Goto

Collaborating Authors

Olaf Scholz: Germany's Staid But Steady Next Chancellor

International Business Times

Often described as austere and even robotic, Social Democrat Olaf Scholz nonetheless managed to inspire German voters in this year's election with a campaign that played on his reputation as a safe pair of hands. Scholz, 63, will now take office as Germany's ninth post-war chancellor, replacing Angela Merkel who is leaving the political stage after 16 years. The Social Democrats (SPD) had begun the election campaign at rock bottom in the polls, with many completely writing off Scholz's chances of heading the next government -- so much so that he didn't even have an official biography until this week. But Scholz managed to stage a stunning upset, beating Merkel's conservatives by positioning himself as the best candidate to continue her legacy, even adopting her famous "rhombus" hand gesture on a magazine cover. Unlike his rivals, he also managed not to make embarrassing mistakes during a campaign that drew on his reputation as a quiet workhorse, using the slogan "Scholz will sort it".


How AI Changed -- in a Very Big Way -- Around the Year 2000

#artificialintelligence

In "Hyping Artificial Intelligence Hinders Innovation" (podcast episode 163), Andrew McDiarmid interviewed Erik J. Larson, author of The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do (2021) (Harvard University Press, 2021) on the way "Machines will RULE!" Erik Larson has founded two two DARPA-funded artificial intelligence startups. Inthe book he urges us to go back to the drawing board with AI research and development. This portion begins at 01:59 min. A partial transcript and notes, Show Notes, and Additional Resources follow.


Multiclass Image Classification -- Hands-On with Keras and TensoFlow

#artificialintelligence

Image classification or computer vision is a branch of artificial intelligence where the task is to design systems that can recognise or classify objects based on digital images. It is a popular field due to the sheer breadth of applications -- image classification can be used for applications as diverse as recognising a particular flower from a photograph or to classifying medical images as normal or disease.


Autonomous driving startup Deeproute.ai prices L4 solution at $10,000 – TechCrunch

#artificialintelligence

Deeproute.ai, an autonomous vehicle startup with offices in Shenzhen and Fremont, California, unveiled an ambitious self-driving solution on Wednesday. The package, named DeepRoute-Driver 2.0, is a production-ready Level 4 system that costs approximately $10,000. The price tag is incredible given the hardware used: five solid-state lidar sensors, eight cameras, a proprietary computing system, and an optional millimeter-wave radar. Lidar accounts for roughly half of the total cost, a Deeproute spokesperson told TechCrunch. "As the whole supply chain is getting more developed and scale[s] up, we can expect the cost can go further down."


Text to SQL Queries

#artificialintelligence

WikiSQL is one of the most popular benchmarks in semantic parsing. It is a supervised text-to-SQL dataset, beautifully hand-annotated by Amazon Mechanical Turk. Some of the early works on WikiSQL modeled this as a sequence generation problem using seq2seq but we are moving away from it. The text has to be cleaned before passing it to the model like doing decontraction of the words, removing stop words, removing non-alphanumeric text from the corpus. As we have the dataset in SQL queries and headers, so we have to featurize the text using a tokenizer from the nltk library and then concatenate the query and headers.


The Year in Artificial Intelligence -- 2021 Popular Reads on JD Supra

#artificialintelligence

This website uses cookies to improve user experience, track anonymous site usage, store authorization tokens and permit sharing on social media networks. By continuing to browse this website you accept the use of cookies. Click here to read more about how we use cookies.


Stellantis' AI strategy targets $22.6b in revenues by 2030

#artificialintelligence

Carmaker Stellantis announced a strategy Tuesday to embed AI-enabled software in 34 million vehicles across its 14 brands targeting 20 billion euros ($22.6 billion) in annual revenues by 2030. CEO Carlos Tavares heralded the move as part of a strategy that would transform the car company into a "sustainable mobility tech company," with business growth coming from over-the-air features and services. It includes key partnerships with BMW on autonomous driving, iPhone manufacturer Foxconn on customized cockpits and Waymo to expand their autonomous driving partnership into a light commercial vehicle delivery fleets. Stellantis' embrace of AI and expansion of software-enabled vehicles is part of a broad transformation in the auto industry, with a race toward more fully electric and hybrid powertrains, more autonomous driving features and increased connectivity in automobiles. Stellantis, which was formed from the combination of PSA Peugeot and FCA Fiat, said the software integration would seamlessly integrate into customers lives, with the capability of live updates providing upgraded services over time.


Getting software to "hallucinate" reasonable protein structures

#artificialintelligence

Chemically, proteins are just a long string of amino acids. Their amazing properties come about because that chain can fold up into a complex, three-dimensional shape. So understanding the rules that govern this folding could not only give us insights into the proteins that life uses but could potentially help us design new proteins with novel chemical abilities. There's been remarkable progress on the first half of that problem recently: researchers have tuned AIs to sort through the evolutionary relationships among proteins and relate common features to structures. But that may change, thanks to the methods described in a paper released on Wednesday.


COVID-19 patients in Japan recovering in robot-staffed hotels

The Japan Times

Across the street from one of the busiest train stations in Tokyo, the Shinagawa Prince Hotel is in a bustling complex filled with restaurants, sports facilities and entertainment options like a dolphin show. The only formal greeting guests receive is from Softbank Corp.'s robot, Pepper. They're given written instructions on their rooms and stay. That's because the new arrivals all have one thing in common: they're infected with the coronavirus. In Japan, some COVID-19 patients get a hotel booking -- and can enroll in clinical trials during their stay -- with their positive test results.


Fundamentals of Machine Learning for Healthcare

#artificialintelligence

Machine learning and artificial intelligence hold the potential to transform healthcare and open up a world of incredible promise. But we will never realize the potential of these technologies unless all stakeholders have basic competencies in both healthcare and machine learning concepts and principles. This course will introduce the fundamental concepts and principles of machine learning as it applies to medicine and healthcare. We will explore machine learning approaches, medical use cases, metrics unique to healthcare, as well as best practices for designing, building, and evaluating machine learning applications in healthcare. The course will empower those with non-engineering backgrounds in healthcare, health policy, pharmaceutical development, as well as data science with the knowledge to critically evaluate and use these technologies. Co-author: Geoffrey Angus Contributing Editors: Mars Huang Jin Long Shannon Crawford Oge Marques The Stanford University School of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.