With the theme of Unlocking Business Growth with AI & Data Performance, DATAMATIXX Summit and Awards 2023 turned the focus on the significant impact of data-driven strategies and artificial intelligence on business growth and performance.

Anjali Malthankar, National Strategy Director, Tonic Worldwide, moderated a panel discussion on The Power of Data Science, Influencing Smart Business Decision at the event that was held on July 28, 2023 in Mumbai. The esteemed panellists included:

Anant Ranjan, VP - Growth and Initiatives, Mobavenue Media

Nikhil Kurian, Head of Digital, Tata Motors

Suchit Sikaria, CBO, Sugar Cosmetics

Shibu Shivanandan, Founder and MD, Pivotroots

Yatnesh Pandey, VP - Marketing, GreenPly

Speaking on the power of data Science and how it influences business decision-making, Anjali Malthankar said, Power of data science is the new essential for decision-making in any business and brand building overall. She went on to ask the panellists as to how important data science is in decision-making in business building today.

Anant Ranjan noted, There have been a lot of transitions taking place in terms of business. When we started one of the first e-commerce companies, it was very difficult to educate people in terms of digital sites. As we see, now most of the brands are on this digital site and we can say digital has opened the eyes of brands.

Speaking about the automobile industry, Nikhil Kurian pointed out that people never change their car frequently. He added, In such an industry, data plays a really important role in decision-making in two ways. One is the conventional research that goes into the invention of cars, but today it is unlocking through social and passive listening of designing the car.

For Shibu Shivanandan, The basic crack is understanding the data and the insight that you are going to build on it. Data science and data analytics are something that go from the research phase to the bottom of funnel conversion optimisation. Data is very important and conventional at each stage and brands and advertisers need to understand the kind of insights we should be deriving and the outcome of that decision.

From our perspective, data does two things for us very importantly, said Suchit Sikaria, adding, One is it removes subjectivity at an individual level and aggregates the subjectivity at the mass level. For individual level, people who are driving the business, it is difficult to keep the biases away and I think data helps us do that so that we dont impose our views on our customers. On the other hand, it is difficult to understand consumers because every consumer of ours behaves differently every day, thats where data also helps in trying to bring trends insights from a large number of transactions and behaviors we record for our consumers.

Focussing more on the contextual side of data science, Yatnesh Pandey said, Earlier, the capital was the denominator to succeed in any business, but now the competition is more towards the speed and time of the equation and there are certain tools behind it. Now, the kind of foreside you needed, you need to decision science into the place to make your business succeed.

These are edited excerpts. For the complete discussion, please watch below:

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The power of data science - Influencing smart business decisions - Adgully

Welcome to the inaugural edition of "This Week in AI" on KDnuggets. This curated weekly post aims to keep you abreast of the most compelling developments in the rapidly advancing world of artificial intelligence. From groundbreaking headlines that shape our understanding of AI's role in society to thought-provoking articles, insightful learning resources, and spotlighted research pushing the boundaries of our knowledge, this post provides a comprehensive overview of AI's current landscape. Without delving into the specifics just yet, expect to explore a plethora of diverse topics that reflect the vast and dynamic nature of AI. Remember, this is just the first of many weekly updates to come, designed to keep you updated and informed in this ever-evolving field. Stay tuned and happy reading!

The "Headlines" section discusses the top news and developments from the past week in the field of artificial intelligence. The information ranges from governmental AI policies to technological advancements and corporate innovations in AI.

AI Titans Pledge Responsible Innovation Under Biden-Harris Administration

The Biden-Harris Administration has secured voluntary commitments from seven leading AI companies - Amazon, Anthropic, Google, Inflection, Meta, Microsoft, and OpenAI - to ensure the safe, secure, and transparent development of AI technology. These commitments underscore three principles fundamental to the future of AI: safety, security, and trust. The companies have agreed to conduct internal and external security testing of their AI systems before release, share information on managing AI risks, and invest in cybersecurity. They also commit to developing technical mechanisms to ensure users know when content is AI-generated and to publicly report their AI systems' capabilities, limitations, and areas of appropriate and inappropriate use. This move is part of a broader commitment by the Biden-Harris Administration to ensure AI is developed safely and responsibly, and to protect Americans from harm and discrimination.

