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AI is finally living up to the hype that has surrounded it for decades. While AI is not (yet) the saviour of humanity, it has progressed from concept to reality, and practical applications are improving our environment.

However, much like Clark Kent, many of AI’s astounding exploits are veiled, and its impacts can only be seen when you look past the ordinary mask. Consider BNP Paribas Cardif, a large insurance corporation with operations in more than 30 countries. Every year, the organisation handles around 20 million client calls. They can evaluate the content of calls using speech-to-text technology and natural language processing to satisfy specific business purposes such as controlling sales quality, understanding what customers are saying and what they need, getting a sentiment barometer, and more.”

Consider AES, a leading producer of renewable energy in the United States and around the world. Renewable energy necessitates far more instruments for management and monitoring than traditional energy. AES’ next-level operational effectiveness is driven by data science and AI, which provide data-driven insights that supplement the actions and decisions of performance engineers. This guarantees that uptime requirements are met and that clients receive renewable energy as promptly, efficiently, and cost-effectively as feasible. AES, like Superman, is doing its part to save the planet.

These are only a few of the many AI applications that are already in use. They stand out because, until now, the potential of AI has been constrained by three major constraints:

  Compute Power

Traditionally, organizations lacked the computing power required to fuel AI models and keep them operational. Companies have been left wondering if they should rely only on cloud environments for the resources they require, or if they should split their computing investments between cloud and on-premise resources.

  Centralized Data

Data has traditionally been collected, processed, and stored in a centralised location, sometimes referred to as a data warehouse, in order to create a single source of truth for businesses to work from.

Maintaining a single data store simplifies regulation, monitoring, and iteration. Companies now have the option of investing in on-premises or cloud computation capability, and there has been a recent push to provide flexibility in data warehousing by decentralizing data.

Data localization regulations can make aggregating data from a spread organization unfeasible. And a fast-growing array of edge use cases for data models is undermining the concept of unique data warehouses.

  Training Data

A lack of good data has been a major impediment to the spread of AI. While we are theoretically surrounded by data, gathering and keeping it may be time-consuming, laborious, and costly. There is also the matter of bias. When designing and deploying AI models, they must be balanced and free of bias to ensure that they generate valuable insights while causing no harm. However, data, like the real world, has bias. And if you want to scale your usage of models, you’ll need a lot of data.

To address these issues, businesses are turning to synthetic data. In fact, synthetic data is skyrocketing. According to Gartner, by 2024, 60% of data for AI applications would be synthetic. The nature of the data (actual or synthetic) is unimportant to data scientists. What matters is the data’s quality. Synthetic data eliminates the possibility of prejudice. It’s also simple to scale and less expensive to obtain. Businesses can also receive pre-tagged data with synthetic data, which drastically reduces the amount of time and resources required to build and generate the feedstock to develop your models.

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Cornell University: Shaping The Future Of Technology Through Data Science And Statistics

Cornell University was founded in 1865 in Ithaca, New York by Andrew D. White and Ezra Cornell, the latter famously stating “I would found an institution where any person can find instruction in any study.” The founders could not have envisioned the full extent of modern data science, of course, but scientific research of all types has been at the heart of Cornell’s mission since its beginning. Statistics itself – the precursor or original discipline underlying data science – first came to prominence at Cornell after World War II, with the presence of two seminal figures in the field, Jack Kiefer and Jacob Wolfowitz, as faculty members. Since then, Cornell’s Department of Statistics and Data Science (as it is now called) has hosted and continues to be the home of many prominent researchers in theoretical and applied statistical methods.  

Data Science Programs at Cornell

Cornell University offers two undergraduate degrees in statistics and data science, as well as the M.S. and Ph.D., all of which enroll numerous students who find successful careers upon graduation. But its flagship Master of Professional Studies in Applied Statistics, or M.P.S., is unique and is the only program of its type offered by an Ivy League university. The M.P.S. is a two-semester Master’s degree program that provides training in a broad array of applied statistical methods. It has several components: (i) a theoretical core focusing on the underlying mathematical theory of probability and statistical inference (with a 2-year calculus prerequisite); (ii) a wide selection of applied courses including (but not limited to), data mining, time series analysis, survey sampling, and survival analysis; (iii) certification in the SAS® programming language (required); (iv) a professional development component including in-depth training in career planning and job searching, interviewing and resume writing, professional standards and etiquette, etc.; and (v) a year-long, hands-on, start-to-finish professional data analysis “capstone” project.  

