Larsen and Turbo Technology (L&T) Technology Services announces to open its new Engineering, Research, and Development (ER&D) center in Toronto, Canada. This is the third global design center of L&T after having two ERD centers in France and Polland.
This new center will address Canada-based clients to develop solutions for digital products. It will be the nearshore site for many American-based customers, and plans to hire more than 100 engineers in the next 12-24 months.
Amit Chanda, Chief Executive Officer of L&T Technology Services, stated that through this new center in Toronto, customers from Canada and North America would use the company’s cutting-edge technology and digital products.
This center can focus on developing digital solutions for the transportation sectors like railway engineering. Besides railway engineering, the new L&T center can also build applications for digital asset management, sensors, advanced mobility solutions, communications systems, and digital flyboard.
The inauguration of this new ER&D center in Toronto is a significant step towards further strengthening the two countries’ relationship and encouraging the Canada-India economic corridor.
Ruben Villegas and a few other researchers at Google unveil Phenaki, a system that generates videos from story-like descriptions given as text prompts. There are only a few datasets that can be used for text-to-video generation, but there are many text-to-image pairs available, using which Google has also developed text-to-image frameworks like Imagen.
Now, text-to-video generator Phenaki generates short videos by using images as single-frame videos and clubbing them together with a dataset of short videos having captions.
1/ From today's AI@ event: we announced our Imagen text-to-image model is coming soon to AI Test Kitchen. And for the 1st time, we shared an AI-generated super-resolution video using Phenaki to generate long, coherent videos from text prompts and Imagen Video to increase quality. pic.twitter.com/WofU5J5eZV
Phenaki works using some main components. It uses an encoder for video embedding, a language model for text embedding, a MaskGIT bidirectional transformer, and a decoder.
The system uses a “videos less than three seconds long” dataset to train the C-ViViT encoder/decoder to generate embeddings. The encoder is trained to generate non-overlapping patches as vectors by splitting frames. The decoder is trained to convert embeddings into pixels.
Phenaki uses the t5x language model to produce text embedding. MaskGIT generates the masked embeddings at inference using a set of masked video embeddings and text embeddings and then re-masks a portion of them to be generated in subsequent iterations.
To create minute-long videos, the authors repeatedly combined MaskGIT and C-ViViT. They first created a short film from a single sentence, after which they encoded the final k frames. They combined the text after the video embeddings to create more video frames. Unlike Make-A-Video, which uses several diffusion models to generate short videos and then upscale its resolution, Phenaki bootstraps its own frames to enhance throughput and narrative complexity.
Researchers from IIIT Delhi used the conventional Baum-Welch algorithm and deep learning to devise novel AI-driven approaches to predict crypto pricing and other financial parameters. As cryptocurrencies are not pegged against standard parameters or products, speculating their prices is challenging.
Shalini Sharma, a Ph.D. scholar from IIIT-Delhi, and her supervisor Dr. Angshul Majumdar devised two predominant approaches to predict financial parameters like crypto prices. Elsevier Information Sciences has recently validated the work and declared it to be “very precise” in predicting future prices.
The first approach builds on the Baum-Welch framework, a particular case of expectation-maximization used in a Hidden Markov Model (HMM). Using this framework, users can predict not only the prices but also the prediction uncertainty. This strategy calls for understanding the underlying factors influencing price swings, which is only sometimes possible with cryptocurrency.
The research also shows that the uncertainty estimates received from the first approach are correlated with historical CVI values. CVI, or crypto volatility index, shows how crypto prices react to fluctuations over time.
The second approach is driven by DL, as it does not require prior knowledge about underlying factors. This approach can predict crypto prices but cannot give uncertainty, making it ineffective in interpretability aspects.
The work is a significant step forward to aid crypto enthusiasts in having confidence in mining cryptocurrencies.
Hong Kong is all set to relax its strict crypto regulation with a plan to allow retail cryptocurrency trading. According to the report, a mandatory licensing regime for crypto platforms that enables retail crypto trading is set to be enforced in March 2023.
