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DP Technology DevDay 2024 Showcases Large Science Models and Announces Open Science Initiative

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In recent years, the rapid development of artificial intelligence has introduced new possibilities across numerous scientific disciplines. As an AI for Science pioneer, DP Technology is continually collaborating with partners to explore the transformative impact AI can bring to science. During its DevDay held in Beijing on April 12th, DP Technology showcased a series of large science models, including the DPA large atomic model[1], Uni-Mol 3D molecular model[2], Uni-Fold protein folding model[3], Uni-RNA ribonucleic acid model[4], and Uni-SMART large language model for multimodal scientific literature[5] among others.
DPA
The rapid development of artificial intelligence (AI) is driving significant changes in the field of atomic modeling, simulation, and design. Inspired by recent advancements of large language models, DP aspires to develop a similar foundational model for the atomic domain. Developed by DP and collaborators, DPA is a large pre-trained model for interatomic potential with attention mechanism. The recently released DPA-2 model addresses the limitations of single-source DFT data reliance in other pre-trained atomistic models. DPA-2 covers ~100 elements in the periodic table. In a perovskite study, Liu Shi’s team at Westlake University utilised the pre-trained DPA increased the efficiency of force field development by 100x.
DPA-2 is also used in drug discovery. The latest version of Uni-FEP (free energy perturbation) can now be powered by DPA’s pre-trained inter-atomic potential. Uni-FEP now utilizes the DPA-2 pre-trained model to optimize classical force field parameters on-the-fly, providing enhanced free energy predictions. This results in improved R^2 values and reduced RMSE.
Uni-Mol
Uni-Mol, a pre-trained 3D molecular representation learning model (ICLR ’23), now boasts an improved accuracy in predicting these binding poses with over 77% of ligands achieving an RMSD value under 2.0 Å and over 75% passing all quality checks. This marks a substantial leap from the 62% accuracy of the previous version, also eclipsing other known methods. It effectively tackled common challenges like chirality inversions and steric clashes, ensuring that predictions are not just accurate but also chemically viable.
Based on Uni-Mol, VD-Gen[6], developed by DP and collaborators, is capable of directly generating molecules with high binding affinity within the protein pocket. VD-Gen accurately predicts the elemental types and fine-grained atomic coordinates of the generated molecules without the need to coarse-grain the atomic coordinates into a grid, offering higher precision compared to three-dimensional grid-based methods. Furthermore, VD-Gen can efficiently generate all types of atoms and their coordinates simultaneously, outperforming autoregressive generation models in performance without being affected by the order of generation.
Uni-QSAR[7], built on the Uni-Mol model, is an innovative tool for automated prediction of molecular properties. It can rapidly and cost-effectively assess ADMET properties during the early stages of drug development. This method utilizes the three-dimensional structural information of molecules, combined with computational chemistry and bioinformatics tools, to predict the behavior of drug molecules in the body. DP demonstrated benchmarks include 22 ADMET public datasets from the TDC Benchmark and 30 activity datasets from the MoleculeACE Benchmark with Chemprop, DeepAutoQSAR, and DeepPurpose as baselines. Uni-QSAR achieved the best performance in 21 out of 22 tasks in the TDC ADMET Benchmark tests and in 26 out of 30 tasks in the MoleculeACE benchmark tests.
Uni-RNA
Uni-RNA is pre-trained on approximately one billion high-quality RNA sequences, covering virtually all RNA space. By fine-tuning the model across a broad range of downstream tasks, Uni-RNA achieved leading results in all three RNA domains: RNA structure prediction, mRNA sequence property prediction, and RNA function prediction.
Through a research conducted by DP, it is found that out of 10 RNA sequences generated by Uni-RNA, each one surpassed the performance level of the commercially available vaccine sequences from Moderna, while being generally comparable and sometimes exceeding the level of BioNTech’s commercial mRNA vaccine sequence. This demonstrates that models like Uni-RNA not only hold immense value for academic research but also possess significant potential for industrial research and development applications.
Uni-Fold
Protein structure modeling is a prerequisite for structure-based drug development. Once a preliminary structure is obtained, further refining and optimizing key structural regions is crucial for ensuring the accuracy of subsequent research.
Uni-Fold is the first protein structure prediction tool to fully open-source its training and inference code. It supports the structural prediction of polymeric protein systems and achieves top industry accuracy in prediction results under the same training datasets.
Uni-SMART
Uni-SMART (Science Multimodal Analysis and Research Transformer) tackles the urgent need for new solutions that can fully understand and analyze multimodal content in scientific literature. Indeed, many LLMs can already ingest PDF, but they often struggle to digest and interpret the rich information encapsulated within charts, graphs, and molecular structures embedded within those documents.
Through rigorous quantitative evaluation, Uni-SMART demonstrates significant performance gain in interpreting and analyzing multimodal contents in scientific documents, such as tables, charts, molecular structures, and chemical reactions, compared with other leading tools, such as GPT-4 and Gemini.
Industrial Software for Drug Discovery, Battery Development and beyond
Advancing AI for Science, DP Technology has developed a suite of industry applications based on its large science models and advanced algorithms. This suite includes the innovative Bohrium® Scientific Research Space, Hermite® Computational Drug Design Platform, RiDYMO® Dynamics Platform, and Piloteye® Battery Design Automation Platform. Together, these platforms support a robust foundation for industrial innovation within an open ecosystem for AI in science, fostering advancements in key areas such as drug discovery, energy, materials science, and information technology.
Open Science initiative
At DevDay, DP Technology joined forces with industry leaders such as CATL, Yunnan Baiyao, Alibaba Cloud, Tencent Cloud, Volcano Engine, China Unicom etc, to initiate an AI for Science open science ecosystem. This cross-industry collaboration aims to integrate the strengths of each party in artificial intelligence, cloud computing, and industry applications to propel innovation. The initiative aims to accelerate the open-source development of datasets, algorithms, code and pre-trained models.
Sun Weijie, founder and CEO of DP Technology, stated, “The launch of large science models is our firm commitment to advancing scientific and industrial innovation. With this series of scientific large models, we are not only able to accelerate the process of scientific research and product development but also increase the success rate of R&D, bringing disruptive impacts to drug discovery, battery development and beyond.”
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AI should be trained to respect a regulatory ‘constitution’ says BofE policy maker

