Microsoft has announced that former AWS India President Puneet Chandok has been named Corporate Vice President of Microsoft India and South Asia. The announcement comes at a time when the two major international providers of cloud services AWS and Microsoft are increasingly competing for market share in India.
Chandok would take over the operational responsibilities from outgoing president of India Anant Maheshwari on September 1, 2023, who resigned last month.
According to the company, Chandok would be in charge of integrating Microsoft’s operations in South Asia, including Bangladesh, Bhutan, the Maldives, Nepal, and Sri Lanka. Additionally, he will be in charge of the company’s focus on important industries using a customer-centric strategy with generative AI at its foundation.
Ahmed Mazhari, President Microsoft Asia said, “Puneet has a proven track record of starting, expanding, and using technology-based enterprises to create influence and change. Puneet’s leadership will be crucial in ensuring Microsoft’s continued success in South Asia as we embrace an AI-driven future. I also want to thank Anant Maheshwari for setting us on a growth path.”
Chandok oversaw the business operations for AWS in South Asia and India in his prior position there. He collaborated closely with large corporations, startups, small and medium-sized businesses (SMBs), and digital businesses to help them innovate, decrease technological debt, and add agility. A partner at McKinsey for more than ten years in India and Asia, he has also held senior regional and global positions at IBM.
Chandok has a bachelor’s degree in commerce, an MBA from the Indian Institute of Management in Calcutta, as well as diplomas in high-level computer systems, networking, and computer programming.
A six-week accelerator programme called ML Elevate 2023 has been announced by venture capital firm Accel and Amazon Web Services (AWS) to support Indian businesses creating generative AI solutions.
By giving them access to useful AI models and tools, commercial and technical mentoring, curated resources, the AWS Activate programme, and up to USD 200,000 in AWS Credits, ML Elevate aims to promote generative AI businesses.
Other advantages include the chance to scale production-ready generative AI apps on Amazon SageMaker JumpStart and peer support from a network of top AI and ML startup founders.
Startups in generative AI that have created a Minimum Viable Product (MVP) and plan to apply for funding in the ensuing 12 to 18 months are eligible to apply. Selected companies will participate in live virtual masterclasses, which will include fireside chats and panel discussions with investors, business titans, and AWS specialists.
Poonacha Kongetira, SVP Engineering at SambaNova, Anupam Datta, co-founder, President, and Chief Scientist at TruEra, Tom Mason, Chief Technology Officer at Stability AI, Vishal Dhupar, Managing Director, Asia-South of NVIDIA Graphics, Apurva Kalia, Senior Researcher at Tufts University, and others will be on the panel of speakers.
Through a dedicated Demo Week, the cohort will also have the chance to pitch to top VC funds, angel investors, and business titans in order to raise money.
“Our goal with ML Elevate is to help generative AI startups create solutions tailored to specific industries and invent to advance the digital economy,” Vaishali Kasture, Director of AWS India and South Asia at Amazon Web Services India.
Rakesh Krishnan, a security researcher for Netenrich, recently reported in a blog that he had discovered evidence of a model known as FraudGPT.
Under the nickname CanadianKingpin, the culprit of this programme asserts that FraudGPT may be used to generate malicious code, produce undetectable malware, locate leaks, and pinpoint vulnerabilities. The undetectable malware can circumvent conventional security measures and make it challenging for antivirus software to find and eliminate threats.
Identification of Non-Verified by Visa (Non-VBV) cards, which enables hackers to execute unauthorized transactions without additional security checks, is another feature of FraudGPT. Additionally, the programme has the ability to automatically create convincing phishing pages that look like authentic websites, which raises the success rate of phishing attempts.
FraudGPT may design various hacking tools that are suited to particular targets or exploits in addition to creating phishing pages. Furthermore, it can search the internet for secret hacker organizations, illicit websites, and markets where stolen data is sold.
Additionally, the application can create phishing emails to trick people into falling for scams. FraudGPT can also produce content to teach coding and hacking methods, giving cybercriminals resources to advance their skills. Finally, it helps in identifying cardable sites where credit card information can be stolen and used for unauthorized purchases.
