researchers.
- 5 Innovative AI Software Companies You Should Know - Jul 8, 2020.
While machine learning is impacting organizations around the world, some are driving forward the real-world applications of innovative AI. Check out these interesting companies to watch for exciting new progress this year.
- Scope and Impact of AI in Agriculture - Jul 6, 2020.
The major advantage of focusing on AI-based methods is that they tackle each of the challenges faced by farmers from seed sowing to harvesting of crops separately and rather than generalising, provide customised solutions to a specific problem.
- Tools to Spot Deepfakes and AI-Generated Text - Jun 23, 2020.
The technologies that generate deepfake content is at the forefront of manipulating humans. While the research developing these algorithms is fascinating and will lead to powerful tools that enhance the way people create and work, in the wrong hands, these same tools drive misinformation at a scale we can't yet imagine. Stopping these bad actors using awesome tools is in your hands.
- Bias in AI: A Primer - Jun 23, 2020.
Those interested in studying AI bias, but who lack a starting point, would do well to check out this introductory set of slides and the accompanying talk on the subject from Google researcher Margaret Mitchell.
- What is emotion AI and why should you care? - Jun 19, 2020.
What is emotion AI, why is it relevant, and what do you need to know about it?
- How to make AI/Machine Learning models resilient during COVID-19 crisis - Jun 11, 2020.
COVID-19-driven concept shift has created concern over the usage of AI/ML to continue to drive business value following cases of inaccurate outputs and misleading results from a variety of fields. Data Science teams must invest effort in post-model tracking and management as well as deploy an agility in the AI/ML process to curb problems related to concept shift.
- GPT-3, a giant step for Deep Learning and NLP? - Jun 9, 2020.
Recently, OpenAI announced a new successor to their language model, GPT-3, that is now the largest model trained so far with 175 billion parameters. Training a language model this large has its merits and limitations, so this article covers some of its most interesting and important aspects.
- Why Do AI Systems Need Human Intervention to Work Well? - Jun 5, 2020.
All is not well with artificial intelligence-based systems during the coronavirus pandemic. No, the virus does not impact AI – however, it does impact humans, without whom AI and ML systems cannot function properly. Surprised?
- Upcoming Webinars and Online Events in AI, Data Science, Machine Learning: June - Jun 4, 2020.
Here are some interesting upcoming webinar, online events and virtual conferences in in AI, Data Science, and Machine Learning.
- 5 Essential Papers on AI Training Data - Jun 4, 2020.
Data pre-processing is not only the largest time sink for most Data Scientists, but it is also the most crucial aspect of the work. Learn more about training data and data processing tasks from 5 leading academic papers.
- Privacy-preserving AI – Why do we need it? - May 29, 2020.
Various data privacy threats can result from the usual process of building and constructing data and AI-based systems. Avoiding these challenges can be supported by utilizing state-of-the-art technologies in the domain of privacy-preserving AI.
- 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.
- Deepmind’s Gaming Streak: The Rise of AI Dominance - May 27, 2020.
There is still a long way to go before machine agents match overall human gaming prowess, but Deepmind’s gaming research focus has shown a clear progression of substantial progress.
- Machine Fairness: How to assess AI system’s fairness and mitigate any observed unfairness issues - May 26, 2020.
Microsoft is bringing the latest research in responsible AI to Azure (both Azure Machine Learning and their open source toolkits), to empower data scientists and developers to understand machine learning models, protect people and their data, and control the end-to-end machine learning process.
- 13 must-read papers from AI experts - May 20, 2020.
What research articles do top AI experts in the field recommend? Find out which ones and why, then be sure to add each to your reading to do list.
- AI Channels to Follow - May 15, 2020.
AI is certainly playing an important role in our global fight against the novel coronavirus. These YouTube channels are recommended to keep you covered with the latest advancements in the field and how it is impacting our world.