FOR some organizations, harnessing artificial intelligence’s full potential begins tentatively with explorations of select enterprise opportunities and a few potential use cases. While testing the waters this way may deliver valuable insights, it likely won’t be enough to make your company a market maker (rather than a fast follower). To become a true AI-fueled organization, a company may need to fundamentally rethink the way humans and machines interact within working environments. Executives should also consider deploying machine learning and other cognitive tools systematically across every core business process and enterprise operation to support data-driven decision-making. Likewise, AI could drive new offerings and business models. These are not minor steps, but as AI technologies standardize rapidly across industries, becoming an AI-fueled organization will likely be more than a strategy for success—it could be table stakes for survival. Read More
Tag Archives: Books
What is “The Art of Thinking Like a Data Scientist” Workbook and Why It Matters
To survive in today’s digital economy, it’s imperative for organizations to convert their key business stakeholders into “Citizens of Data Science.” Meaning, they should not only understand where and how to apply data science to power the business, but champion a data-first approach toward decision-making across the entire organization.
That’s the subject my new workbook, “The Art of Thinking Like A Data Scientist”, seeks to accomplish. It’s designed to be a pragmatic tool that can help your organization leverage data and analytics to power its business and operational models. The content is jammed with templates, worksheets, examples, and hands-on exercises — all composed to help reinforce and deploy the fundamental concepts of “Thinking Like A Data Scientist.” Read More
7 Resources for Those Wanting to Learn Data Science
Data Science happens as a natural consequence of multiple skills and experiences acquired by working with computers, maths, people and businesses. Some will develop these skills naturally from multiple experiences along many years… But what if there’s a shortcut?
I’ve decided to compile the top 7 resources that I reckon as my fundamental steps in my personal journey towards data science. The resources below are to engender both the interest and the intuition required for dealing with the data and the science involved. Read More
AI Reading List
For newcomers to the field of artificial intelligence, prioritizing among endless AI resources can be an overwhelming challenge. This list attempts to do exactly that: it’s a carefully curated compilation of resources for getting up to speed quickly on key topics in artificial intelligence research and its long-term implications.
The list is divided into “80/20” sections with a few high-priority readings, for maximum value with minimal time investment, and “deep dive” sections for further exploration. Read More
Artificial intelligence-enhanced journalism offers a glimpse of the future of the knowledge economy
Much as robots have transformed entire swaths of the manufacturing economy, artificial intelligence and automation are now changing information work, letting humans offload cognitive labor to computers. In journalism, for instance, data mining systems alert reporters to potential news stories, while newsbotsoffer new ways for audiences to explore information. Automated writing systems generate financial, sports and elections coverage.
A common question as these intelligent technologies infiltrate various industries is how work and labor will be affected. In this case, who—or what—will do journalism in this AI-enhanced and automated world, and how will they do it?
The evidence I’ve assembled in my new book “Automating the New: How Algorithms are Rewriting the Media” suggests that the future of AI-enabled journalism will still have plenty of people around. However, the jobs, roles and tasks of those people will evolve and look a bit different. Human work will be hybridized—blended together with algorithms—to suit AI’s capabilities and accommodate its limitations. Read More
Human + Machine
Artificial Intelligence is no longer just a futuristic notion, it’s here right now, leading the 4th Industrial Revolution. And everyone is talking about AI, all the time.
We are all well aware of AI challenges 1. Data management in corporations, 2. Lack of deeply skilled AI talent, 3. Responsible AI needs (moral use, bias or security). But I find there are so many articles online about AI industry disruption that drive too much concern about malicious use of AI or even job loss paranoia.
I recently read “Human + Machine: Reimagining Work in the Age of AI” by Paul R. Daugherty and H. James Wilson, both Accenture leaders, and found their approach to machines and human collaboration and the implications in terms of human resources and new business models, refreshing and inspiring. Read More
The Best and Most Current of Modern Natural Language Processing
Over the last two years, the Natural Language Processing community has witnessed an acceleration in progress on a wide range of different tasks and applications. 🚀 This progress was enabled by a shift of paradigm in the way we classically build an NLP system: for a long time, we used pre-trained word embeddings such as word2vec or GloVe to initialize the first layer of a neural network, followed by a task-specific architecture that is trained in a supervised way using a single dataset.
Recently, several works demonstrated that we can learn hierarchical contextualized representations on web-scale datasets 📖 leveraging unsupervised (or self-supervised) signals such as language modeling and transfer this pre-training to downstream tasks (Transfer Learning).Excitingly, this shift led to significant advances on a wide range of downstream applications ranging from Question Answering, to Natural Language Inference through Syntactic Parsing…
“Which papers can I read to catch up with the latest trends in modern NLP?”
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Is a master algorithm the solution to our machine learning problems?
Suppose there’s an algorithm that knows what we’re searching for on Google, what we’re buying on Amazon and what we’re listening to on Apple Music or watching on Netflix. Now this algorithm knows a lot about us and has a better and more complete picture of us. This powerful “master algorithm” is at the heart of work postulated by Pedro Domingos, author of The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Read More
AI Superpowers: China, Silicon Valley, and the New World Order
Dr. Kai-Fu Lee—one of the world’s most respected experts on AI and China—reveals that China has suddenly caught up to the US at an astonishingly rapid and unexpected pace. Read More
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