Since July 22, 2023, FraudGPT has been making the rounds in darknet forums and Telegram channels. It may be obtained through membership for $200 per month, $1,000 for six months, or $1,700 for a year. Although the large language model upon which this model is based is unknown, the author asserts that it has amassed more than 3,000 verified sales and reviews.
The Digital Personal Data Protection bill has not been referred to any committee, according to Minister of State for Electronics and Information Technology Rajeev Chandrasekhar, and it can only be done if it is introduced in the Parliament.
Chandrasekhar made his remarks in response to a letter submitted by Rajya Sabha member John Brittas on July 28 asking the speaker of the Lok Sabha and the head of the Rajya Sabha to prevent the introduction of a report from the Parliamentary Standing Committee on Communications and IT.
➡️This is misinformation and completely wrong.
➡️No bill including the proposed DPDP (Digital Personal Data Protection Bill) can be referred to any committee unless it is done so by Parliament
➡️In turn, the bill can be only referred to committee AFTER the Cabinet-approved… https://t.co/YJ7PnE9FsW
Curiously, Chandrasekhar also charged Brittas, a fellow committee member, with spreading false information about the bill that was, according to Birittas, “adopted” by the committee. A few days ago, Brittas and other opposition lawmakers from the IT committee left the meeting when the committee, presided over by Shiv Sena MP Jadhav Prataprao Ganpatrao, adopted a report backing the bill.
The members who opposed the report said that they were not provided with the updated version of the DPDP law, which had been approved by the Union Cabinet, and as a result were not aware of the report’s creation. The committee demanded that the DPDP Bill be passed into law right now, in the report.
According to a tweet from Chandrasekhar, no bill including the proposed DPDP can be referred to any committee unless it is done by Parliament. In turn, the bill can be only referred to committee only after the Cabinet-approved bill is introduced in Parliament. He added that DPDP has not been introduced into Parliament and so question of considering it in committee does not arise.
However, earlier, Ashwini Vaishnaw, the minister of electronics and information technology, courted controversy when he said, in a specific day, that the IT Committee had “approved” the bill. The minister’s assertions have been deemed untrue by committee members like Karti Chidambaram, Jawhar Sircar, and Brittas.
According to executive chair Michael Miller, News Corp Australia is utilizing generative AI to produce 3,000 articles every week. According to a story in Mediaweek, Miller stated at the World News Media Congress in Taipei that a team of four employees uses the technology to produce thousands of local reports each week on the weather, fuel costs, and traffic conditions.
Peter Judd, the data journalism editor for News Corp, oversees the Data Local section, and he is credited in many of the stories. The unit augments the content that journalists write for the 75 hyperlocal mastheads owned by the corporations around the nation, including those in Penrith, Lismore, Fairfield, Bundaberg, and Cairns.
According to a News Corp representative, stories like “Where to find the cheapest fuel in Penrith” are produced by AI under the supervision of journalists. The fact that the reports are generated using AI is not disclosed on the page.
The spokeswoman claimed it would be more accurate to describe the 3,000 articles as giving service information and that Miller had in fact made the remarks at a conference last month.
For instance, the automated updating of local fuel prices, daily court lists, traffic and weather reports, and death and burial notices has been done for some time using artificial intelligence, according to the spokeswoman.
“I’d like to emphasize that the Data Local team’s working journalists oversee all such information and judgements,” she said. According to a recent job posting by News Corp for a data journalist, creating automated content to build a proposition and pipeline for revenue is one of the responsibilities.
In Australia, the majority of newsrooms are investigating AI’s potential. In a statement to Guardian Australia, the ABC said that it was concentrating on artificial intelligence applications that may improve content accessibility. This includes transcription of audio content, text-to-speech delivery of articles using AI voice and translation, as well as recommendations and personalization.
Leading AI expert Andrew Ng is back with another free short course called Building Generative AI Applications with Gradio, in association with Hugging Face. One will learn how to develop and demo machine learning applications fast in this course. Apolinário Passos, an ML Art Engineer at Hugging Face, will teach the course, which is meant for beginners.
Participants will experiment with a variety of Gradio-based activities during the course, such as text summarization, image captioning, and image production. With the help of the open-source Gradio Python library, users can quickly create customizable user interface elements for their machine learning model or any API and create user-friendly applications that can be used by people who are not programmers.
