Daily Archives: October 22, 2020
Algorithms Are Making Economic Inequality Worse
The risks of algorithmic discrimination and bias have received much attention and scrutiny, and rightly so. Yet there is another more insidious side-effect of our increasingly AI-powered society — the systematic inequality created by the changing nature of work itself. We fear a future where robots take our jobs, but what happens when a significant portion of the workforce ends up in algorithmically managed jobs with little future and few possibilities for advancement?
… How many Uber drivers do you think will ever have the chance to attain a managerial position at the company, let alone run the ride-sharing giant? … There’s a “code ceiling” that prevents career advancement — irrespective of gender or race. Read More
Software Engineering for Machine Learning
Software Engineering for Machine Learning are techniques and guidelines for building ML applications that do not concern the core ML problem — e.g. the development of new algorithms — but rather the surrounding activities like data ingestion, coding, testing, versioning, deployment, quality control, and team collaboration. Good software engineering practices enhance development, deployment and maintenance of production level applications using machine learning components. SE-ML provides a curated list of articles covering the intersection of these two disciplines. Read More
#machine-learningContinuous Delivery for Machine Learning
Automating the end-to-end lifecycle of Machine Learning applications
Machine Learning applications are becoming popular in our industry, however the process for developing, deploying, and continuously improving them is more complex compared to more traditional software, such as a web service or a mobile application. They are subject to change in three axis: the code itself, the model, and the data. Their behaviour is often complex and hard to predict, and they are harder to test, harder to explain, and harder to improve. Continuous Delivery for Machine Learning (CD4ML) is the discipline of bringing Continuous Delivery principles and practices to Machine Learning applications. Read More
Expanding AI’s Impact With Organizational Learning
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
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
Rick's Cafe AI 