It said that Hong Kong is planning to legalize retail trading for cryptocurrency starting in March after years of skepticism, contrasting with ban in mainland China’s.
Moreover, regulators are also planning to allow retail exchanges to list prominent cryptocurrencies, like bitcoin (BTC) and ether (ETH). The listing rules will likely include criteria such as the token’s inclusion, market value, liquidity in third-party crypto indexes.
Executive director of crypto firm BC Technology Group, Gary Tiu, commented that introducing mandatory licensing in Hong Kong is one of the essential things regulators must do. They cannot forever effectively close the needs of retail investors, he added.
Executive president of Hashkey, digital asset financial services group, Michel Lee, explained that Hong Kong has been toiling to create an all-encompassing crypto regime, citing tokenized bonds and stocks as a potentially more critical segment in the future. “Just trading digital assets on their own is not the goal. The goal is to grow the ecosystem,” he said.
The Securities and Futures Commission (SFC), Hong Kong’s top financial regulator, introduceD a voluntary licensing regime in the year 2018. It limited crypto trading platforms to clients with portfolios of minimum HK$8 million ($1 million). However, the strict regulation turned away several crypto businesses, and only two firms, i.e., BC Technology Group and Hashkey, were approved.
After buying Twitter, Elon Musk is all set to relaunch Vine, a short video platform. He held a poll on Twitter on October 31st that asked users if they would want Vine back. The poll received 4.9 million views which specify 69.6% of people voting for yes and 30.4% for no.
As per the Axios report, Elon Musk has asked Twitter engineers to bring back Vine by the end of this year. He had informed his engineers to access the Vine code that had been left untouched since 2016 when the Vine services were shut down.
Sara Beykpour, the former Vine employee, tweeted that the codebase of Vine is about six years old, and it also has elements that are old than a decade. This means that the engineers of Vine will have to work extra hard to make Vine as good as TikTok and YouTube shorts.
Besides Vine, Elon Musk is also planning a series of changes on Twitter. He wants to bring verification badges in the limitations of Twitter’s monthly paid service.
Qiskit, an open-source software development kit platform, has recently started the ‘Understanding Quantum Information and Computation’ course series on YouTube. The course is said to be at par with University programs across the world for advanced undergraduate or introductory graduate students.
Richard Feynman and Yuri Manin both originally presented the concept of quantum computing in the 1980s. By using principles from quantum-mechanical phenomena, such as superposition and entanglement, to process data, quantum computing offers processing capabilities that are vastly superior to those of classical computing. Applications range from economics to machine learning, forecasting the weather, and genetic research. It is anticipated that a fault-tolerant universal quantum computer would further both scientific and economic advancement to new horizons.
Even at this nascent point in the development of the quantum computing industry, the accelerating development of the creation of quantum applications and architecture will only expedite the potential applications being discovered in mainstream industries. As a result, the quantum computing field is ripe with opportunities. Thus, students with knowledge of quantum computing are in great demand.
The ‘Understanding Quantum Information and Computation’ course series by Qiskit will be taught by John Watrous, Technical Director of Education at IBM Quantum. In the overview YouTube video, Watrous reveals to have started learning about quantum computing in 1994 and has been imparting his expertise on the subject since then. The main objective behind the Qiskit quantum computing series is to offer educational content to those interested in learning about this emerging technology.
Watrous mentions that students need to have pre-requisite knowledge about mathematical concepts like linear algebra, including vectors and matrices, complex numbers, sets and functions. He adds that students need not have a specialized background in quantum physics nor quantum computing before starting this course.