With a few lines of code, students will be able to develop a user-friendly app to take input text, summarize it with an open-source LLM, and display the summary. They will also create an app that lets the user upload an image, which uses image captioning to describe the image uploaded, and display both the image and its caption in the app.
Students will create an app that takes text and generates an image with a diffusion model, then displays the generated image within the app.
They will also create an interface to chat with an open source large language model using Falcon, the best-ranking open source LLM, according to the Open LLM Leaderboard.
After completing the one-hour training, you will have acquired useful skills for creating interactive apps and demos that will speed up project implementation and validation. Anyone who has basic Python knowledge and wants to learn to build and share apps and demos using Gradio, is eligible for the course.
A portfolio of generative AI services, ranging from strategy definition to the practical development and execution of generative AI at scale, is being introduced by the French IT consulting firm Capgemini. The Group announced that during a three-year period, it will invest 2 billion euros (INR 18 million) in AI.
Franck Greverie, Chief Portfolio Officer, Global Business Lines leader, and Group Executive Board Member at Capgemini, said, “Generative AI is already becoming a key pillar of digital transformation for businesses. We see a breadth of opportunities to unlock substantial business value for our clients, which go far beyond important productivity gains.”
The Paris-based company said in the blog that it had completed numerous generative AI projects in recent years, particularly in the life sciences, consumer goods and retail, and financial services industries.
According to the recent portfolio, CXOs will build the proper groundwork in terms of people, process, and technology by using a generative AI strategy that prioritizes the pertinent use cases for their company. Four generative AI assistants for a highly personalized customer experience are part of generative AI for customer experience.
Moreover, improved software engineering with generative AI will result in a longer, more secure software lifecycle. According to the portfolio, Custom generative AI for business is a platform that allows pre-trained open large foundation models (LFMs) to be customized to each customer’s needs using business proprietary data.
Microsoft and Google Cloud already have generative AI collaborations with The Capgemini Group. By incorporating AI training into every aspect of its training programme, the company will also train a significant portion of its staff in generative AI.
The most recent statements were made on the same day that the company reported greater half-year revenue, driven by cloud, data, and AI operations. The Group is currently working with Heathrow Airport to improve customer service by developing solutions using generative AI.
In the ever-evolving landscape of technology, artificial intelligence (AI) has emerged as a driving force of innovation, revolutionizing various industries. From healthcare to finance, AI has left an indelible mark on numerous sectors. In recent years, the realm of sports has also experienced a profound shift due to AI technologies. Even some of the reviews on the bookies websites, available through this source, can be written using the stats, gathered by that tech. This article explores how AI is revolutionizing and transforming sports forever, with a focus on athlete training and performance optimization, enhanced fan experiences, referee assistance and fair play, data analytics and performance analysis, and injury prevention and athlete health.
Athlete Training and Performance Optimization
AI is radically transforming athlete training and performance optimization. With its ability to process and analyze vast amounts of data, AI algorithms are revolutionizing training programs, helping athletes reach their full potential. By incorporating biometrics, motion tracking, and historical performance records, AI systems provide valuable insights into an athlete’s strengths and weaknesses. This information allows trainers and coaches to tailor personalized workouts and training regimes that target specific areas for improvement. AI can also simulate game scenarios, providing athletes with invaluable opportunities to refine their skills and decision-making abilities. By creating virtual environments, AI allows athletes to practice and strategize, preparing them for the challenges they may face during actual competition.
Enhanced Fan Experiences
AI is not only transforming the performance of athletes but also enhancing the experiences of sports fans. Augmented reality (AR) and virtual reality (VR) technologies are at the forefront of this revolution, allowing fans to immerse themselves in the action as if they were part of the game. Through AR and VR, fans can witness sports events from unique perspectives, accessing exclusive content and experiencing the thrill of being on the field or court. For example, VR headsets can transport fans into the stadium, allowing them to virtually sit in the best seats and enjoy the game as if they were physically present. Additionally, AI-powered chatbots have become an integral part of fan engagement, providing instant responses to inquiries, updates on scores, and personalized recommendations. Social media platforms are also utilizing AI algorithms to deliver tailored content and updates based on fans’ preferences, allowing for a more interactive and engaging experience.
