During the Salesforce AI Day today, the company announced the world’s largest AI event, Dreamforce. The first batch of Dreamforce 2023 speakers were introduced by Salesforce. Dreamforce 2023 will bring together customers, partners, leaders, and visionaries to discuss the future of trustworthy AI for organizations around the world, delving deeply into AI and how it is changing the world around us. Registration for the same is open.
Dreamforce 2023, the biggest AI conference in the world, will be held at Moscone Centre in San Francisco from September 12–14. For three full days, tens of thousands of guests in person and millions online on Salesforce+ will join forces to learn, interact, and give back.
At the Salesforce AI Day, the majority of senior business leaders said they will prioritize AI for their company over the next 18 months because it will enable them to use data more effectively, provide better customer service, and run more smoothly. However, in order for enterprise AI to be implemented in a trustworthy, responsible manner, a number of significant problems like ethics, security, and bias need to be resolved.
At the largest AI event, which will feature more than 70 AI researchers, innovators, ethical experts, and thought leaders, the best minds influencing the future of AI will be present. Some of the the top speakers include Sam Altman, CEO of OpenAI, Dario Amodei, Co-Founder and CEO of Anthropic, Aidan Gomez, CEO of Cohere, and, Dr. Margaret Mitchell, Chief Ethics Scientist at Hugging Face, and many more.
Along with other visionaries and futurists, including Dr. Jane Goodall, Matthew McConaughey, Rainn Wilson, San Francisco Mayor London Breed, and others, the speakers will discuss how they can consider business as the best platform for change in the era of artificial intelligence. A series of TIME100 Talks highlighting the fundamental part that female leadership plays in AI innovation will also be presented at Dreamforce. At the largest AI event in the world, TIME will honor leaders and innovators in the field.
The entire community will be able to connect to Dreamforce event at any time, from anywhere, thanks to Salesforce+. Exclusive to Salesforce+, more than 72 hours of live broadcast footage and 120+ new on-demand episodes showcasing the most significant developments and highlights from Dreamforce will be available.
Microsoft has announced that it is integrating Python, a well-known programming language, into Excel. Users of Excel now have the ability to manage and analyze data using Python, thanks to a feature that is now in public preview.
Stefan Kinnestrand, general manager of contemporary work at Microsoft, explains that users can manipulate and explore data in Excel using Python plots and libraries, and then use Excel’s formulas, charts, and pivot tables to further refine their insights. Now that Python is readily accessible from the Excel ribbon, complex data analysis may be done in the comfortable Excel environment.
Python integration in Excel will be a part of Excel’s built-in connectors and Power Query, so one won’t need to install any additional software or set up an add-on to use the features. Additionally, Microsoft is introducing a new PY function that makes it possible to display Python data inside the grid of an Excel spreadsheet.
Popular Python libraries like pandas, statsmodels, Matplotlib, and others are available in Excel through a collaboration with Anaconda, an enterprise Python repository.
Python calculations run in the Microsoft Cloud will show the results returned into an Excel worksheet. With the potential to incorporate charting libraries like Matplotlib and Seaborn for visualizations like heatmaps, violin plots, and swarm plots, Excel users will be able to generate formulas, PivotTables, and charts based entirely on Python data.
Today, a public preview of Python in Excel is being made available to Microsoft 365 Insiders in the Beta Channel. It will initially only be compatible with Windows before being available on other platforms at a later date. During the preview, Python in Excel will be a part of a Microsoft 365 subscription, but some features will be restricted without a paid licence when the preview expires, according to Microsoft.
The developer of the well-known AI chatbot ChatGPT, OpenAI, has announced the availability of fine-tuning features for its GPT-3.5 Turbo model. Developers will be able to run the current model at scale and modify it according to their use cases, thanks to the new feature.
According to OpenAI, the fine tuned GPT-3.5 Turbo can even surpass the performance of GPT-4. In preliminary tests, a refined version of the model was able to match, or even outperform, base GPT-4-level capabilities on certain narrow tasks, according to OpenAI.
In order to increase a language model’s performance on a certain job, it can be fine-tuned by training it on a particular dataset. With OpenAI’s most recent development, companies can now fine-tune GPT 3.5 Turbo using their own datasets.
