Most companies developing AI capabilities have yet to gain significant financial benefits from their efforts. Only when organizations add the ability to learn with AI do significant benefits become likely.
Only 10% of companies obtain significant financial benefits from artificial intelligence technologies. Why so few?
Our research shows that these companies intentionally change processes, broadly and deeply, to facilitate organizational learning with AI. Better organizational learning enables them to act precisely when sensing opportunity and to adapt quickly when conditions change. Their strategic focus is organizational learning, not just machine learning. Read More
Monthly Archives: October 2020
The Centralized Internet Is Inevitable
On Wednesday, October 14th, Twitter locked the accounts of a White House press secretary and the New York Post, one of America’s largest tabloid newspapers. The accounts shared a story the Post ran on leaked emails which seemingly implicate Democratic presidential candidate Joe Biden’s son in corruption. When users tried to share the story publicly or privately, they found a message informing them that their tweets could not be sent. Chinese users of Twitter mused on the feeling of déjà vu, as links to corruption stories sometimes vanish on Chinese social media apps like WeChat as well. For a moment, the paths of the Chinese and American internets once more converged. Read More
When governments turn to AI: Algorithms, trade-offs, and trust
Artificial intelligence can help government agencies solve complex public-sector problems. For those that are new at it, here are five factors that can affect the benefits and risks.
As artificial intelligence (AI) and machine learning gain momentum, an increasing number of government agencies are considering or starting to use them to improve decision making. Additionally, COVID-19 has suddenly put an emphasis on speed. In these uncharted waters, where the tides continue to shift, it’s not surprising that analytics, widely recognized for its problem-solving and predictive prowess, has become an essential navigational tool. Some examples of compelling applications include those that identify tax-evasion patterns, sort through infrastructure data to target bridge inspections, or sift through health and social-service data to prioritize cases for child welfare and support, or predicting the spread of infectious diseases. They enable governments to perform more efficiently, both improving outcomes and keeping costs down. Read More
Artificial intelligence and machine learning algorithms to transform chatbots
…A chatbot is an artificial intelligence software. It helps to communicate with a user in natural language. It uses websites, message applications, mobile apps, or telephone to provide interaction.
… chatbots usually learn from their past experiences. They analyze client queries and improve their performance. This feature of the bot helps it increase its overall interaction with the client making it more user-friendly. Further, it can also understand customer’s preferences and choices. The Machine learning algorithms used in chatbots helps bots to gain the knowledge required during bot training. During bot training, the organizations provide all the necessary information to the bot. Exercise will increase the bot’s working efficiency. Read More
Deciphering Undersegmented Ancient Scripts Using Phonetic Prior
Most undeciphered lost languages exhibittwo characteristics that pose significant de-cipherment challenges: (1) the scripts arenot fully segmented into words; (2) the clos-est known language is not determined. Wepropose a decipherment model that handlesboth of these challenges by building on richlinguistic constraints reflecting consistentpatterns in historical sound change. We cap-ture the natural phonological geometry bylearning character embeddings based on theInternational Phonetic Alphabet (IPA). Theresulting generative framework jointly mod-els word segmentation and cognate align-ment, informed by phonological constraints.We evaluate the model on both decipheredlanguages (Gothic, Ugaritic) and an undeci-phered one (Iberian). The experiments showthat incorporating phonetic geometry leadsto clear and consistent gains. Additionally,we propose a measure for language close-ness which correctly identifies related lan-guages for Gothic and Ugaritic. For Iberian,the method does not show strong evidencesupporting Basque as a related language,concurring with the favored position by thecurrent scholarship. Read More
Agence is an experience that is never the same twice.
A dynamic short-film that merges cinematic storytelling, artificial intelligence, and user interactivity, Agence is never the same twice. In this simulated universe, you have the power to observe or interfere with tiny AI creatures, called the “Agents”, as they react to each other and their emerging world. Once you meet these little AI creatures, their story will never be the same. Read More
Understanding the Role of Individual Units in a Deep Neural Network
Deep neural networks excel at finding hierarchical representations that solve complex tasks over large data sets. How can we humans understand these learned representations? In this work, we present network dissection, an analytic framework to systematically identify the semantics of individual hidden units within image classification and image generation networks. First, we analyze a convolutional neural network (CNN) trained on scene classification and discover units that match a diverse set of object concepts. We find evidence that the network has learned many object classes that play crucial roles in classifying scene classes. Second, we use a similar analytic method to analyze a generative adversarial network (GAN) model trained to generate scenes. By analyzing changes made when small sets of units are activated or deactivated, we find that objects can be added and removed from the output scenes while adapting to the context. Finally, we apply our analytic framework to understanding adversarial attacks and to semantic image editing. Read More
#gans, #neural-networksEye on A.I. Episode 44 – Fei-Fei Li
This week I speak to Stanford professor Fei-Fei Li, one of the people responsible for the current AI revolution. Fei-Fei talked about her early days running a New Jersey dry cleaner to finance her Princeton education, her creation of ImageNet, the world’s first large labeled image data set, which allowed the validation of neural networks, and her latest work on ambient intelligence, which promises to transform elder care. Read More
The Ultimate Python Resource hub
Programming language Python is a big hit for machine learning. But now it needs to change
Despite its popularity, Python could become limited to data science alone on its current trajectory, say two experts.
Open-source programming language Python has become one of the few languages that won’t disappear anytime soon. It’s the top or one of the top two languages in most notable language popularity indexes, and even looks set to beat Java these days.
But 35-year-old Python does have its weaknesses. Not necessarily for the data-science and machine-learning communities built around Python extensions like NumPy and skippy, but as a general programming language. Read More
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