Referee Assistance and Fair Play
AI is playing a significant role in ensuring fair play by providing valuable assistance to referees. The Video Assistant Referee (VAR) system is a prime example of how AI can be utilized in sports officiating. VAR leverages AI algorithms to analyze video footage, helping referees make accurate decisions in crucial moments of the game. Whether it’s determining offside calls, identifying fouls, or resolving goal-line controversies, AI can process data in real-time, reducing human error and ensuring fairness in the game. VAR has been implemented in various sports, including football (soccer) and rugby, with the aim of minimizing controversial calls and maintaining the integrity of the game. However, it’s important to strike a balance between AI assistance and maintaining the human judgment required for nuanced decision-making.
Data Analytics and Performance Analysis
AI-driven data analytics is revolutionizing the way sports teams analyze performance and strategize. By harnessing the power of AI, teams can process massive amounts of data and extract valuable insights that human analysts might overlook. From player performance analysis to opponent scouting, AI enables teams to make data-driven decisions, optimize game strategies, and gain a competitive edge. Computer vision systems powered by AI can track player movements, detect patterns, and provide real-time feedback during training sessions and matches. This level of analysis not only benefits teams but also provides fans with a deeper understanding of the game through statistical insights and advanced visualizations. For example, AI algorithms can generate real-time statistics, highlight key moments in the game, and provide predictive analysis, allowing fans to gain a deeper appreciation for the intricacies of the sport.
Injury Prevention and Athlete Health
The field of athlete health and injury prevention has also witnessed significant advancements with the help of AI. Sensor-equipped wearables and AI algorithms are used to monitor athletes’ vital signs, biomechanics, and fatigue levels in real-time. This data is then processed and analyzed, providing athletes and coaches with valuable insights into their physical condition and performance. Machine learning algorithms can identify injury patterns and risk factors, allowing teams to implement preventive measures and develop personalized training programs to minimize the occurrence of injuries. For example, AI can help detect early signs of overexertion or fatigue and recommend appropriate rest periods or adjustments to training intensity. AI-powered rehabilitation systems further optimize the recovery process, assisting athletes in returning to peak physical condition. By leveraging AI in injury prevention and athlete health, teams can enhance the longevity and performance of their athletes, ultimately leading to better overall team performance.
Conclusion
Artificial intelligence is revolutionizing and transforming the world of sports, affecting athletes, fans, and the entire sports ecosystem. Through AI, athlete training and performance optimization have reached new heights, with the ability to simulate game scenarios and refine skills. Enhanced fan experiences are made possible through the immersive technologies of AR and VR, chatbots for interaction, and personalized content through social media platforms. AI-assisted referee systems ensure fair play, aiding in critical decisions through VAR and reducing human error. Data analytics and performance analysis driven by AI provide teams with valuable insights, while injury prevention and athlete health benefit from AI’s ability to monitor and personalize training programs. As AI continues to evolve, its integration into sports promises an exciting and transformative future for athletes, fans, and the entire sports industry. The intersection of AI and sports opens up new possibilities, pushing boundaries and creating a more engaging, fair, and optimized sports experience for all.
When it comes to data analytics, navigating complex information is a crucial skill. It is where Data Jujitsu comes into play. But what exactly it is, and how can it help you master analytics? Find these answers in our article!
Understanding Data Jujitsu
Data Jujitsu is a concept introduced by DJ Patil, the previous U.S. Chief Data Scientist. It is a strategic approach to deriving meaningful insights from intricate information. The philosophy behind it is to achieve the greatest outcomes with the least amount of effort. It involves breaking down complex information challenges into manageable parts and applying innovative analysis techniques to explore them.
Here are a few core principles on which this philosophy rests:
Flow with It: This principle encourages one to be flexible and adapt their approach according to the data they encounter. Just like a martial artist flows with the movements of their opponent, successful analysts should be able to adjust their techniques based on the characteristics and quality of the data they are working with.
Simplicity and Elegance: Strive to find the most straightforward and efficient ways to extract valuable insights from the input. Avoid overcomplicating analysis with unnecessary steps, and focus on delivering actionable results without sacrificing accuracy.