Businesses using the GPT-3.5 Turbo through the OpenAI API can fine-tune the model to make it more compliant, for example, by programming it to always reply in a specific language. Alternatively, they can enhance the model’s capacity to format responses consistently, for example, when completing snippets of code, and fine-tune the demeanor of the model’s output, such as its tone, to make it better fit a brand or voice.
Additionally, fine-tuning allows OpenAI users to reduce the length of their text prompts, accelerating API calls and lowering costs. According to OpenAI’s blog post, early testers have reduced prompt size by up to 90% by fine-tuning instructions into the model itself.
In order to fine-tune, one must currently prepare the data, upload the required files, and create a fine-tuning job using OpenAI’s API. The company claims that all fine-tuning data must go through a “moderation” API and a moderation system that is driven by GPT-4 to determine whether it violates OpenAI’s safety parameters.
But in the future, OpenAI intends to release a fine-tuning UI with a dashboard for monitoring the progress of ongoing fine-tuning workloads. OpenAI stated that fine-tuning support for GPT-4, which, in contrast to GPT-3.5, can recognise visuals in addition to text, will be available later this autumn.
The third edition of the Machine Learning (ML) Summer School has been announced by Amazon India. It is an immersive programme designed to give students the chance to study crucial ML technologies from Scientists at Amazon and prepare them for a career in machine learning. The registration for the program is open till September 6 and the program commences on September 16.
Eight modules will be covered over four weekends in September as part of the free educational programme, giving participants the chance to learn about machine learning challenges. The emphasis is on building a solid foundation in both theoretical ideas and real-world applications using supervised learning, deep neural networks, probabilistic graphical models, dimensionality reduction, and unsupervised learning.
A novel application-focused learning strategy is provided by Amazon’s ML Summer School, which departs from traditional approaches that place an emphasis on theory. Additionally, Amazon’s ML Summer School distinguishes out because it combines modern science courses at universities with contemporary business practices, emphasizes hands-on learning, and accepts students from a variety of ML backgrounds.
All engineering students enrolled in Bachelor’s, Master’s, or PhD programmes from any accredited institution in India who expect to graduate in 2024 or 2025 are eligible to attend the ML Summer School. All qualified students will be asked to complete an online test covering principles of arithmetic and machine learning in areas including probability, statistics, and linear algebra. The top 3000 students will then be accepted for the ML Summer School, where they will study eight modules live over the course of four weekends, with live Q&A sessions with Amazon scientists following each session.
The Amazon ML Summer School has advanced quickly and shown extraordinary progress since its pilot in 2021. Over 3500 students participated in the first programme, with the top 300+ students receiving invitations to attend. In 2022, the programme was enlarged based on its prior success, and over 17,500 engineering students from all around India registered. Out of these, 2880 pupils were chosen to participate in the programme. The class size will increase this year as it is being expanded to include engineering students registered in any accredited institute in India.
A US court in Washington DC has ruled that an artwork produced by artificial intelligence without any human involvement is not protected by copyright under US law. US District Judge Beryl Howell ruled on Friday that only work by human authors are eligible for copyrights, upholding the Copyright Office’s decision to deny computer scientist Stephen Thaler’s application on behalf of his DABUS system.
The ruling on Friday comes in the wake of failures for Thaler in his attempts to get US patents for ideas he claimed were produced by DABUS, or Device for the Autonomous Bootstrapping of Unified Sentience. With very limited success, Thaler has also applied for DABUS-generated patents in the UK, South Africa, Australia, and Saudi Arabia.
Ryan Abbott, Thaler’s attorney, declared on Monday that they will appeal the judgment since they strongly disagree with it. In a statement released on Monday, the Copyright Office stated that it “believes the court reached the correct result.”
New intellectual property concerns have emerged in the quickly expanding generative AI sector. Recently, the Copyright Office also rejected an artist’s request for copyrights on photographs produced by the AI system Midjourney, despite the artist’s claim that the system was an integral part of their creative process.
A number of ongoing legal actions have also been brought against the wrongful training of generative AI using copyrighted material. “We are approaching new frontiers in copyright as artists put AI in their toolbox,” Howell said on Friday, adding that this will lead to challenging questions for copyright law.