The Dynamic Leadership

Dr. John Bunge is the founding director of the M.P.S., in 1999-2000, and served in that role for 12 years. The position was then held by another Statistics professor, and at the end of his (6-year) term Dr. Bunge again became Director and will continue through 2023. Dr. Bunge has witnessed the program growth from an initial enrollment of 6 students to its current steady-state of 60, which is about the institute’s maximum capacity. Interestingly, the number of M.P.S. applications seems to continue to increase so that the demand for the available spaces becomes ever more intense. “We are content with many of the decisions we made in designing the program (as long ago as the 1990’s), but we continue to monitor professional trends in data science and to adapt our program accordingly,” Dr. Bunge said. “In particular in the past decade we have added a second “concentration” to the M.P.S., so that students may now specialize more in classical (and modern) statistical data analysis; or (the second concentration) in more computationally oriented data science, including topics such as Python programming, database management and SAS, and big data management and analysis.”  

Prominent Features of the Program

   

Offering Extraordinary Industry Exposure

The main type of practical exposure offered to M.P.S. students is the M.P.S. project. During the fall semester, the faculty identifies a number of current applied research projects, some within Cornell or from Weill Cornell Medicine (the university’s medical school in New York City), some from external clients in the private or nonprofit sectors. The M.P.S. class is then divided randomly into teams of 3 or 4 students, and each team ranks the available projects by preference. The faculty then assigns projects to teams, attempting to accommodate preference as well as possible (this is known as the “fair item assignment” problem). Teams then have until the end of the spring semester to complete their projects. In the course of this, the team must communicate continuously with the client; formulate and re-formulate the problem in statistical terms; organize and manage relevant data (provided by the client); carry out statistical analyses using suitable computational methods and software; and finally provide both a written and an oral presentation of the results. Upon completion, the projects are evaluated by the students themselves, the clients, and the faculty, and each year one or two “best project” awards are made. This is the closest experience to actual on-the-job statistical consulting that can be obtained within the academy, and it is very effective both as a learning process and as proof of competency for M.P.S. graduates. In addition, Cornell allows M.P.S. students to elect to take an additional semester of study, which then introduces the opportunity for an internship in the intervening summer, another form of practical exposure for students.  

Overcoming Academic and Industry Challenges

Dr. Bunge feels the most significant challenge is simple, and characteristic of any aspect of the technological or scientific enterprise: keeping abreast, or preferably ahead, of current developments. In practical terms, for example, what software will the students need to be familiar with? SAS® is still important but R is increasingly so, not to mention scripting languages such as Python, and big data resources or environments such as Hadoop. It is a major undertaking to stay current with developments in these areas much less to predict their future directions, and academics, while experts in their own fields, are less conversant with trends in industry, government, banking and so forth. From a broader perspective, what will be the industries of the future, and how will they apply data science? A forward-looking program cannot ignore, to take just three examples, quantum computing, genome editing (CRISPR), and for-profit space exploration (e.g., asteroid mining). These may seem like science fiction at present, but in no time at all, we will be sending our data science graduates to work in these fields, and we must prepare them accordingly, he said.  

Remarkable Accomplishments of the University

Adalend On Cardano Is The Future Of Defi

As part of the Cardano ecosystem, ADALend builds a scalable and decentralized lending protocol, which the Cardano community will regulate.

A new generation of flexible financial services for digital asset markets will be powered by the ADALend protocol, which will provide a foundation for speedy loan approval, automated collateralization, trustless custody, and liquidity in the digital asset markets.

Why Cardano?

Cardano (ADA) is a blockchain platform with various capabilities that will power the ADALend protocol. To produce a scalable, transparent, and resilient cryptocurrency, Cardano (ADA) uses cutting-edge technology. The fact that it is a publicly accessible blockchain network makes it one of the many well-known cryptocurrencies that have grown and developed rapidly in recent years. With Input Output Hong Kong (IOHK), Charles Hoskinson laid the framework in 2023 for what is unquestionably the most vital third-generation blockchain asset now available on the market.

A well-organized team is in place at Cardano (ADA), and the company has a clearly defined plan for the future development of the company’s projects. With its colossal scalability potential and the ability to construct decentralized applications, the blockchain is a robust technology that satisfies future demands in many fields.

ADALend heats the DeFi Space

ADALend chose Cardano as the primary blockchain that will power the DeFi system, unlike Ethereum based AAVEbecause Cardano is significantly less expensive to send, receive, and initiate contracts. In 2023, the price of Ethereum gas surged, causing dissatisfied users to realize that fees were a serious concern for everyone who used the AAVE protocol at the time.

