Рубрика: AI

At GitHub Constellation 2024 in Bengaluru, GitHub CEO Thomas Dohmke passionately spoke about the company’s deep connection with India and its vibrant open-source community. “You commit changes, your commit saves lives,” he said about the open source ecosystem of India.

Dohmke said that India stands at a pivotal moment, poised to lead the global software development community. Dohmke emphasised, “By 2027, India will overtake the United States as the largest software developer community on Earth.”

He stressed the importance of open source, likening it to a Jenga tower that relies on every developer’s participation. “The world depends on open source. The world depends on you,” he declared, reinforcing the critical role of Indian developers in driving innovation.

Addressing the misconception that India only consumes open source without contributing significantly, he highlighted India’s active participation in global open-source projects. He pointed out that in the open-source ecosystem, identity and background are irrelevant.

Interestingly, Zerodha CTO Kailash Nadh shared contrasting thoughts about India’s open-source ecosystem. “We have one of the highest concentrations of engineers and software developers in the world. But, if you look at the number of open source projects that come out of India, we would rank really, really, really low, and that is so disproportionate. It is not just sad, it’s also scary,” Nadh told AIM.

To foster this ecosystem and assist developers across India and beyond, it has partnered with Indian IT firm Infosys and opened the first GitHub Center of Excellence at Infosys, Bengaluru.

Meanwhile, Dohmke emphasised the collaborative nature of open source. “On a Sunday evening, somebody sent me a pull request. I don’t care who that person is. All I care about is that thing that we have decided to collaborate on… It doesn’t matter what religion, nationality, where I’m sitting in some organisational chart, none of that matters in open source,” he asserted.

GitHub Talks in Hindi

GitHub’s latest innovation, Copilot Workspace, exemplifies this spirit of collaboration. This new environment allows developers to move from issue to pull request seamlessly, all in natural language. Dohmke invited GitHub’s senior manager for international developer relations Karan M.V to demonstrate Copilot Workspace live on stage.

Karan showcased how Copilot could help create a 3D Lego brick on a webpage, highlighting its capabilities in understanding and generating code from natural language instructions. He also demonstrated Copilot Workspace’s ability to interact in Hindi in the Devanagari script, where the Copilot replied, explaining what the code was about.

Karan’s live coding session culminated in building a 3D Lego house, seamlessly transitioning from idea to implementation, all within the Copilot Workspace environment.

“You shouldn’t have to speak English to code, create, or participate in open source,” Dohmke declared. The barriers to entry are crumbling, making software development more accessible to a global audience. “Machines can now speak, understand and interpret nearly every major human language — Hindi, Portuguese, Indonesian, German, Kannada,” said Dohmke.

“Coding is as simple as the thoughts in your mind, and now you can see how natural language is the most powerful thing you have,” Dohmke stated.

Coding in Hindi on GitHub Copilot was also highlighted by Satya Nadella at Microsoft Build 2024. “Think about it — every person can now start programming, whether it’s in Hindi, Brazilian, or Portuguese, and bring back the joy of coding in their native language,” said Nadella.

Dohkme said that the age of AI has begun, and India’s developer community is at the forefront of this transformation. India currently has 13.2 million developers using GitHub, compared to approximately 20 million in the US. India also ranks second globally in the number of GenAI projects hosted on GitHub, following the US. The country hosts just under 30 million repositories.

“Developers are already 55% faster using Copilot,” Dohmke pointed out. This efficiency translates into significant time savings, allowing developers to focus more on creative and impactful aspects of their work.

India’s Love for Open-Source AI

Dohmke highlighted the convergence of India’s vast developer population and the emerging possibilities of AI, predicting it will supercharge the economy and fuel unprecedented demand for developers. This convergence is expected to simplify developers’ lives, allowing them to focus on innovation and creativity.

For decades, building software required understanding complex programming languages. Dohmke illustrated this with a reference to COBOL, a language from the 1950s still in use today. With tools like Copilot, the complexity barrier is being dismantled.

GitHub’s commitment to empowering India’s developer community is clear. As Dohmke aptly put it, “It all starts with open source. It all starts with you.”

The connection with India, Dohmke noted, is not just professional but also personal, recalling his visit to Bengaluru 16 years ago with his wife. “We loved our time here, and we almost moved to this beautiful country,” he reminisced, highlighting how India has held a special place in his heart ever since.

