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Alexa, why have you charged me £2 to say the Hail Mary?

The Guardian

When my 87-year-old mother, Patricia Collinson, was given an Alexa speaker by my sister, she was delighted to find she could ask it to say the Hail Mary. Every morning for a week the devout Catholic asked Alexa to recite the prayer. What she was less delighted to learn was that she had unwittingly ordered a premium subscription payable through Amazon to a private company called Catholic Prayers. Patricia, a retired district nurse in Hastings, does not own a computer, and does not know how to use one. She had signed up by voice command, without being presented with the kind of outline or terms and conditions that now comes as standard when you pay for things online.


Artificial Intelligence as a Service Market Is Booming Worldwide

#artificialintelligence

JCMR Primary research- Our primary research efforts include reaching out Artificial Intelligence as a Service industry participants through mail, tele-conversations, referrals, professional networks and face-to-face interactions. We are also in professional corporate relations with various companies discussions, fulfilling following functions: That allow us greater flexibility for reaching out Artificial Intelligence as a Service industry participants and commentators for interviews and • Validates and improves the data quality and strengthens Artificial Intelligence as a Service industry research proceeds • Further develops analyst team's Artificial Intelligence as a Service market understanding and expertise • Supplies authentic information about Artificial Intelligence as a Service market size, share, growth and forecasts Our primary Artificial Intelligence as a Service industry research interview and discussion panels are typically composed of most experienced Artificial Intelligence as a Service industry members.


Council Post: Is Decision Intelligence The New AI?

#artificialintelligence

Pascal Bornet is an expert in AI and Automation, best-selling author, keynote speaker, and CDO at Aera Technology. Decision intelligence is a new field that helps support, augment and automate business decisions by linking data with decisions and outcomes. It uses a combination of methods (e.g., decision mapping and decision theories) and technologies (e.g., machine learning and automation) to improve the way decisions are made in companies. Decision intelligence includes continually evaluating decision outcomes and optimizing them through a feedback system. The term "decision intelligence" was popularized in Lorien Pratt's 2019 book, Link: How Decision Intelligence Connects Data, Actions, and Outcomes for a Better World, after Google launched its decision intelligence department in 2018.


GitHub - lucidrains/imagen-pytorch: Implementation of Imagen, Google's Text-to-Image Neural Network, in Pytorch

#artificialintelligence

It is the new SOTA for text-to-image synthesis. Architecturally, it is actually much simpler than DALL-E2. It consists of a cascading DDPM conditioned on text embeddings from a large pretrained T5 model (attention network). It also contains dynamic clipping for improved classifier free guidance, noise level conditioning, and a memory efficient unet design. It appears neither CLIP nor prior network is needed after all.


The best smart home and kitchen sales we found for Memorial Day

Engadget

If you've been waiting to upgrade your home with the latest gear, this weekend might be the time to do so. From robot vacuums to Instant Pots, there are a number of great sales for connected appliances and kitchen gadgets for Memorial Day this year. As you can imagine, there are quite a lot of them, so we've collected some of the best ones below. Anker's Eufy RoboVac 11S is one of our favorite budget robot vacuums thanks to its slim profile, smart features and affordable price. It doesn't have WiFi, but it does have a remote control.


'Quantum Internet' Inches Closer With Advance In Data Teleportation - AI Summary

#artificialintelligence

Researchers believe these devices could one day speed the creation of new medicines, power advances in artificial intelligence and summarily crack the encryption that protects computers vital to national security. In 2019, Google announced that its machine had reached what scientists call "quantum supremacy," which meant it could perform an experimental task that was impossible with traditional computers. Part of the challenge is that a qubit breaks, or "decoheres," if you read information from it -- it becomes an ordinary bit capable of holding only a 0 or a 1 but not both. But by stringing many qubits together and developing ways of guarding against decoherence, scientists hope to build machines that are both powerful and practical. Ultimately, ideally, these would be joined into networks that can send information between nodes, allowing them to be used from anywhere, much as cloud computing services from the likes of Google and Amazon make processing power widely accessible today.


iiot bigdata_2022-05-27_03-56-20.xlsx

#artificialintelligence

The graph represents a network of 1,040 Twitter users whose tweets in the requested range contained "iiot bigdata", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 27 May 2022 at 11:01 UTC. The requested start date was Friday, 27 May 2022 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 18-hour, 9-minute period from Tuesday, 24 May 2022 at 05:50 UTC to Thursday, 26 May 2022 at 23:59 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


How data science startup Hugging Face is giving Microsoft an edge against Amazon and Google by giving Azure users easy access to its machine learning models

#artificialintelligence

As the competition for capturing the machine learning industry heats up, Microsoft is turning to a popular $2 billion startup to get an edge over rivals. The creators of Azure are rolling out an integration with Hugging Face, a popular data science startup that hosts some of the most-used machine learning models, to gain a new route into companies and grow business. The startup recently raised $100 million at a $2 billion valuation led by Lux Capital in a highly competitive funding round, with Addition and Sequoia participating. Microsoft is betting that Endpoints, Hugging Face's new integration, will help drastically simplify the time required to get machine learning models into place. The majority of efforts in machine learning die before seeing the light of day due to the number of people involved -- which Hugging Face is trying to drop to as small a number as possible by making it easy for a single person to share a model across the organization.


Machine Learning at the Edge

#artificialintelligence

I'm really excited to talk about advances in federated learning at the edge with you. When I think about the edge, I often think about small embedded devices, IoT, other types of things that might have a small computer in them, and I might not even realize that. I recently learned that these little scooters that are all over my city in Berlin, Germany, and maybe even yours as well, that they are collecting quite a lot of data and sending it. When I think about the data they might be collecting, and when I put on my data science and machine learning hat, and I think about the problems that they might want to solve, they might want to know about maintenance. They might want to know about road and weather conditions. They might want to know about driver performance. Really, the ultimate question they're trying to answer is this last one, which is, is this going to result in some problem for the scooter, or for the human, or for the other things around the scooter and the human? These are the types of questions we ask when we think about data and machine learning. When we think about it on the edge, or with embedded small systems, this often becomes a problem because traditional machine learning needs quite a lot of extra information to answer these questions. Let's take a look at a traditional machine learning system and investigate how it might go about collecting this data and answering this question. First, all the data would have to be aggregated and collected into a data lake. It might need to be standardized, or munged, or cleaned, or something done with it beforehand. Then, eventually, that data is pulled usually by a data science team or by scripts written by data engineering, or data scientists on the team.


Imagen: Will AI text-to-image generators put illustrators out of a job?

#artificialintelligence

Examples of images created by Google's Imagen AI Tech firms are racing to create artificial intelligence algorithms that can produce high-quality images from text prompts, with the technology seeming to advance so quickly that some predict that human illustrators and stock photographers will soon be out of a job. In reality, limitations with these AI systems mean it will probably be a while before they can be used by the general public. Text-to-image generators that use neural networks have made remarkable progress in recent years. The latest, Imagen from Google, comes hot on the heels of DALL-E 2, which was announced by OpenAI in April. Both models use a neural network that is trained on a large number of examples to categorise how images relate to text descriptions. When given a new text description, the neural network repeatedly generates images, altering them until they most closely match the text based on what it has learned.