It has been reported that the average transaction cost in 2023 and 2023 went as high as 80 USD in some circumstances (BitInfoCharts). Cardano fees remain low compared to other cryptocurrencies, primarily due to the dual-layer design of the network, which isolates calculations from settlements.

Because it still employs a Proof-of-Work (PoW) blockchain, the Ethereum network is still inefficient compared to the Cardano blockchain, which uses a Proof-of-Stake (PoS) system, which follows the same fees principles as the Ethereum network. Compared to the Ethereum blockchain, the Cardano blockchain enables the processing of a significantly greater number of transactions. The Cardano blockchain operates at a considerably faster rate. To make auditing as simple as possible, the Cardano codebase is being created in Haskell, a widely-used programming language chosen explicitly for this purpose.

A particularly specialized programming language, Solidity, was created by Ethereum developers and is only written by a small number of programmers, let alone subjected to rigorous peer review. The greater the number of engineers who can examine and audit code, the more safe and impenetrable the system will appear to be. To put it another way, the Cardano developers want the blockchain to be as free of code flaws as possible to prevent future security risks from occurring.

ADALend will leverage the oracles Chainlink and Ergo to provide a more secure and efficient experience for clients. Using Ergo’s oracle pools is more efficient and configurable than Chainlink’s oracle architecture, which relies on many single oracle data sources. AAVE solely makes use of Chainlink oracles.

Cardano makes use of the Ouroboros consensus algorithm, which is a Proof-of-Stake consensus system. Due to the ability of ADA holders to delegate their assets to secure the network, this closed-loop approach maximizes the efficiency with which network resources are utilized. The outcome is a significantly less resource-intensive system than Ethereum, primarily powered by miners who consume a lot of energy to protect the network, consuming vast quantities of electricity in the process.

Chatgpt Stock Symbol: Investing In The Future Of Ai

See More: How To Use ChatGPT Prompts For Writing Cover Letter?

Investing in Microsoft (NASDAQ: MSFT) is one of the indirect ways to gain exposure to ChatGPT. Microsoft has a strategic partnership with OpenAI, the organization behind ChatGPT. Microsoft has taken a massive position in OpenAI, which makes it a compelling investment option for those interested in the potential of ChatGPT. As one of the leading technology companies globally, Microsoft’s involvement with OpenAI highlights its commitment to AI research and development.

Perion Network (NASDAQ: PERI) is another company that could benefit from the ChatGPT rollout. Perion Network has a strategic partnership with Microsoft’s Bing search engine, and Microsoft is planning to launch a new version of Bing powered by ChatGPT. This collaboration positions Perion Network as a potential winner in the AI space, as ChatGPT’s language capabilities can enhance the user experience and effectiveness of Bing’s search results.

Investors looking to capitalize on the AI boom have several other options to consider. Here are some of the top AI-powered companies that have established themselves as leaders in the industry:

Google (Alphabet): Google, a subsidiary of Alphabet Inc. (NASDAQ: GOOGL), is at the forefront of AI innovation. From its search engine algorithms to autonomous vehicles and machine learning applications, Google has made significant strides in harnessing the power of AI.

Microsoft: We’ve already discussed Microsoft’s involvement with OpenAI and its commitment to AI research and development. Microsoft’s Azure cloud platform also offers various AI services, making it a well-rounded investment option.

Nvidia: Nvidia (NASDAQ: NVDA) is a leading provider of graphics processing units (GPUs) used in AI training and inference. Its GPUs have become essential components in data centers and AI systems, positioning Nvidia as a key player in the AI hardware market.

Amazon: As one of the largest e-commerce and cloud computing companies in the world, Amazon (NASDAQ: AMZN) utilizes AI extensively to improve customer experience, optimize logistics, and enhance its virtual assistant, Alexa.

Meta Platforms: Formerly known as Facebook, Meta Platforms (NASDAQ: FB) is investing heavily in AI technologies. The company’s AI initiatives include facial recognition, content filtering, and natural language processing, among others.

C3.ai: chúng tôi (NYSE: AI) is a leading enterprise AI software provider. The company offers a range of AI solutions, including predictive maintenance, fraud detection, and customer engagement, catering to various industries such as energy, healthcare, and manufacturing.

Accenture: Accenture (NYSE: ACN) is a global professional services company that has embraced AI as a core part of its offerings. The company leverages AI to enhance its consulting, technology, and outsourcing services.