Quoting Gandhi, Dohmke ended the session, saying, “The future depends on what you do today. You are building your own future here in India.” He also compared the software race to the India and United States cricket match later this week. “You want to win that, right? And so you also want to win in software,” he concluded.

The post GitHub CEO Thomas Dohmke Embraces his Love for Indian Open Source Ecosystem appeared first on AIM.

Antler, the venture capital firm, announced plans to invest $10 million in early-stage Indian startups over the next six months. Partners Rajiv Srivatsa and Nitin Sharma will lead the initiative, targeting idea-stage founders with investments of $500,000 per company, totaling 20 investments.

Srivatsa shared the news on X, emphasising the timeliness of building startups in India. “NOW is the best time to build a startup in India!,” he posted.

He also provided further details on the investment: “Towards this, Nitin Sharma & I are committing $10M from @AntlerIndia into exceptional idea-stage founders in the coming 6 months. $500K per company X 20 investments – We will work with you in your ambiguous -1 to 0 phase and get you >$1Mn in total within 6-9 months of starting up!”

NOW is the best time to build a startup in India!
Towards this, @nitinsharma1 & I are committing $10M from @AntlerIndia into exceptional idea-stage founders in the coming 6 months.
$500K per company X 20 investments – We will work with you in your ambiguous -1 to 0 phase and…

— Rajiv Srivatsa (@telljeeves) June 12, 2024

Srivatsa encouraged aspiring entrepreneurs to write “Build” in the comments to get a message from the Antler team. He also invited those with startup ideas or interest to tag others and join an AMA next week for more details.

Antler launched the $285 million Antler Elevate Fund in June 2023, focusing on Series A rounds and later stages, with investments ranging from $1 million to $10 million.

Since its establishment in 2018 in Singapore, Antler has expanded its investments and networks globally, covering regions such as the U.S., Europe, Africa, and Asia, including Vietnam, Japan, and Malaysia.

The post Antler to Invest $10 Million in Early Stage Indian Startups appeared first on AIM.

Recently, Adobe faced severe backlash for forcing users to accept its privacy policy before allowing them to use its products. The concern was mainly around Adobe’s rights to use and modify user content to operate and improve its services.

Adobe accessing and analysing user content through automated and manual methods for various purposes, such as customer support, is another reason that left the users fuming, so much so that many contemplated abandoning Adobe.

The recent outcry may seem unwarranted, considering how most companies often collect user data to improve their products and services. However, the extent to which they collect such data is questionable.

• Google has confirmed the authenticity of a leak of 2,500 internal documents detailing data collection practices for its search engine.
• The documents suggest Google may use data previously stated not to be used for ranking, such as clicks and Chrome user data.
• The leak… pic.twitter.com/mkUEf4zLV2

— Jason Castellano (@TekOpinions) June 3, 2024

Why Collect Data?

“Millions of businesses, large and small, rely on Google Analytics to understand customer preferences and create better experiences for them,” said Vidhya Srinivasan, VP/GM at Google Ads, noting the impact of user data on business growth.

Companies like Meta and Google are notorious for offering free services and collecting your data in return – and you don’t have a choice here.

Unless you use a locally hosted service or an open-source software, it’s very rare that you would decline to accept a policy and still get to use the product.

What really matters here is how companies process your data.

A Quora user said, “You would recognise your name and bank account number, but knowing that your browser is collecting and storing your ‘session IDs’ wouldn’t tell you much because you probably don’t know what they are or what they can be used for.”

Data Collection is Good

A great example of data collection in healthcare is how the Apple Watch saved the life of Toralv Østvang, where the device (Apple Watch Series 4) detected a hard fall and automatically called emergency services.

The Apple Watch health data is encrypted on the device and in iCloud and Apple cannot read it. This way, users have granular control over which apps can access their health data.

Meanwhile, JPMorgan Chase developed an AI-powered fraud detection system that analyses customer behaviour and spending patterns. They used the tech to successfully protect their client Wei from a $99,101 fraudulent payment.

Another handy example of data collection being in favour of the users is when Google Maps collects your GPS data to show you routes with better traffic conditions and the fastest route possible.