Thaler submitted an application for a copyright in 2018 for “A Recent Entrance to Paradise,” a work of art he claimed his AI system produced entirely on its own without any human input. Last year, the office denied the application and stated that in order for creative works to be copyrighted, they must have human writers.
In a lawsuit against the ruling, Thaler argued that human authorship is not a legally binding condition and that permitting AI copyrights would be consistent with the US constitution’s stated purpose for copyrights, which is to promote the progress of science and useful arts. The Copyright Office and Howell both agreed that human authorship is a bedrock requirement of copyright based on “centuries of settled understanding”.
In a letter to the company’s shareholders, Paytm founder Vijay Shekhar Sharma emphasized that the company is making investments in artificial intelligence, with a particular focus on developing a software stack for Artificial General Intelligence (AGI). This innovation can be used outside of India and is not just aimed at the Indian market.
Sharma said, “Paytm is investing in AI with the goal of creating a software stack for artificial general intelligence. By developing it here, we are also producing something that could be used outside of India, in addition to advancing India’s technological capacity.”
Small mobile-led credit is the first application that Paytm hopes to use AI for. “Paytm’s next important contribution to India’s digital revolution will be small mobile credit with good credit quality and complete adherence to regulators’ standards. This project calls for highly developed AI and other technological skills. I am incredibly happy with our current AI capabilities in use and our ongoing growth,” the CEO said.
Sharma also noted that Paytm has built a methodology for delivering small digital loans through payment partnerships with both customers and merchants over the past two years with success. In this approach, the risk is taken on by the lending partners of Paytm, who employ the fintech’s capabilities to streamline loan distribution and repayment.
Further adding to the AI capabilities of Paytm, Sharma said, “We are building an India-scale AI system, in order to help financial institutions identify possible hazards and frauds while also protecting them from new kinds of threats brought on by advances in AI.”
In a letter to Paytm’s shareholders in May, Sharma voiced his opinion that the rise of Artificial General Intelligence would provide opportunities to improve business efficiency and introduce AI-first solutions. In the near future, the company projects that there will be 100 million merchants and 500 million payment consumers in the nation.
According to a recent estimation report from IBM, almost 40% of workers or 1.4 billion of the 3.4 billion people in the global labor force will need to reskill over the next three years as a result of automation and artificial intelligence (AI).
About 87% of executives, according to tech giant IBM, believe that generative AI would improve job functions rather than replace them. That percentage is closer to three-quarters in the areas of marketing (73%) and customer service (77%) and exceeds 90% in the areas of finance (93%), risk and compliance (93%), and procurement (97%) respectively.
Moreover, three out of four executives claimed that entry-level positions are already being impacted, whereas only 22% agreed about the same for those in executive or senior management roles. The potential impacts of generative AI on their current staff have only been considered by 28% of CEOs.
According to the report, as AI develops further, its effects will probably become more pronounced everywhere, especially at the administrative and executive levels. No level is exempt from the effect and this will compel executives to reconsider job responsibilities, skill levels, and the way work is completed. says IBM.
The World Economic Forum (WEF) estimates that between 2020 and 2025, this transformation would disrupt 85 million jobs globally and create 97 million new job roles.
The experts also identified three key priorities that can help them elevate employees and gain a competitive edge. Transforming traditional processes, job roles, and organizational structures to boost productivity and enabling new business and operating models; developing human-machine partnerships that enhance value creation and employee engagement; and investing in technology that enables people to concentrate on higher-value tasks and fosters revenue growth.
The UK government will invest £100 million in an effort to establish the country as a contender in the race to create the computer chips that will fuel artificial intelligence innovation.
The taxpayer money will be utilized in an effort to create a national AI resource in Britain that is comparable to those being developed in the US and other countries. It is anticipated that funds will be used to place orders with significant semiconductor manufacturers Nvidia, AMD, and Intel for essential parts.
An official who was briefed on the plans, however, told that the government’s offer of £100 million is much too low when compared to investments made by competitors in the US, China, and the EU. The official confirmed that the government is in the advanced stages of placing an order for up to 5,000 graphics processing units (GPUs) from Nvidia, a move that was originally reported by the Telegraph.
Nvidia first built processing units for video games, and has now experienced a significant rise in value as the artificial intelligence competition has intensified. It is because its chips run ChatGPT and other leading large language models. GPUs, often known as graphics cards, are a crucial component of chips’ processing power and are in much demand.