Unit 1 of the ‘Understanding Quantum Information and Computation’ series will focus on the basics of quantum information, including quantum states, measurements and operations, quantum circuits, and examples like quantum teleportation. Unit 2 will be centered around explaining quantum computing algorithms like Shor’s algorithm and other key quantum algorithms in use today. In Unit 3, Watrous will offer deeper insight into quantum information and how it can be described using density matrices and the relevance of density matrices in quantum information. Lastly, in the final Unit, viewers will get an understanding and mitigation of the effects of noise on quantum computers.
Researchers at Meta AI have developed a new model,ESMFold, that predicts over 600 million protein structures from lesser-studied sources. ESMFold framework is a first-of-its-kind and is known to accelerate the performance of protein-folding AI by 60x.
Our research team developed ESMFold’s new approach using a large language model that allowed us to accelerate folding by up to 60x — an improvement that has the potential to accelerate work in medicine, green chemistry, environmental applications, & renewable energy.
Additionally, Meta has provided a platform, ESMFold Metagenomic Atlas, where users can instantly retrieve protein sequences.
Announcing the ESM Metagenomic Atlas — the first comprehensive view of the ‘dark matter’ of the protein universe. Made possible by ESMFold, a new breakthrough model for protein folding from Meta AI.
Numerous databases published by NCBI, Joint Genome Institute, and a few others have already aided in cataloging newly uncovered protein structures. While breakthroughs in genomics have made it possible to identify the sequences of many unique proteins, this data alone cannot explain how proteins fit together to form a functional molecule.
Meta AI’s new protein-folding approach will utilize large language models to represent an initial comprehensive view of protein structures in a metagenomics database containing millions of proteins. This model allows scientists to analyze structural relationships and discover new combinations that benefit medicine and other fields.
ESMFold works in a two-fold manner. Initially, the network is trained with an intuitive understanding of protein structures and sequences. The second step combines this information with the information containing possible protein combinations/relationships.
The model closely resembles DeepMind’s AlphaFold AI, which came earlier this year. AlphaFold AI predicted more than 200M cataloged proteins using a protein’s 1D amino acid sequence. While the ESMFold is not as accurate, it is about 60x faster in making predictions, allowing researchers to scale protein structure cataloging to much larger databases.
Twitter’s Chief of People and Diversity Dalana Brand and Chief Consumer Officer Sarah Personette have resigned from their respective positions. Personette and Brand announced their resignations in respective Twitter threads on Tuesday.
Personette, former in charge of Twitter’s ad sales business, said she resigned on Friday, and her work access was cut off by Tuesday. Brand also resigned on Friday. After Twitter’s takeover, Musk fired four key executives immediately: CEO Parag Agrawal, CFO Ned Segal, Head of Legal Policy Vijaya Gadde, and General Counsel Sean Edgett.
With Personette and Brand out, the number of remaining executives at Twitter is dwindling, with more key executives rumored to be leaving soon. Twitter’s head of product, Jay Sullivan, deleted the bio on his Twitter account, which earlier denoted his role at the company. The previous head of product, Kayvon Beykpour, was let go by former CEO Agrawal in May.
Brand joined Twitter in 2018, previously serving as VP of People Experience and Head of Inclusion & Diversity. According to Ebony, she was appointed to her most recent role in February of this year, making her the first Black woman to work in Twitter’s c-suite.
A former Facebook marketing VP, Personette has worked at Twitter since October 2018, when she joined as a VP of Global Client Solutions, per LinkedIn. She was promoted to chief customer officer in August 2021. That role is crucial to Twitter’s business since most of its revenue comes from ad sales.
After some uncertainty around Twitter Blue’s revamp, Elon Musk has finally laid out the company’s approach. He said the new paid plan would cost $8 per month, which he mentioned in reply to Stephen King’s tweet. Moreover, the price will be adjusted based on the country’s purchasing power parity, hinting toward a global launch of Twitter Blue.
Twitter’s current lords & peasants system for who has or doesn’t have a blue checkmark is bullshit.
Musk’s tweet also says that the social network’s current verification system is akin to a “lords & peasants” system. His tweet about the new paid plan indicated offering verification to subscribers.