Stability AI Unveils Stable Beluga: The New Workhorses of Open Access Language Models

Stability AI and its CarperAI lab have announced the launch of Stable Beluga 1 and Stable Beluga 2, two powerful, open access, Large Language Models (LLMs). These models, which demonstrate exceptional reasoning ability across varied benchmarks, are based on the original LLaMA 65B and LLaMA 2 70B foundation models respectively. Both models were fine-tuned with a new synthetically-generated dataset using Supervised Fine-Tune (SFT) in standard Alpaca format. The training for the Stable Beluga models was inspired by the methodology used by Microsoft in its paper: "Orca: Progressive Learning from Complex Explanation Traces of GPT-4. Despite training on one-tenth the sample size of the original Orca paper, the Stable Beluga models demonstrate exceptional performance across various benchmarks. As of July 27th, 2023, Stable Beluga 2 is the top model on the leaderboard, and Stable Beluga 1 is fourth.

Spotify CEO Hints at Future AI-Driven Personalization and Ad Capabilities

During Spotify's second-quarter earnings call, CEO Daniel Ek hinted at the potential introduction of additional AI-powered functionality to the streaming service. Ek suggested that AI could be used to create more personalized experiences, summarize podcasts, and generate ads. He highlighted the success of the recently launched DJ feature, which delivers a curated selection of music alongside AI-powered commentary about the tracks and artists. Ek also mentioned the potential use of generative AI to summarize podcasts, making it easier for users to discover new content. Furthermore, Ek discussed the possibility of AI-generated audio ads, which could significantly reduce the cost for advertisers to develop new ad formats. These comments come as Spotify seeks a patent for an AI-powered "text-to-speech synthesis" system, which can convert text into human-like speech audio that incorporates emotion and intention.

The "Articles" section presents an array of thought-provoking pieces on artificial intelligence. Each article dives deep into a specific topic, offering readers insights into various aspects of AI, including new techniques, revolutionary approaches, and ground-breaking tools.

ChatGPT Code Interpreter: Do Data Science in Minutes

This KDnuggets article introduces the Code Interpreter plugin by ChatGPT, a tool that can analyze data, write Python code, and build machine-learning models. The author, Natassha Selvaraj, demonstrates how the plugin can be used to automate various data science workflows, including data summarization, exploratory data analysis, data preprocessing, and building machine-learning models. The Code Interpreter can also be used to explain, debug, and optimize code. Natassha emphasizes that while the tool is powerful and efficient, it should be used as a baseline for data science tasks, as it lacks domain-specific knowledge and cannot handle large datasets residing in SQL databases. Natassha suggests that entry-level data scientists and those aspiring to become one should learn how to leverage tools like Code Interpreter to make their work more efficient.

Textbooks Are All You Need: A Revolutionary Approach to AI Training

This KDnuggets article discusses a new approach to AI training proposed by Microsoft researchers, which involves using a synthetic textbook instead of massive datasets. The researchers trained a model called Phi-1 entirely on a custom-made textbook and found that it performed impressively well in Python coding tasks, despite being significantly smaller than models like GPT-3. This suggests that the quality of training data can be as important as the size of the model. The Phi-1 model's performance also improved when fine-tuned with synthetic exercises and solutions, indicating that targeted fine-tuning can enhance a model's capabilities beyond the tasks it was specifically trained for. This suggests that this textbook-based approach could revolutionize AI training by shifting the focus from creating larger models to curating better training data.

Latest Prompt Engineering Technique Inventively Transforms Imperfect Prompts Into Superb Interactions For Using Generative AI

The article discusses a new technique in prompt engineering that encourages the use of imperfect prompts. The author argues that the pursuit of perfect prompts can be counterproductive and that it's often more practical to aim for "good enough" prompts. Generative AI applications use probabilistic and statistical methods to parse prompts and generate responses. Therefore, even if the same prompt is used multiple times, the AI is likely to produce different responses each time. The author suggests that rather than striving for a perfect prompt, users should make use of imperfect prompts and aggregate them to create effective prompts. The article references a research study titled "Ask Me Anything: A Simple Strategy For Prompting Language Models" which proposes a method of turning imperfect prompts into robust ones by aggregating the predictions of multiple effective, yet imperfect, prompts.

The "Learning Resources" section lists useful educational content for those eager to expand their knowledge in AI. The resources, ranging from comprehensive guides to specialized courses, cater to both beginners and seasoned professionals in the field of AI.