Epam Systems: Epam Systems (NYSE: EPAM) is a leading global provider of digital platform engineering and software development services. The company utilizes AI technologies to deliver innovative solutions across industries such as finance, healthcare, and retail.

Adobe: Adobe (NASDAQ: ADBE) is known for its creative software suite, but it has also made significant investments in AI. Adobe Sensei, its AI and machine learning framework, powers features in its products and helps businesses make data-driven decisions.

Baidu: Baidu (NASDAQ: BIDU) is often referred to as the “Google of China” and is a major player in the AI industry. The company focuses on AI research, autonomous vehicles, and voice recognition technology.

Also Check: How to Use Adobe Podcast AI: A Comprehensive Guide

While investing in AI presents promising opportunities, it is essential to consider the associated risks. Here are some factors to keep in mind:

Regulatory Challenges: As AI technology becomes more prevalent, there may be increased scrutiny and regulatory challenges. Changes in regulations or public sentiment towards AI could impact the growth prospects of AI companies.

Competitive Landscape: The AI industry is highly competitive, with many companies vying for market share. The success of an AI company may depend on its ability to differentiate itself and stay ahead of the competition.

Ethical Considerations: AI technology raises ethical concerns related to privacy, data security, and algorithmic biases. Negative publicity or legal issues surrounding these concerns can have a significant impact on AI companies.

Technological Risks: AI is a complex field, and breakthroughs in new technologies could render existing AI solutions obsolete. Investing in AI requires understanding the technical landscape and the potential risks associated with evolving technologies.

ChatGPT is not publicly traded, and therefore, there is no stock symbol for it.

One way to gain exposure to ChatGPT is by investing in Microsoft (NASDAQ: MSFT), as Microsoft has a partnership with OpenAI and a significant position in the company.

Perion Network (NASDAQ: PERI) has a strategic partnership with Microsoft’s Bing search engine, and Microsoft is rolling out a new version of Bing powered by ChatGPT. This collaboration could benefit Perion Network by improving the effectiveness of Bing’s search results.

Some of the top AI-powered companies to consider include Google (Alphabet), Nvidia, Amazon, Meta Platforms, and chúng tôi among others.

Investing in AI comes with risks such as market volatility, regulatory challenges, competition, ethical considerations, and technological risks. It’s important to assess these factors before making investment decisions.

Conduct thorough due diligence, stay updated with industry trends, diversify your investment portfolio, and consider consulting with financial professionals to mitigate the risks associated with investing in AI.

Investing in AI offers exciting opportunities for investors looking to capitalize on the transformative power of artificial intelligence. While there is no stock symbol for ChatGPT, there are indirect ways to gain exposure to AI technology. Companies like Microsoft and Perion Network have strategic partnerships and collaborations that can provide investors with indirect exposure to ChatGPT.

Moreover, there are several other AI-powered companies such as Google (Alphabet), Nvidia, Amazon, Meta Platforms, and chúng tôi that investors can consider for their AI-focused investment strategies. These companies are at the forefront of the AI industry, driving innovation and shaping the future.

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Is Square The Future Of Mobile Payments?

Last week it was reported that Square, a mobile credit card reader, had opened its doors and was available for download in the app store. Square is the brainchild of Jack Dorsey, who is also co-founder of Twitter.

The app, when used in conjunction with a small card reader that plugs into the auxiliary port, allows anyone to process credit card payments. This takes “mobile payments” to a whole new level as now small businesses and vendors can process payments without the need for a wired or complex point-of-sale system.

All you need is a compatible device (iPhone, iPad, iPod touch, or one of select Android devices), the card reader, and a signal on your device.

So what does this mean for retailers and small businesses? Is it secure to use? And what about the cost? Will this be the new method businesses large and small use? Read more to find out…

Cost

Using Square is not terribly expensive. The mobile card reader is free when you are approved for a Square account, and the transaction fee is 2.75% + .15 to swipe. It’s slightly more if keyed in. There’s no start-up fee, monthly fee, minimum fee, early-cancellation fee, or any other bizarre and ridiculous fee. Transaction fee. That’s it!

Security

When watching the demo video, the part I was most impressed with was the finger-based signature. Merchants can allay customer fears or hesitation by allowing them to hold the device, swipe it themselves, and then sign onscreen with their finger. They’ll see the transaction is complete, their information is secure (only the last 4 digits of the card will show), and they don’t need to worry. Receipts are sent immediately to their email.