Focus on Impact: Concentrate on the most critical business or research questions and focus your efforts on finding meaningful patterns. Avoid getting lost in the noise of information and use the latest data analytic solutions to derive insights that matter.
The Value of Jujitsu
Its real value is simplifying and streamlining the analysis process. A recent survey by Anaconda revealed that data manipulation, a key component of Jujitsu, represents about 65% of the total time spent by ML/data scientists.
With the exponential growth of information scope, this technique is more pronounced than ever. It’s not just about managing the volume of information but transforming it into a strategic asset. As DJ Patil puts it in his same-name book, the technique is about building data products and turning information into actionable, valuable insights.
10 Techniques for Mastering Complex Analytics
As we delve deeper into the science of Jujitsu, we encounter ten techniques that form the backbone of this approach. Let’s explore each of them in more detail.
#1: Cleaning
It is the process of identifying and correcting errors in datasets. It’s scrutinous work of spotting the inconsistencies, missing values, and outliers that can skew your analysis.
Example: A data professional might use algorithms to fill in missing information or remove duplicate entries, ensuring integrity. According to IBM, poor information quality costs the U.S. economy around $3.1 trillion a year, highlighting the importance of this step.
#2: Integration
It involves combining info from different sources into a unified view. It’s like assembling a jigsaw puzzle, where each piece represents another information source. This technique is crucial when information comes from various sources like CRM systems, social media, and sales records.
Example: A case study from Cisco revealed that integrating info from different departments led to a 360-degree customer view and improved customer service.
#3: Transformation
It is converting information from one format or structure into another. It’s akin to translating a foreign language into your native tongue.
Example: You might normalize numerical info or encode categorical information to prepare it for a machine learning model. This step is crucial in making the data understandable for the algorithms.
#4: Visualization
Visualization is representing information in a graphical or pictorial format. Use tools like Tableau or PowerBI to create interactive dashboards that bring info to life.
Example: According to Aberdeen Group, organizations that use visual info discovery tools are 28% more likely to find timely information than those that don’t.
#5: Feature Engineering
Feature engineering involves creating new features or modifying existing ones to improve machine learning model performance.
Example: A professional might create a new feature that combines age and income to predict a customer’s purchasing power. A famous example is the Netflix Prize competition, where the winning team used feature engineering to improve their algorithm’s performance and win the $1 million prize.
#6: Dimensionality Reduction
Dimensionality reduction is about reducing the number of random variables under consideration. Its goal is to retain the most important patterns and relationships, making it easier to perform the analysis.
Example: Techniques like Principal Component Analysis (PCA) can help simplify high-dimensional information without losing important insights. This technique is used in fields like genomics, where datasets can have thousands of dimensions.
#7: Anomaly Detection
Anomaly detection involves identifying outliers in info that deviate from the norm. Machine learning algorithms can help detect anomalies in large datasets, which can be crucial in fraud detection or network security.
Example: Credit card companies use anomaly detection to identify fraudulent transactions and prevent losses.
#8: Predictive Analytics
Predictive analytics uses historical information to predict future events. Machine learning models, such as regression or time series analysis, can forecast sales, customer churn, or market trends.
Example: Companies like Amazon use predictive analytics to recommend products, contributing to 35% of their total sales.
#9: Real-time Analytics
Real-time analytics involves analyzing information as it’s generated in real time. This technique is crucial in social media monitoring, stock market trading, or emergency response, where timely insights can make a big difference.
Example: Twitter uses real-time analytics to tailor content to user preferences, improving user engagement.
#10: Machine Learning
Machine learning is training a model to make predictions or informed decisions. Machine learning algorithms can uncover patterns and relationships in complex information, providing valuable insights that would be difficult to discover manually.
Example: According to a report by McKinsey, machine learning could generate up to $6 trillion value annually in marketing and sales alone.
Conclusion
Mastering the techniques described in this article can help you easily navigate the complex world of data analytics. Remember, Data Jujitsu is, first and foremost, about using the proper technique at the right time. As information grows in volume and complexity, the importance of these skills will only increase.