LLM University by Cohere: Your Gateway to the World of Large Language Models

Cohere's LLM University is a comprehensive learning resource for developers interested in Natural Language Processing (NLP) and Large Language Models (LLMs). The curriculum is designed to provide a solid foundation in NLP and LLMs, and then build on this knowledge to develop practical applications. The curriculum is divided into four main modules: "What are Large Language Models?", "Text Representation with Cohere Endpoints", "Text Generation with Cohere Endpoints", and "Deployment". Whether you're a new machine learning engineer or an experienced developer looking to expand your skills, the LLM University by Cohere offers a comprehensive guide to the world of NLP and LLMs.

Free From Google: Generative AI Learning Path

Google Cloud has released the Generative AI Learning Path, a collection of free courses that cover everything from the basics of Generative AI to more advanced tools like the Generative AI Studio. The learning path includes seven courses: "Introduction to Generative AI", "Introduction to Large Language Models", "Introduction to Image Generation", "Attention Mechanism", "Transformer Models and BERT Model", "Create Image Captioning Models", and "Introduction to Generative AI Studio". The courses cover a range of topics, including Large Language Models, Image Generation, Attention Mechanism, Transformer Models, BERT Model, and Image Captioning Models.

The "Research Spotlight" section highlights significant research in the realm of AI. The section includes breakthrough studies, exploring new theories, and discussing potential implications and future directions in the field of AI.

The Role of Large Language Models in the Evolution of Data Science Education

The research paper titled "The Role of Large Language Models in the Evolution of Data Science Education" discusses the transformative impact of Large Language Models (LLMs) on the roles and responsibilities of data scientists. The authors argue that the rise of LLMs is shifting the focus of data scientists from hands-on coding to managing and assessing analyses performed by automated AI systems. This shift necessitates a significant evolution in data science education, with a greater emphasis on cultivating diverse skillsets among students. These include creativity informed by LLMs, critical thinking, programming guided by AI, and interdisciplinary knowledge.

The authors also propose that LLMs can play a significant role in the classroom as interactive teaching and learning tools. They can contribute to personalized education and enrich learning experiences. However, the integration of LLMs into education requires careful consideration to balance the benefits of LLMs while fostering complementary human expertise and innovation. The paper suggests that the future of data science education will likely involve a symbiotic relationship between human learners and AI models, where both entities learn from and enhance each other's capabilities.

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This Week in AI, July 31: AI Titans Pledge Responsible Innovation ... - KDnuggets

Indian Institute of Technology (IIT) Guwahati has exciting news for all aspiring data scientists and AI enthusiasts. They have introduced an online Bachelor of Science (Hons) degree program in Data Science and Artificial Intelligence on the renowned online learning platform, Coursera. Importantly, you can get IIT admission without JEE exam.

The answer is simple: anyone passionate about data science and AI and looking to kickstart or advance their career in these cutting-edge fields. The program offers a comprehensive curriculum covering both foundational and specialized topics, ensuring that students gain up-to-date knowledge and hands-on experience in Data Science and AI. The best part is that this flexible program is designed to accommodate individuals with diverse commitments, allowing them to study online at their own pace and convenience.

Whether you are a high school graduate from a science or non-science background, a professional seeking to enhance your skills, an aspiring entrepreneur, or someone looking to switch careers, this program caters to all. Even if you are already pursuing another degree, you can still earn a valuable second degree from a prestigious institution like IIT Guwahati.

Application Opens: July 19, 2023

Application Deadline: September 10, 2023

Application Decision Release: October 1, 2023

Classes Commence: October 30, 2023

For applicants without JEE (Advanced) registration, there is a straightforward process. You are required to complete the Mathematics Essentials online course specifically designed for this program. Upon completion, you will undertake a basic mathematics test, and based on your overall performance in Class 10, Class 12 examinations, and the Mathematics Essentials test, you will be ranked. Admission offers will be communicated via email on October 1, 2023.

If you already have a JEE (Advanced) registration, you are in luck. You will receive direct admission into the program without the need for the Mathematics Essentials course or qualifier test. The admission process is simplified for JEE (Advanced) registrants.