In comparison to the US’s $52 billion Chips Act and the EU’s €43 billion in subsidies, Rishi Sunak’s government announced intentions to invest £1 billion over 10 years in semiconductor research, design, and production in May. In the face of escalating geopolitical tensions over AI chip technology, there is a fear that a halt in development brought on by comparatively poor investment may leave the UK vulnerable.
Applications for the well-known artificial intelligence fellowship, which is provided by the Robert Bosch Centre for Data Science and AI (RBCDSAI) at IIT Madras, have been reopened.
Graduates and postgraduates with an extraordinary academic record who are interested in working on the newly emerging fields of artificial intelligence and data science are eligible for the two-year fellowship. Throughout the course of the fellowship, those chosen will receive a monthly stipend of ₹40,000.
The primary goal of the IIT Madras artificial intelligence fellowship is to provide a platform for developing research abilities by giving the chosen applicants plenty of opportunity to study extensively, collaborate with colleagues, and develop creative solutions. The program’s primary goal is to instill ethical AI practices, ensuring that participants are prepared for work after the fellowship by developing ethical competence.
Candidates with a four-year bachelor’s degree or a master’s degree in a field related to RBCDSAI are eligible for the position. The candidate must be younger than 27 on March 31, 2023. In addition to having a stellar academic history, applicants should be interested in fields relevant to data science and artificial intelligence. The candidate must accept the offer right away, be available full-time, and suggest a joining date within six weeks of the offer letter’s receipt.
Participants will receive knowledge on artificial intelligence and data science, interact with leading experts from across the world, and the chance to collaborate on projects with illustrious organizations and businesses including Google, NASA, CMU, Walmart, JHU, MIT, The Ohio State University, Harvard, etc.
Research participants will also have access to cutting-edge high-performance CPU and GPU computing infrastructure. There is also a possibility to get course credits while receiving guidance from IIT Madras teachers.
Candidates who are interested and qualified must submit their applications through this Google Form, along with information like their selection of three RBCDSAI faculty members they would like to collaborate with. During the fellowship, chosen fellows may also apply for a PhD programme. However, they will need to give one month’s notice if they decide to leave.
With the advent of Artificial Intelligence, organizations are now more inclined towards digitalization and automation of operations. The functions of a data scientist have become central to decision-making in all types of businesses. A well-rounded pedagogy for machine learning and data science courses typically includes the following elements:
Theoretical foundations: Covering the mathematical concepts and principles behind different machine learning algorithms, such as probability, statistics, optimization, and generalization.
Hands-on practice: Students can implement and experiment with various machine learning algorithms on different data types using popular programming tools such as Python, R, and Matlab.
Data preparation and pre-processing: Teaching students the importance of data quality, cleaning, feature engineering, and data preparation and pre-processing techniques.
Model evaluation and selection: Emphasizing the importance of evaluating and comparing different models and selecting the most appropriate model for a given problem.
Real-world application: Providing examples of machine learning applications in various domains such as computer vision, natural language processing, and recommender systems.
Communication and interpretation: Emphasizing the importance of effectively communicating the results of machine learning models and understanding and interpreting the outputs of these models.
Ethics and safety: Teaching the ethical, societal, and safety considerations that arise with machine learning, such as bias, fairness, and explainability.
Continuous learning and staying updated on the field: Advise students on staying updated with the latest developments and advancements in the area, and continue learning and experimenting with machine learning.
They play a crucial role in machine learning as they provide a way to organize and manipulate data efficiently. Here are some commonly used data structures in machine learning:
• Arrays: An ordered collection of elements often used to store data in a contiguous memory block.
• Lists: These are dynamic data structures that can grow or shrink in size. It provides a way to store elements as separate nodes in memory.
• Tuples: An ordered, immutable collection of elements that can store different data types.
• Dictionaries: A key-value mapping data structure with unique keys used to look up values.
• Sets: A collection of unique elements, often used for set operations like union, intersection, etc.
• Matrices: A two-dimensional data structure widely used in linear algebra and numerical computations in machine learning.
• Trees are hierarchical data structures where each node has a parent and zero or more children used for decision-making and data classification tasks.