When you need to design a logo for your company, there are many tools that you can use to create one. The newest technology that everyone is talking about is artificial intelligence. The use of artificial intelligence in different fields has brought a lot of success to companies by increasing the productivity of the workforce. The use of AI is diversifying and becoming very popular in different areas of the industrial workflow. AI is also used in marketing and branding activities. In fact, the use of AI is also done for the design and creation of business logos.
There are many AI powered logo makers online where you can get great, meaningful logo designs to represent your business. What a graphic designer can do with traditional tools, he can do better with the help of AI Tools. The idea of using AI in industrial operations, in any field, whether marketing or sales, is not to replace human skill but to enhance capabilities with the right assistance.
AI or do it yourself?
The do-it-yourself mode of creating a logo design is time-consuming and can take hard work from the graphic designer. Moreover, the graphic designer has to take a trial-and-error approach if he is doing it on his own. On the other hand, if the designer is taking the help of AI-powered tools, the finished design is better and without fewer errors. The designer can then modify it again, but the time and effort put into the process is lesser than before.
Usually, a logo design takes somewhere from 3 to 4 weeks to complete without errors. If your business wants a perfect logo design that is even better than the brief, then you have to wait for at least a month. In that one month, the logo design goes through multiple revision cycles and changes. This happens when you entirely depend on manual skills and the more conventional tools.
The use of AI can expedite the logo design process to such an extent that the whole process is shortened to 2 to 5 minutes. If a graphic designer is really good at fine-tuning the logo design and working with AI logo maker tools, he will be able to create a great logo design in half a day or a day. Even if the client wants some major changes in the business logo, the whole repeat process would also take a day or so, and you have the output ready in a day.
Since we are looking at a boom in startup culture, there is a need to create new logos with originality and emphasis. Sometimes, startup companies need a logo at the earliest so that they can launch their marketing strategies. At other times, some startup companies do not wish to invest a lot in the logo design process. In both cases, the use of AI-powered logo makers and other tools is useful. AI can reduce turnaround time in the design while also reducing the cost of design.
Why do companies opt for AI designing tools?
If you are still thinking about how AI helps businesses in logo designing and making, here are some important points to consider-
It is fast
Using AI is a good decision because it reduces the time taken to design, create and finalize a logo design. AI does all the hard work on the basis of the prompts given to it, and based on the prompts that the graphic designer gives, it creates a complete logo design that looks really good and has a great impact on the customers. Artificial intelligence algorithms create digital art that looks as beautiful as the art created by humans.
More control
Often, people have this misconception that when we use AI, we are relinquishing control to the computer and its algorithm. The truth is when we are using AI, we are manipulating the algorithm like a tool, and according to our commands, it creates designer-quality output. How the AI algorithm responds and what type of logo design you get depends on what prompts you share with the computer.
We control the information fed into the computer and, in a way, also control the final output. Suppose you think in another way; often, what the designer imagines does not come across properly on the screen. The control we have over AI is greater as we can make smaller changes and larger transformations of logo design. You can not only create new logo designs for upcoming brands but also rebrand existing ones in a better way.
The smart way of creating logos
A logo design is important, but it is only one component of the brand that you wish to create. When designing the logo, adopting a smart method that does not take a lot of effort will help designers. In the age of AI, the smartest decision that a graphic designer can make is to learn about the new AI upgrades that existing editing tools and designing tools have. The more you update your designing toolkit, the smarter and better you work.
At a time when there is a huge demand for logo designing skills, the use of AI logo makers and features can help designers complete their projects within the deadline. The use of AI also gives you the freedom to work on multiple projects at the same time. By using Ai, you are using your own imagination and prompts, but you are letting the machine do the research for a good unique design.
It’s inexpensive
The use of AI has reduced the cost of logo generation. Earlier, the time taken by a graphic designer and the tools used for logo creation used to cost a lot. However, now that you get great logo design in one go on an AI-powered platform or app, the costs are reduced to a great extent. In fact, if you simply want a basic logo design generated by AI, there are many free logo makers that will give you good output.
Companies are using Ai for logos and other designing tasks by hiring graphic designers who are trained in the use of Ai and ML. They are not completely replacing human expertise but choosing to use better tools in the workforce.