For those without JEE (Advanced) registration, the qualifier test entails a 4-module-long mathematics preparatory course and a Mathematics aptitude test. After completion, you can appear for the qualifier test, and your score will determine your application's fate.

IIT Guwahati promises to equip students with the digital skills needed to thrive in the modern workforce. Graduates will be well-versed in implementing the latest AI and data science techniques across various domains, ensuring their success in their chosen careers.

The program also offers job placement support, and students gain access to Coursera's skill-bred recruitment platform, Coursera Hiring Solutions, making the transition to their dream careers even more attainable.

So, if you have a passion for data science and AI and dream of studying at IIT Guwahati, this is your chance. Embrace the opportunity and apply before the September 10, 2023 deadline to embark on an exciting journey of knowledge and possibilities in Data Science and Artificial Intelligence.

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Get into IIT Guwahati, no JEE exam required; land job too; here is how - HT Tech

Jaime Settle, the Cornelia Brackenridge Talbot Associate Professor of Government and Data Science at William & Mary, was a collaborator on the most comprehensive research project to date exploring the impact of social media on American democracy.

The first findings were released July 27 from the study on how critical aspects of algorithms that determine what people see in their Facebook and Instagram feeds affected what they believed during the 2020 U.S. presidential election period.

Settle was among a group of academics from U.S. colleges and universities that worked in collaboration with researchers at Meta, the company that owns and operates Facebook and Instagram, to produce an initial batch of papers that were peer-reviewed and published in the journals Science and Nature.

The research showed that algorithms are extremely influential in on-platform social media experiences, and there is significant ideological segregation in political news exposure. But changes to critical aspects of algorithms that determine what subjects saw did not sway political beliefs.

Settle remarked on the high quality of data available to do what she called very rigorous social science focused on data and democracy, two key initiatives of William & Marys Vision 2026 strategic plan. She says this large-scale project was an admirable place to advance the understanding of potential solutions to combat polarization on social media platforms.

You have to start somewhere, and you have to explore what could work to tackle the problems we know exist, said Settle, the associate director of data science at W&M and the director of the Social Networks and Political Psychology Lab. Doing so could lead to some counterintuitive findings and unintended consequences because most of our really complex problems are going to have really complex solutions.

I think a lot of times the public or pundits will latch on to an idea that they think is a panacea: if we could just fix this one big problem it would solve a lot of the other problems we have related to our political system or social media. However, the findings of these papers speak to the idea that there arent simple solutions.

The project has spanned more than three years and required intensive work from the likes of Settle.

This was one of the great honors of my professional life, Settle said. I learned an incredible amount from my colleagues, both the other academic researchers as well as the data scientists and researchers at Meta. The sophistication of the processes and the methods and the thoroughness with which we were able to think things through was an exciting intellectual challenge, and it was fun to be a part of that.

The team proposed and selected specific research questions and study designs with a clear agreement that the only reasons Meta could reject such designs would be for legal, privacy or infeasibility reasons. Meta could not restrict or censor findings, and the academic lead authors had final say over writing and research decisions.

Internal researchers at Meta initiated the partnership with Professor Talia Jomini Stroud, founder and director of the Center for Media Engagement at the University of Texas at Austin, and Professor Joshua A. Tucker, co-founder and co-director of the Center for Social Media and Politics at New York University and director of the Jordan Center for the Advanced Study of Russia.

The core research team consisted of 15 additional academic researchers, including Settle, with expertise in four areas political polarization, political participation, (mis)information transmission online and beliefs about democratic norms and the legitimacy of democratic institutions.

Settle was a co-lead author on a study called Like-minded Sources on Facebook Are Prevalent but Not Polarizing along with professors Brendan Nyhan from Dartmouth, Emily Thorson from Syracuse and Magdalena Wojcieszak from University of California, Davis. The study presented data from 2020 for the entire population of active adult Facebook users in the U.S., showing that content from politically like-minded sources constitutes the majority of what people see on the platform, but political information and news represent only a small fraction of these exposures.

This speaks to this concern about echo chambers, Settle said. Everyone believes that were so polarized and we all dislike each other because we are in bubbles where we only encounter points of view that are similar to our own. What we show is that randomly assigning some people to encounter less content from like-minded sources didnt have these beneficial effects that some people thought it would.

