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.
Researchers from Georgia Institute of Technology have proposed a simple approach to defending against harmful content generation by large language models by having a large language model filter its own responses. Their results show that even if a model is not fine-tuned to be aligned with human values, it is possible to stop it from presenting harmful content to users by validating the content using a language model.
LLMs have been shown to have the potential to generate harmful content in response to user prompting. There has been a focus on mitigating these risks, through methods like aligning models with human values through reinforcement learning. However, it has been shown that even aligned language models are susceptible to adversarial attacks that bypass their restrictions on generating harmful text. This is where the newly proposed method comes into light.
The approach for filtering out harmful LLM-generated content works by feeding the output of a model into an independent LLM, which validates whether or not the content is harmful. By validating only the LLM-generated content of a user-prompted LLM, and not the prompt itself, the approach potentially makes it harder for an adversarial prompt to influence their validation model.
First, the researchers conducted preliminary experiments to test the ability of the approach to detect harmful LLM-generated content. They randomly sampled 20 harmful prompts and 20 harmless prompts, generating responses to each. They used an uncensored variant of the Vicuña model to produce responses to each prompt. The researchers manually verified that the LLM-generated responses were indeed relevant to the prompts, meaning harmful prompts produce harmful content and harmless prompts produce harmless content.
They then instantiated their harm filter using several widely used large language models, specifically, GPT 3.5, Bard, Claude, and Llama-2 7B. They presented the Vicuña generated content to each of the LLM harm filters, which then produced a “yes” or “no” response. These responses act as a classifier output, which were then used to compute various quantitative evaluation metrics
According to experimental results, Claude, Bard, and GPT 3.5 performed similarly well at identifying and flagging harmful content, each reaching 97.5%, 95%, and 97.5% accuracy respectively. Llama 2 had the lowest performance on the sampled data with an accuracy of 80.9 %. According to the paper, this approach has the potential to offer strong robustness against attacks on LLMs.
After author Jane Friedman protested that five books listed as being written by her on Amazon were actually not written by her, Amazon pulled the titles from sale. The books were also listed on Goodreads which is owned by Amazon. Friedman believes that the books were written by AI.
Friedman said, “It feels like a violation since it’s extremely poor material with my name on it.” The author, who is from Ohio, has written a number of books about the publishing industry, and the fake titles imitated her legitimate work.
How to Write and Publish an eBook Quickly and Make Money and A Step-by-Step Guide to Crafting Compelling eBooks, Building a Thriving Author Platform, and Maximizing Profitability were some of the listed books. Friedman’s real books include Publishing 101 and The Business of Being a Writer.
A reader discovered the book listings on Amazon and emailed Friedman after thinking the listings were fake, which is how she first learned about the phony titles. After reading the first few pages, Friedman assumed the books were produced by AI since she had familiarity with AI technologies like ChatGPT.
According to Friedman, the books were “if not entirely generated by AI, then at least mostly generated by AI.” She immediately started looking for ways to have the books removed from Amazon and filled out a claim form. Friedman claims that Amazon informed her it would not take the books down because she had not registered a trademark for her identity.
By Tuesday, however, the books had been removed from both Amazon and Goodreads, and Friedman believes this was as a result of her addressing the problem on social media.
Friedman said, “This will continue; it won’t end with me, unless Amazon puts some sort of policy in place to stop anyone from just uploading whatever book they want and applying whatever name they want. They don’t have a process in place for reporting this kind of conduct, where someone is trying to cash in on someone else’s name.” She urged the websites to create a way to verify authorship.
A recent opinion by Analytics India Magazine (AIM) claimed that Sam Altman’s OpenAI, which is also the creator of the infamous ChatGPT, will go bankrupt by 2024 due to the decline in user base, astronomical operational costs, and unrealistic revenue expectations. However, on detailed analysis, the claims of AIM are found to be lacking expert insights, rendering the article to be perceived as mere sensationalism.
The article cited significant daily expenditures, notably around $700,000 per day (approximately ₹5.8 crore per day), dedicated solely to ChatGPT. However, the writert fails to consider the fact that such huge expenses are quite common for early stage startups. The report also mentions that ChatGPT user base has declined as users are making use of LLM APIs in their workflows. This assertion also seems to undermine the fact that APIs are also a major source of revenue for the startup.