Is it unreasonable to expect a jailbreak app designed to clone or retain the swiped info? Maybe not, but do thieves really want to go through the hassle of creating some kind of “business” with items or services to sell so they can dupe people into swiping their card on a phone? I run a small business, and honestly it sounds like a lot less work to learn how to pickpocket.

Convenience

Obviously, this is Square’s strongest selling point. This is a truly wireless and simple solution to credit card processing. Further, it doesn’t just make accepting credit cards easier, in some cases it makes it possible when it wasn’t before.

Think of festivals and street fairs, places where cash-only is the norm. They can now turn a bigger profit by snagging those customers that don’t carry cash or forgot to stop by the ATM (or maybe are too cheap to pay that $3 withdraw fee!)

But it’s not just small businesses and vendors that could benefit, I imagine larger companies can, too. Apple stores are a great example of mobile payment, with their own card reader and device to process payments on the spot. Now other retailers can trial out this system using Square.

It may not happen in your local department stores, but perhaps seasonal retailers that set-up shop temporarily or sell door-to-door can make use of Square’s simplicity. Maybe in the future, Square will grow to include a barcode-scanning system and inventory count for retailers.

The Downsides

Square is still an app, and apps still crash or have bugs. Already Square’s pushed out an update to resolve some issues. And it might be discouraging to think of lost revenue or customers because AT&T’s network is having a grumpy day or your business is in a weak reception area.

And of course, phones are lost every day, which could compromise security. And then there’s the fact that Square is only as good as your device’s battery. Better keep that cord handy and make sure an outlet’s nearby.

But most of these downsides can be avoided or remedied easily. Find a bug? Let Square know. Bad reception? Invest in a Microcell. Lost your phone? Good thing you had a passcode that was set to erase the data after 10 failed attempts. (You did think to do that, right?) Didn’t charge your battery? Well then you shouldn’t be running a business because you don’t know how to plan! (I kid, I kid.)

Is This the Future?

Mobile payment processing is no doubt catching on and building buzz. Paypal has their options, and I think the field is bound to get more crowded. Crowded means competition, which is usually a good thing.

I own a small business that sells clothing at local festivals, and I have used the bank’s merchant payment processing system. It’s a cumbersome and expensive tool, and the cost hasn’t really been worth the benefit of being able to accept credit cards. Square is a greatly welcome alternative. I can’t wait to try it out.

Top Female Ai Influencers In 2023 Who Rocked The Data Science World!

Overview

Have a look at the list of Top 11 Female AI influencers of 2023.

We have attached their respective social media handles so that you can better understand their work

Introduction

With the volume and panoply of data being generated any second the demand for data scientists has shot through the roof. Organizations now need to make better and faster decisions to make an early move in the market and progress in the direction of their vision.

And with the passage of time people have realized that to make better decisions the community has to be inclusive of people from different backgrounds to capture their respective perspectives. The rising awareness of this fact is pulling more and more women into the data science industry thus decreasing the gender gap in the traditionally male-dominated STEM industry.

Kate Crawford is a principal researcher at Microsoft research. She is a leading researcher and professor who has spent the last decade studying the social implications of data systems, machine learning, and artificial intelligence. She is a Senior Principal Researcher at MSR-NYC. Along with this, she is also the inaugural Visiting Chair for AI and Justice at the École Normale Supérieure in Paris, and the Miegunyah Distinguished Visiting Fellow at the University of Melbourne.

Kate is the co-founder of the AI Now Institute at New York University, the world’s first university institute dedicated to researching the social implications of artificial intelligence and related technologies.

Fei Fei Li is an American computer science professor at Stanford University. She is the Co-Director of the Stanford Institute for Human-Centered Artificial Intelligence, and a Co-Director of the Stanford Vision and Learning Lab.

She also co-Founded AI4All, a nonprofit organization working to increase diversity and inclusion in the field of artificial intelligence. Her research expertise includes artificial intelligence (AI), machine learning, deep learning, computer vision, and cognitive neuroscience. She was the leading scientist and principal investigator of ImageNet.

In May 2023, Li joined the board of directors of Twitter as an independent director.

Dr.Geetha Manjunath is the Founder, CEO, and CTO of NIRAMAI, a deep tech startup developing a novel solution for detecting early-stage breast cancer. She has over 25 years of experience in IT research and has led to many innovative projects in Healthcare and Transportation.