Data, if collected with the right intent, can be of great help in improving the overall user experience.

But There are Caveats

After the recent announcement from Apple regarding integrating ChatGPT into their gadgets, Tesla CEO Elon Musk was furious and said if Apple integrated OpenAI at the core OS level, he would ban all Apple devices from his companies.

Similarly, Samsung had put a ban on ChatGPT after a misuse of the chatbot resulted in a sensitive data leak.

Then there’s Reddit’s plans to licence its user-generated content to AI companies for training their models, which is expected to generate $203 million in revenue over the next few years.

Likewise, Stack Overflow announced a partnership with OpenAI to improve the latter’s coding-related AI models using Stack Overflow’s database of over 58 million programming questions and answers.

Apart from selling data to train AI models, there have been other instances where the data stored to improve user experience was used by organisations for their own profit.

Not so long ago, the data of up to 87 million Facebook users was unethically obtained by Cambridge Analytica, predominantly to be used for political advertising. Meanwhile Verizon used “supercookies” to track millions of mobile customers’ internet browsing without their knowledge or consent.

Apparently, Vizio, a manufacturer of smart TVs, settled with the FTC for $2.2 million after the company was found tracking the viewing habits of millions of users on the sly.

Most users only want the companies to be upfront about what data they are collecting, how it will be used, and who it will be shared with. People should have the ability to opt-out of data collection or delete their data if desired.

There are other ways you can protect your online privacy such as using privacy centric search engines like DuckDuckGo and gradually switching to open-source software will enhance your online privacy by miles.

After all, like Geoffrey Moore, theorist and author of Crossing the Chasm, said: “Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.”

The post Everyone is Collecting Your Data, And You Know It appeared first on AIM.

Databricks today announced several innovations to its Mosaic AI platform to help customers build production-quality generative AI applications. The company is investing in three key areas: support for building compound AI systems, capabilities to improve model quality, and new AI governance tools.

Organisations are struggling to transition generative AI projects from pilot to full-scale production due to privacy, quality, and cost concerns. While foundation models have significantly improved, they still face challenges in producing consistently high-quality results. To address these issues, organisations are moving beyond deploying a single large model and instead adopting compound AI systems.

This approach utilises multiple components, including various models, retrievers, vector databases, and tools for evaluation, monitoring, security, and governance, resulting in higher production quality and more accurate, safe, and governed AI applications.

“We believe that compound AI systems will be the best way to maximise the quality, reliability, and measurement of AI applications going forward, and may be one of the most important trends in AI in 2024,” said Matei Zaharia, co-founder and CTO at Databricks. “Databricks is uniquely positioned to capitalise on these trends with the investments we’re making to improve quality, augmenting the model with real-time data and agents and tools to give it new capabilities it has little knowledge of.”

To support customers in building production-quality generative AI applications, Databricks is launching several new features:

Mosaic AI Agent Framework and Mosaic AI Tools Catalog help organisations build compound AI systems. The Agent Framework enables developers to quickly and safely build high-quality RAG (Retrieval-Augmented Generation) applications using foundation models and enterprise data. The Tools Catalog allows organisations to govern, share, and register tools using Databricks Unity Catalog, ensuring secure and governed use of tool-enabled models.

Mosaic AI Agent Evaluation is an AI-assisted evaluation tool that automatically determines if outputs are high-quality and provides an intuitive UI for gathering feedback from human stakeholders. This helps organisations deploy production-quality generative AI solutions.

Mosaic AI Model Training enables fine-tuning of open-source foundation models with an organisation’s private data, resulting in higher-quality results for specific use cases. These fine-tuned models are fully owned and controlled by the customer, and are faster and less expensive to serve compared to larger proprietary models.

Mosaic AI Gateway provides a unified interface to query, manage, and deploy any open-source or proprietary model, allowing customers to easily switch the large language models (LLMs) powering their applications without complex code changes. It offers usage tracking, guardrails, governance, and monitoring to ensure quality and control spending.

Several Databricks customers, including Corning, Ford Direct, and Lippert, have already benefited from these new capabilities in building their generative AI applications. By leveraging the Databricks Data Intelligence Platform and Mosaic AI, they have improved retrieval speed, response quality, accuracy, and confidence in deploying to production.