Microsoft-backed OpenAI has projected an annual revenue of $200 million in 2023. According to the AIM article, OpenAI “expects to reach $1 billion in 2024, which seems to be a long shot since the losses are only mounting.” We must consider here that OpenAI is still in its initial operational phase and has several projects and resources to raise decent funding to stay afloat.
After the AIM opinion gained some traction, Ather Energy CEO Tarun Mehta took to Twitter to explain how the ChatGPT maker won’t go bankrupt despite various claims.
This post seems to be going around.
People are sharing that Open AI might go bankrupt next year because it burns through 5.8cr PER DAY!
Yeah, right.
5.8cr/day is ~2000cr/year = $250M/year.
Your friendly neighbourhood Swiggy, Meesho, Paytm, Ola, Flipkart have burnt through… pic.twitter.com/Nx4babff2F
Tarun Mehta of Ather Energy voiced confidence on Sunday that OpenAI will easily manage its predicament, despite the claims of AIM. Mehta cited well-known Indian startups that later became well-known corporations, such as Flipkart, Meesho, Ola, Paytm, and Swiggy, as evidence in favor of his claim. These companies, he noted, also experienced extended periods of significant financial burns.
In addition, he pointed out that many Indian companies have seen a comparable level of capital consumption during their peak moments, and many of them have been able to maintain stability. According to him, Uber, at its height, consumed ten times the capital for an extended period of time. “They will be fine folks,” he added.
OpenAI is funded by multiple large companies and is at the forefront of Large Language Models as of now. Microsoft has invested about $10 billion in OpenAI and there is every possibility that it will continue to invest more. For years, Microsoft has been in a tug-of-war with Google. Now, partnering with and investing in OpenAI gives Microsoft a once in life-time opportunity to make Google eat dirt, and we doubt that it will let OpenAI go bankrupt.
To add to the narrative, OpenAI today announced that it has acquired a New York-based AI design studio called Global Illumination. Now, it is only sensible to ask why would a startup, which is on the very precipice of bankruptcy, spend such crucial capital on acquisition. It only implies two things, either Sam Atham is out of his senses, or OpenAI is not going bankrupt.
Considering all these facts, it is safe to say that OpenAI will not be going bankrupt, or at least at any rate not because of the reasons cited by the AIM article, and certainly not as early as 2024.
The expansion of the Digital India Programme, which includes a ₹14,903 crore boost for e-governance services, cybersecurity, and the use of artificial intelligence, was approved by the Union Cabinet on Wednesday.
Initiatives under the expanded Digital India programme would prioritize cybersecurity. The Information Security & Education Awareness Phase (ISEA) Programme will provide training on information security to about 265,000 citizens.
The Indian Computer Emergency Response Team (CERT-In), the government’s official organization for cyber forensics, emergency response, and cyber diagnostics, would be greatly expanded, according to Ashwini Vaishnaw, Union Minister for Communication, Electronics, and Information Technology. Along with developing cybersecurity technologies, the plan will also integrate more than 200 locations with the National Cyber Coordination Centre.
As previously stated, the government intends to construct three Centres of Excellence (CoE) for the growth of the nation’s ecosystem for AI research and innovation, under this program. These centers will concentrate on sustainable cities, agriculture, and health. Moreover, 22 official Indian languages will all be supported by the AI-enabled multi-language translation tool Bhashini, which is currently offered in 10 languages.
Under the National Supercomputer Mission, the government will also install nine additional supercomputers for AI modeling and weather forecasting. This will be in addition to the existing 18 supercomputers.
To enable digital delivery of services to residents, the Digital India programme was introduced in July 2015. The programme will now run for a total of five years, from 2021–2022 to 2025–2026. Over 1,200 startups from Tier-II and Tier-III cities will receive help from the government throughout the extended time.
Approximately 625,000 IT employees will receive new training and up-skilling for next-generation technologies like the Internet of Things (IoT), machine learning, data analytics, and more, as part of the second phase of the government’s digital push.