She was a Lab Director heading Data Analytics Research in Xerox India. Before that, she was a Principal Research Scientist at Hewlett Packard Laboratories for 17 years, and a member of the C-DAC team which built the first commercial supercomputer in India.

Dr. Manjunath holds a Ph.D. in Computer Science from the Indian Institute of Science and management education from Kellogg’s School of Management, Chicago

She was awarded the Gold Medal by the Computer Society of India, is a recipient of the Karnataka State Award, was named as one of the top 50 NASSCOM IT Innovators and was also the winner of the 2010 MIT Tech Review Grand Challenges. She is a Senior Member of IEEE and past Chair of IEEE Computer Society, Bangalore Chapter. Also, she is the inventor of 15 US patents with more pending grants.

She was named by Forbes and AI Summit as 2023‘s “AI Innovator of the Year”.

Allie has spoken about AI and field diversity around the world, addressed the European Commission, drafted foreign AI strategies, and created eight guidebooks to educate businesses on how to build successful AI projects.

Prof. Jennifer Chayes is the Associate Provost of Data Science and Information, and Dean of the School of Information, at UC Berkeley. Previously, she was at Microsoft for almost 23 years, where she was Technical Fellow, and founder and managing director of three interdisciplinary labs: Microsoft Research New England, New York City, and Montreal.

She is a member of the American Academy of Arts and Sciences and the National Academy of Sciences. Chayes is the author of about 150 academic papers and inventor of over 30 patents. Her research areas include phase transitions in computing, structural and dynamical properties of networks, graph theory, graph algorithms, and computational biology.

Chayes is one of the inventors of the field of graphons, which are widely used in the machine learning of large-scale networks.

Another leading name on our list is Kim Hazelwood. She is the West Coast head of Engineering for Facebook AI Research as well as the Technical Lead for Facebook Systems and Machine Learning (SysML) Research. Her expertise lies in scalable computer systems and applied machine learning.

Kim holds a Ph.D. in computer science from Harvard University and has authored over 50 publications and one book. She is a recipient of the MIT “Top 35 Innovators under 35″​ award and an NSF Career Award. She currently serves on the Board of Directors for the Computing Research Association. 

She holds an M.S. in Operations Research from Stanford University.

Ujjyaini is Currently working as the Chief Data Officer at Zee5.  She has almost 13 years of rich Leadership experience in establishing Data as a Culture at large organizations. Previously, She has worked with organizations like  Airtel, Flipkart Mckinsey & company in leading positions.

Due to her love for mathematics, Ujjyaini chose to study Mathematics and Theoretical Computer Science at Chennai Mathematical Institute and then pursued Quantitative Economics at her Masters from ISI Kolkata.

She is in the Top 50 Most Influential Women 2023 in Media, Marketing and Advertising by Impact.

Yael has a Ph.D. in Biomedical Informatics from Stanford University School of Medicine. Her research was focused on information extraction and semantic understanding. To predict and understand how human genetic variations impact drug response using natural language processing of scientific text.

Another leading lady in the data science domain is Anima Anandkumar. She is the director of machine learning research at NVIDIA and a Bren professor at Caltech CMS department. Before this, she was a principal scientist at Amazon Web Service, where she has enabled machine learning on the cloud infrastructure.

She has spearheaded research in unsupervised AI, deep learning, optimization, and tensor methods. Anima, a graduate from IIT Madras has received her Ph.D. from Cornell University. She has been featured in documentaries by PBS, KPCC, wired magazine, and in articles by MIT Technology Review, Forbes, Yourstory, O’Reilly media, and more.

Anima has worked to democratize AI, promote ethical use, and improve diversity and inclusion in AI. She was awarded the good tech award 2023 by NYTimes for her efforts.

Rachel Thomas is a deep learning researcher and the co-founder of chúng tôi She is also the founding Director of the Center for Applied Data Ethics at the University of San Francisco which aims to address harms such as disinformation, surveillance, algorithmic bias, and other misuses of data. Rachel earned her math Ph.D. at Duke and was an early engineer at Uber.

She was featured in Forbes magazine as one of the 20 most incredible women in AI. Rachel is a popular writer and keynote speaker, on topics of data ethics, AI accessibility, and bias in machine learning

EndNote

To the women who have gone through this article, I hope this list becomes a beacon of inspiration for you to tackle all the obstacles and ignites the aspiration within to feature in such a list in your future.

In this article, we tried to cover some eminent female AI influencers that shaped 2023. I will personally recommend you to follow these women influencers on social media to learn and get inspired from them.

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