The new Mosaic AI capabilities are part of Databricks’ ongoing commitment to helping customers harness the power of generative AI while maintaining data privacy, quality, and cost-effectiveness. As organisations continue to explore AI’s potential, Databricks aims to provide the tools and platform necessary for building enterprise-ready AI applications.

The post Databricks Enhances Mosaic AI for Enterprise-Ready AI Applications appeared first on AIM.

Databricks announced it is open-sourcing its Unity Catalog, the industry’s first unified governance solution for data and artificial intelligence (AI) that works across clouds and data platforms. By open-sourcing Unity Catalog, the company aims to establish an open standard for interoperable data and AI governance.

Unity Catalog OSS offers a universal interface supporting multiple data formats and compute engines. It enables unified governance across tabular data, unstructured data, and AI assets like machine learning models. With open APIs and an Apache 2.0 licensed open-source server, it provides flexibility and avoids vendor lock-in.

“Our customers love Unity Catalog because it streamlines data access and governance at scale,” said Ali Ghodsi, CEO at Databricks. “We’re excited to open source Unity Catalog to drive the industry forward to an open standard for data and AI governance that gives customers openness and flexibility.”

Key features of Unity Catalog OSS include:
Interoperability across data formats, compute engines, and platforms.
Unified governance for all data and AI assets
Open architecture to maximise customer flexibility and choice

Several partners, including AWS, Google Cloud, Microsoft, Salesforce, Confluent, dbt Labs, Immuta, Informatica, and Unstructured, expressed support for Unity Catalog OSS. They praised Databricks’ move as enabling greater customer flexibility and aligning with open ecosystem principles.

“AWS welcomes Databricks’ move to open source Unity Catalog. AWS is committed to working with the industry on open-source solutions that enable choice and interoperability for customers,” said Chris Grusz, managing director of technology partnerships at AWS.

Customers like AT&T, Nasdaq, and Rivian also welcomed the news, stating it will help eliminate data silos, scale platforms, and enable working across data without vendor lock-in concerns.

Unity Catalog OSS will be available in public preview in Q3 2024. To learn more, visit the Databricks website or attend the Data + AI Summit on June 26-29. Click here to watch the keynote.

Databricks helps organisations take control of their data with its unified data and AI platform, which is used by over 10,000 customers. Headquartered in San Francisco, Databricks was founded by the original creators of Apache Spark, Delta Lake, and MLflow.

The post Databricks Open Sources Unity Catalog for Data and AI Governance Across Platforms appeared first on AIM.

As various industries explore new applications for advanced intelligence, generative artificial intelligence (GenAI) continues to gain traction. Its ability to process complex data, uncover hidden patterns, automate tasks and generate creative content emerges as a transformative tool to advance insights and productivity.

Sanmugam Aravinthan
Credit: IQVIA

However, a key hurdle to successful widespread adoption remains. GenAI’s limited language fluency is a significant handicap in the widespread adoption and use of this transformative technology.

Current GenAI systems are largely trained on data from online resources and databases, which tend to be dominated by a few major languages such as English, Spanish, and Chinese. The surplus of data related to a few globally dominant languages creates an imbalance. Considering the thousands of additional languages spoken around the globe, it can be assumed that a significant portion of global information is likely missing from current GenAI training datasets. This language bias could impart unintended drawbacks, potentially leading to skewed results or limited informational access for diverse populations.

Generative Artificial Intelligence in Action: Revolutionizing Life Sciences

This shortcoming is critically important in many fields, but particularly relevant in the life sciences industry. The life sciences sector is a prime target for GenAI technology due to its data overload. In fact, a recent survey indicates that GenAI investments in the life sciences industry has more than tripled in recent months. As a field at the forefront of discovery, the industry embraces advancements that accelerate research timelines, enhance data analysis and yield deeper insights.

Already, GenAI’s implementation has addressed several challenges in the life sciences industry. Through advanced analytical capabilities, organizations are empowered to improve critical safety surveillance measures with signal detection, data integration and automated reporting.

This technology allows for proactive monitoring of adverse drug or medical device reactions across diverse platforms, including social media. By leveraging ontologies and character recognition to train GenAI for pattern recognition, organizations can potentially predict and identify adverse events with greater accuracy, ultimately leading to improved patient safety.