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Innovative AI models should be trained to respect a ‘constitution’ or a set of regulatory rules that would reduce the risk of harmful behaviour, argues a senior Bank of England policy maker.
In a speech at CityWeek in London, Randall Kroszner, an external member of the Bank of England’s financial policy committee, outlined the distinction between fundamentally disruptive versus more incremental innovation and the different regulatory challenges posed.
“When innovation is incremental it is easier for regulators to understand the consequences of their actions and to do a reasonable job of undertaking regulatory actions that align with achieving their financial stability goals,” he says.
However, in the case of AI, innovation comes thick and fast, and is more likely to be a disruptive force, making it “much more difficult for regulators to know what actions to take to achieve their financial stability goals and what the unintended consequences could be for both stability and for growth and innovation.”
Kroszner suggests that the central bank’s up-and-coming Digital Securities Sandbox, that will allow firms to use developing technology, such as distributed ledger technology, in the issuance, trading and settlement of securities such as shares and bonds, may no longer be an applicable tool for dealing with artifical intelligence technology.
“Fundamentally disruptive innovations – such as ChatGPT and subsequent AI tools – often involve the potential for extraordinarily rapid scaling that test the limits of regulatory tools,” he notes. “In such a circumstance, a sandbox approach may not be applicable, and policymakers may themselves need to innovate further in the face of disruptive change.”
He points to a recent speech by FPC colleague Jon Hall that highlighted the potential risks emerging from neural networks becoming what he referred to as ‘deep trading agents’ and the potential for their incentives to become misaligned with that of regulators and the public good. This, he argued, could help amplify shocks and reduce market stability.
One proposal to mitigate this risk was to train neural networks to respect a ‘constitution’ or a set of regulatory rules.
Kroszner suggests that the idea of a ‘constitution’ could be combined with, and tested in, a sandbox as way of shepherding new innovation in a way that supports financial stability.
“In the cases where fundamentally disruptive change scales so rapidly that a sandbox approach may not be applicable, a ‘constitutional’ approach may be the most appropriate one to take,” he says.
Source: finextra.com
 
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OpenAI apologizes to Johansson, denies voice based on her