• Graphs are data structures that represent a set of vertices and edges that connect them. Applications include recommendation systems and social network analysis.
Additionally, specialized data structures like heaps, hash tables, and Bloom filters are helpful in specific scenarios in machine learning. Understanding these data structures and their operations helps select the proper structure for the task and improves the algorithms’ performance.
2. Machine Learning life-cycle:
It consists of six stages:
Problem Definition: Clearly define the problem and determine the goal of the model.
Data Collection: Gather and pre-process relevant data to train the model.
Data Preparation: Clean, format, and split the data into training and testing sets.
Model Selection: Choose an appropriate algorithm and fine-tune the hyperparameters.
Model Training: Train the model using the prepared data.
Model Evaluation: Evaluate the model’s performance using accuracy, precision, recall, etc.
It is important to note that the machine learning life cycle is an iterative process, with each stage influencing the next. For example, if the data is not of high quality, it may be necessary to go back to the data collection stage to gather more data or improve the pre-processing steps. Similarly, if the model is not performing well, it may be necessary to go back to the model selection stage to choose a different algorithm or fine-tune the hyperparameters. If the model performs well, it is deployed in the production environment and monitored for performance and accuracy. Periodically retrained models incorporate new data and maintain performance.
3. Languages:
Machine learning can be executed using several programming languages, including:
Python: It is the most widely used language for machine learning due to its simplicity, vast libraries (e.g., TensorFlow, PyTorch, Scikit-learn), and strong community support.
R: It is a statistical programming language widely used in academic and research settings. It offers several packages for machine learning, such as caret and mlr.
Java is a popular language for building enterprise-level applications and strongly supports machine learning libraries such as Weka and Deeplearning4j.
Julia: It is a high-level programming language designed for numerical and scientific computing and strongly supports machine learning through packages such as Flux.jl.
Scala: It is a statically-typed programming language that runs on the Java Virtual Machine and supports machine learning through libraries such as Spark MLlib.
The choice of language for machine learning depends on the specific project requirements and the expertise of the data scientists and developers involved. Python and R are the most widely used languages, while Java, Julia, and Scala are used for more specific projects.
4. Data visualization:
There are several platforms for data visualization in machine learning, including:
Matplotlib: It is a plotting library in Python that provides functionality for creating a variety of static, animated, and interactive visualizations.
Seaborn: It is a Python library based on Matplotlib that provides advanced visualization capabilities, including heatmaps, violin, and box plots.
Tableau is a data visualization and BI tool providing interactive dashboards and visualization capabilities.
ggplot2: A plotting library in R provides a flexible and intuitive syntax for creating static, animated, and interactive visualizations.
Plotly: It is a cloud-based platform with advanced visualization capabilities, including interactive dashboards and 3D visualizations.
These platforms provide a range of options for visualizing and exploring data, from a simple bar and line charts to more advanced visualizations such as heatmaps and interactive dashboards. The platform choice depends on the project’s specific requirements, the data scientists’ skills, and the available resources.
5. Machine learning in various industries:
ML has been applied in multiple industries, including:
Healthcare: It is used for diagnosis, prognosis, and personalized treatment plans
Finance: It is used for fraud detection, risk management, and algorithmic trading.
E-commerce: It is used for personalized recommendations, customer segmentation, and pricing optimization.
Transportation: It is used for route optimization, predictive maintenance, and autonomous vehicles.
Manufacturing: It is used for quality control, predictive maintenance, and supply chain optimization.
Agriculture: It is used for yield prediction, soil analysis, and precision farming.
Education: It is used for personalized learning, student assessment, and educational data analysis.
Industries use machine learning to automate processes, make predictions, and gain insights from data. The applications are diverse and continue to grow as machine learning advances. Since the field is ever-evolving, data scientists should be up-to-date with all new developments to create the most value.
Conclusion
In data science and machine learning, essential knowledge is pivotal. As industries embrace digital transformation, data scientists play crucial roles in decision-making. A comprehensive curriculum covers theoretical foundations, practical implementation, data preprocessing, model assessment, real-world applications, effective communication, and ethics. A standout course includes understanding data structures, the machine learning life cycle, programming languages, data visualization, and industry applications. With the field’s constant evolution, staying updated is critical for sustained success.