Beyond safety surveillance, GenAI’s capabilities are also harnessed in the analysis of clinical data to identify suitable candidates for promising new therapies in clinical trials. This streamlining process could potentially lead to faster patient recruitment and, ultimately, shorter trial durations. Furthermore, GenAI's reach extends to patient interaction through chatbot functionalities. These chatbots gather patient symptoms and offer recommendations based on the information provided. This approach not only fosters patient engagement but also alleviates the workload burden of healthcare professionals.

Despite the potential for GenAI to improve healthcare outcomes, a key implication lies in its current language limitations. Current AI and GenAI models struggle to process information beyond a handful of dominant languages. This creates a blind spot for non-English speaking patients, potentially hindering GenAI's ability to revolutionize critical processes such as early detection of adverse events, patient recruitment for clinical trials, and advanced chatbot capabilities.

The Language Challenge: Generative Artificial Intelligence’s Language Blind Spot

The digital language divide poses a significant challenge for deploying advanced technologies across various industries. However, the life sciences industry stands to gain immense benefits from broader GenAI capabilities, potentially leading to dramatic improvements in patient outcomes.

Addressing this language gap now is crucial to ensuring future technologies’ ability to leverage vast, multilingual datasets reflecting the global healthcare landscape. Expanding training models to encompass multilingual data, incorporating diverse patient information and prioritizing language-agnostic development are all essential steps to increasing accessibility to healthcare and life sciences advancements on a global scale.

GenAI's usefulness within life sciences hinges on it ability to incorporate multilingual data

With this context in mind, how do organizations ensure the development of advanced technology that safeguards patients worldwide?

Increasing the amount of digital data utilized to train GenAI effectively is a critical first step. This requires improving global access to digital devices and internet services to expand the number of languages with sufficient digital footprints. The current limitations of many languages’ digital presence stem from a lack of access to digital services, hindering data collection for training purposes. Initiatives promoting high-speed broadband and internet-enabled devices can bridge the gap between languages by tackling this digital divide.

Strengthening GenAI's language capabilities extends beyond just the number of languages sourced in its development. It is crucial to incorporate language variations and dialects in the training of advanced technology. Biases against non-standard forms of language can be just as detrimental to patient safety as various language limitations. Limiting GenAI's exposure to language variations can lead to unintended biases. For GenAI to effectively detect abnormalities and concerns related to patient outcomes, it must be able to understand real-world conversations, including vernacular, slang and code-switching.

Ensuring Equitable Global Outcomes with Generative Artificial Intelligence

As GenAI takes root in the life sciences industry, acknowledging its limitations alongside its potential is critical for future success. For decades, healthcare and life sciences have faced challenges reaching diverse populations and improving research participant demographics.

Studies continue to reveal a concerning lack of representation even as organizations make concerted efforts at increasing access to diverse populations. These stark misrepresentations perpetuate global health inequities and limit the lifesaving potential of new therapeutics. The industry already struggles with existing underrepresentation and accessibility concerns in patient recruitment. As such, not recognizing GenAI's current language limitations will only deepen these existing problems.

GenAI holds immense promise for revolutionizing healthcare and life sciences, but its current language capabilities pose a significant barrier to achieving equitable patient outcomes. By expanding access to multilingual and diverse patient data, increasing the global availability of digital services and embracing language variations, organizations can take steps to bridge the digital language divide.

Addressing these shortcomings now will ensure GenAI's transformative power can create a future where healthcare and life sciences advancements benefit all populations, regardless of language.

About the Author: Sanmugam Aravinthan is the Senior Director, Development at IQVIA Vigilance Detect. As Senior Director, Development of IQVIA’s Vigilance Detect, Aravinthan’s main area of focus is on driving the technology development and delivery of a productized solution that enables optimized approach in detecting adverse events, product quality complaints and other safety risks in large-scale structured and unstructured data. He has 20+ years of industry experience in driving Software Engineering and Systems Development, with the past 10 years in the pharmaceutical and life sciences industries. He has a strong track record in directing software product development, managing technology delivery of clients and leading pharmacovigilance operations in client implementations. He has a US patent titled “System and method for multi-dimensional profiling of healthcare professionals.”