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OpenAI chief Sam Altman apologized Tuesday to Scarlett Johansson after the movie star said she was “shocked” by a new synthetic voice released by the ChatGPT-maker, but he insisted the voice was not based on hers.
At issue is “Sky,” a voice OpenAI featured last week in the release of its more humanlike GPT-4o artificial intelligence technology.
In a demo, Sky was at times flirtatious and funny, seamlessly jumping from one topic to the next, unlike most existing chatbots.
The technology — and sound of the voice — quickly drew similarities to the Johansson-voiced AI character in the 2013 film “Her.”
Altman has previously pointed to the Spike Jonze-directed movie — a cautionary tale about the future in which a man falls in love with an AI chatbot — as inspiration for where he would like AI interactions to go.
He furthered speculation last week with a single-word post on X, formerly Twitter, saying “her.”
“The voice of Sky is not Scarlett Johansson’s, and it was never intended to resemble hers,” Altman said in a statement on Tuesday in a response to the controversy.
“We cast the voice actor behind Sky’s voice before any outreach to Ms. Johansson.
“Out of respect for Ms. Johansson, we have paused using Sky’s voice in our products. We are sorry to Ms. Johansson that we didn’t communicate better.”
The statement came after Johansson on Monday expressed outrage, saying she was “shocked, angered, and in disbelief that Mr Altman would pursue a voice that sounded so eerily similar to mine that my closest friends and news outlets couldn’t tell the difference.”
She said Altman had offered in September to hire her to work with OpenAI to create a synthetic voice, saying it might help people engaging with AI, but she declined.
Risk team disbanded
In a blogpost, the company explained that it began working to cast the voice actors in early 2023, “carefully considering the unique personality of each voice and their appeal to global audiences.”
Some of the characteristics sought were “a voice that feel timeless” and “an approachable voice that inspires trust,” the company said.
The five final actors were flown to San Francisco to record in June and July, it said, with their voices launched into ChatGPT last September.
“To protect their privacy, we cannot share the names of our voice talents,” OpenAI said.
“We believe that AI voices should not deliberately mimic a celebrity’s distinctive voice.”
So far in the AI frenzy, most tech giants have been reluctant to overly humanize chatbots and some observers expressed concern that OpenAI’s demo last week had gone too far.
Microsoft Vice President Yusuf Mehdi cautioned that AI “should not be human.”
“It shouldn’t breathe. You should be able to…understand (it) is AI,” he told AFP.
The Johansson dispute came just days after OpenAI admitted it disbanded a team devoted to mitigating the long-term dangers of artificial intelligence.
OpenAI began dissolving the so-called “superalignment” group weeks ago, integrating members into other projects and research.
Source: france24.com
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India’s next big focus: Artificial Intelligence

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Have you heard about ChatGPT? It’s an AI software that has become quite famous. Chances are, if you’ve been reading stories online, it has been involved in some way.
In today’s fast-changing world, where AI plays a big role, India has a great opportunity to grow. Rajeev Chandrashekhar, Minister of State for the Ministry of Information and Technology, believes India is ready to boost its tech economy. The plan is to invest in both public and private capital flows into the digital space in the real economy space. Chandrashekhar highlights key areas of focus for the next 5-7 years: electronics and microelectronics, telecom, high-performance computing semiconductors, cybersecurity, the future of the internet, automotive and EVs.
Investing in Innovation: Fueling the IndiaAI Mission
Prime Minister Narendra Modi has allocated $12 billion as seed capital into the research and innovation fund that will finance R&D and invest in the next wave of startups, including deep tech, AI, and other similar endeavors.
The government has recently approved over Rs 10,300 crore for the IndiaAI Mission, set to be invested over the next five years. This investment aims to drive various initiatives like building AI computing capacity, establishing innovation centers, creating datasets platforms, and supporting AI startups. The goal is to build cutting-edge AI computing infrastructure, benefiting from collaborations with over 10,000 GPUs.
Tailored Solutions for India: The IndiaAI Approach
India’s approach to AI is tailored to its specific needs. The IndiaAI mission aims to empower states like Kerala, which have untapped potential in the tech sector. By investing in such regions, the government hopes to unlock opportunities for young Indians and increase economic growth.
S Krishnan, secretary of the Ministry of Electronics and Information Technology (MeitY), notes the importance of developing AI models specific to India. While foreign models like ChatGPT 4 can handle Indian languages, they may carry biases due to the data they’re trained on.
Safe & Secure India: Learning from Global Experiences
India’s stance on AI regulation is practical. By observing and learning from other countries’ experiences, India aims to develop effective regulations without hindering innovation. Krishnan also said that India might hold an advantage over other nations by entering AI regulations later, as it can study and learn from the mistakes made by other countries.
The MeitY secretary also raised concerns regarding the potential job losses due to AI. However, he pointed out that India might not be as heavily affected due to its substantial pool of engineers already familiar with the technology. Nonetheless, he underscored the necessity for significant efforts in retraining and upskilling. India’s prioritisation of technology, particularly AI, reflects its ambition to drive progress and prosperity in the digital age.
Source: ddnews.gov.in
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