Settle said her research team didnt just downrank political content; it downranked all content that came from like-minded sources. There is a lot more to politics on social media than just explicitly political content, she said. Even non-political content can send an important signal about other peoples political identities. And since political content is such a small fraction of what people encounter on Facebook, we wanted to design a treatment that would have a larger effect on the user experience.

Our treatment didnt make attitudes less extreme, nor did it make attitudes related to affective polarization any less severe, Settle continued. Interestingly, one of the secondary findings is that even though the people in our treatment group saw less content from like-minded sources, when they did see that content, they were more likely to engage and interact with it. I think this shows that you cant use algorithms alone to entirely override the psychological predispositions that people have.

This finding reinforces the takeaway from Settles 2018 book Frenemies: How Social Media Polarizes America. In it, Settle contends that any solution to polarization must take into account both algorithms and a persons psychological disposition. The book recently won a prestigious award from the American Political Science Association recognizing it as the best book published at least five years ago on the topic of elections, political behavior, and voting.

I think this study really speaks to my previous work in that we were able to work with Meta to do this really important intervention on the algorithm and alter the experience that people had on the site, but that alone is not enough, Settle said. Its not a simple fix. Well have to push further as we brainstorm and think about other potential solutions to polarization.

Settle said more studies related to this project are expected to be published over the next several months. She was a co-lead author on two more papers one about behavioral polarization on social media, such as friending and defriending or decisions to connect or disconnect from groups, and the other about content from untrustworthy sources.

Good science takes time, Settle said, and she is confident in the integrity of the process used in the study.

We hope that this can serve as a model and launching point for future collaborations, not just with Meta but other tech companies as well, Settle said. Itll be up to them to decide how they want to proceed moving forward, but weve learned a lot from this collaboration that should be useful for the process of science moving forward.

Nathan Warters, Communications Specialist

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Is seeing believing? W&M professor collaborates on study ... - William & Mary

Two years after the project ceremonially broke ground at 34th and Chestnut streets, members of the Penn community gathered on Wednesday for the topping off of Amy Gutmann Hall. A time-honored tradition in construction, the signing and placement of the final wood panel signaled the completion of the new School of Engineering and Applied Science buildings frame.

A hub for data science on campus and for the Philadelphia community when it officially opens next summer, Amy Gutmann Hall will embolden interdisciplinary work in a field that is transforming all facets of engineering education, and of course research and innovation, said Penn Engineerings Nemirovsky Family Dean Vijay Kumar.

The new facility, with next-generation hybrid classrooms and laboratories, will be equipped to support exploration that advances graphics and perception, privacy and security, computational social science, data-driven medical diagnostics, scientific computing, and machine learning. It will also allow for the development of safe, explainable, and trustworthy artificial intelligence, said Kumar.

Eighty-two truckloads of mass timbera more sustainable and efficient product than steel or concretehave been used to construct the 116,000-square-foot, six-story building. Philadelphias tallest new mass timber structure, Amy Gutmann Hall will evoke a warm, welcoming environment with its exposed wood throughout its spaces.

The building is not so much built as it is engineered and then prefabricated with extraordinary precision, said President Liz Magill. She noted how the techniques used to create the new building relied heavily on advanced computation and data, which is precisely the kind of work that this building will foster when its completed.

The building reflects the use, and the use helped determine the building, Magill said.

Amy Gutmann Hall, designed by Lake|Flato and KSS Architects, currently under construction led by Gilbane Building Company, and named for Penns longest serving president, has been made possible thanks to a transformative $25 million commitment to Penn Engineering from Harlan Stone in 2019, a University Trustee, member of Penn Engineerings Board of Advisors, and chair of the schools Technical Advisory Board. Stone, a School of Arts & Sciences alumnus and Penn Engineering parent, said at the gathering that he imagines the new building as a place that will produce new ideas, methodologies, and paradigms of how data can impact humanity for good.

After partaking in a celebratory toast, the crowd cheered as a crane erected the wooden panel, which had been signed by those who took a very bold idea and made it a compelling reality, said Magill.

We together celebrate this milestone in the creation of Amy Gutmann Hall, Magill said. A testament to the belief that collaborative research and learning can solve some of the worlds most urgent problems. Within this building, may the insights that we gain through data science help us harness new knowledge and understanding to create a better world. I know we all cannot wait to see these innovations come to life.

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'Topping off' Amy Gutmann Hall | Penn Today - Penn Today