You will also get: – Priority in replies, mentions & search, which is essential to defeat spam/scam – Ability to post long video & audio – Half as many ads
Musk also noted some of the features that will roll out with this new plan, including fewer ads, priority in replies, mentions, and searches (something which verified handles get through the “Verified” notification channel). They will also be able to post longer videos than the current limit of 2 minutes and 20 seconds.
Musk tends to change his mind quickly, so we should take this announcement with a grain of salt. These might not be the final features when Twitter rolls out its new subscription plan.
Earlier this week, reports noted that Musk and Twitter are revamping the verification process, which might involve a fee as high as $20 per month. However, the billionaire has seemed to settle on the $8 per month pricing for now.
The reports also noted that the current verified users would lose their blue checkmark if they do not pay for the new paid plan. Musk has not mentioned any such measure in the new Twitter thread about the subscription plan.
It has been almost a week since Elon Musk has taken over Twitter, and he has already started to revamp the platform. According to The Verge, the new CEO of Twitter has plans to charge users $20 per month for a blue check that indicates verified on a Twitter account.
This new feature would be a part of Twitter Blue, which is the existing subscription feature launched last year. Musk has not been very subtle about his disregard for the monthly $4.99 product, which, according to him, is not very appealing to anyone beyond power users. Currently, subscribing to Twitter Blue gets users early access to features like the edit button and the feature to change the design of the app icon for Twitter on their phones. You can also get ad-free access to specific news sources and a feed of the most talked-about articles from the people you follow.
Elon Musk and venture capitalists Jason Calacanis — who changed his Twitter bio to ‘Chief Meme Officer at Twitter’ — have been hinting at paid user verification since April. As per the leaked texts, Calacanis laid out a five-part plan for Elon Musk that includes the concept of a membership team, which will remove bots while making users pay for actual name membership. Calacanis also complained that no one is setting priorities at Twitter and that 12,000 people work on whatever they like.
Musk’s desire to “authenticate all humans” has been part of his plan since his initial takeover bid. Even if we keep the potential security flaws aside, this plan still ignores the fundamental difference between verification of someone’s identity and giving anyone a blue check to convey that they are who they are saying they are.
“Why should the blue check marks be limited to the celebrities, elite, and press? How is that democratic?” Calacanis texted Musk. Musk and his companions see this plan as a way to give money to Twitter. But by monetizing the blue tick symbol that currently has some value, they will ultimately remove all of that current value. Here’s how.
Blue checks on social platforms are a means of combating misinformation. Today, if someone makes a fake account pretending to be a celebrity, world leader, and journalist, it’s easy to tell it’s fake if the account does not have a blue check. But under this newly proposed system, there is not much incentive to pay $20 per month to remain verified, especially when the once-coveted symbol will be available to anyone willing to pay. It is possible that bad actors posing as journalists to spread fake information would be more inclined to pay the $20 than actual journalists.
Musk, however, doesn’t seem to care very much about misinformation’s dangers. According to the sources, just this weekend, Musk tweeted and later deleted a fake conspiracy theory about the attack on the husband of the Speaker of the House, Nancy Pelosi.
One alternative for this feature could be to charge only corporations like Netflix or Steak-umm (which have a significant Twitter presence) to be verified. Corporate clients are more willing than a local nonprofit newsroom to pay $20 monthly per account to prove their legitimacy. Yet this still does not solve the misinformation issue. If anything, it only pressures companies into buying a product they’ve gotten free for years to prevent a possible PR problem.
How much would you pay to be verified & get a blue check mark on Twitter?
For now, it does not seem like Twitter users are very enthusiastic about this plan that Musk has concocted. Recently, Calacanis posted a poll asking how much people would pay to be verified, and at the time of publication, about 81.6% of more than a million respondents said they would not pay. However, considering the recent developments, Musk might devise a different verification tactic altogether. Hopefully, that plan is a bit more thought-through than this one.
