In a growing legal battle, more authors have joined the fray by suing OpenAI for copyright infringement. They are aligning with fellow writers in their pursuit of legal action against generative AI companies, which have been utilizing their literary works to train AI models.
The Authors Guild, along with 17 prominent authors such as Jonathan Franzen, John Grisham, George R.R. Martin, and Jodi Picoult, recently filed a lawsuit in the Southern District of New York. The plaintiffs are seeking to have their filing designated as a class action.
The lawsuit alleges that OpenAI engaged in the wholesale copying of the plaintiffs’ works without obtaining proper permission or providing compensation. OpenAI then incorporated these copyrighted materials into their extensive language models.
The complaint emphasizes the significance of the authors’ creative endeavors as their source of livelihood. It contends that OpenAI’s Language Models pose a direct threat to fiction writers’ income, as these LLMs enable the automatic and cost-free generation of text that would otherwise require payment to writers for their creation.
Moreover, the authors express concern that OpenAI’s LLMs may give rise to derivative works that imitate, condense, summarize, or rephrase their books, potentially impacting their market and income. The lawsuit also criticizes OpenAI for its choice to utilize copyrighted material without compensating authors, arguing that the company could have employed works in the public domain as an alternative.
This lawsuit is the most recent in a series of legal actions taken by renowned authors against OpenAI, alleging copyright infringement. Recently, Michael Chabon, the author of “The Amazing Adventures of Kavalier and Clay,” and others pursued legal action against the company for using their books to train GPT. Similarly, Paul Tremblay and Mona Awad lodged their complaint in June.
Renowned Indian actor Anil Kapoor has achieved a significant legal victory in a New Delhi court regarding the unauthorized use of his likeness through artificial intelligence technology. Kapoor stressed that this legal action isn’t just for his benefit but also for the future protection of his personality and its potential benefits for his family.
Kapoor, acclaimed for his roles in numerous Bollywood hits and the Oscar-winning film Slumdog Millionaire, secured an interim order against 16 defendants. The court has ruled that these parties are “restrained from in any manner utilizing Anil Kapoor’s name likeness, image, voice, or any other aspect of his persona to create any merchandise, ringtones, either for monetary gain or otherwise.”
Expressing his views on the decision, Kapoor emphasized the progressive nature of the ruling, stating, “I think the decision is very progressive and great not only for me but for other actors also, because of the way AI technology is evolving every day.”
The actor took legal action in response to the widespread use of distorted videos, gifs, and emojis bearing his likeness. He also sought protection for his catchphrase, “jhakaas,” which he coined in the 1985 film “Yudh” and roughly translates to “awesome.”
This legal development occurs amid a broader debate between US writers, actors unions, and studio representatives. A central issue in this debate is the use of AI to generate profits from an actor’s image indefinitely, without their consent or appropriate compensation.
Kapoor expressed solidarity with actors in the US who are currently on strike for their rights. He hopes that his victory will be seen as positive news for their cause, emphasizing that every actor, regardless of their popularity, deserves protection.
In a landmark move, Cisco Systems announced its acquisition of cybersecurity powerhouse Splunk for a staggering $28 billion, marking the largest technology deal of the year. This strategic acquisition is set to bolster Cisco’s software business and harness the growing wave of artificial intelligence.
The purchase comes as Cisco seeks to diversify its revenue streams and reduce dependency on its extensive networking equipment division, which has grappled with supply chain disruptions and a post-pandemic drop in demand. Chuck Robbins, Cisco’s CEO, emphasized the importance of this merger, stating that it unites two industry leaders in security and observability, domains critical to customers in an era marked by ever-evolving threats.
Splunk is renowned for its data observability capabilities, aiding companies in monitoring cybersecurity risks and other vulnerabilities. The company utilizes a subscription-based pricing model. While discussions between the two firms have occurred in the past, this time, the deal appears to be moving forward.
Cisco’s offer of $157 in cash per Splunk share, reflecting a 31% premium over the stock’s last closing price, has garnered attention. Splunk’s shares traded above $145.04, below the offer price, partly due to concerns about regulatory scrutiny, while Cisco’s shares dipped by 4%.
This acquisition will amplify revenue growth and boost gross margins for Cisco in the fiscal year following the deal’s closure. Splunk, which counts industry giants such as Coca-Cola and Intel among its 15,000+ customers, experienced a surge in revenue growth last year but faced a slowdown in 2023 due to rising interest rates and persistent inflation.
Microsoft has announced a significant leadership change as Pavan Davuluri takes the helm as Corporate Product Chief, succeeding Panos Panay. This transition places Davuluri in a pivotal position as he will be reporting directly to Microsoft’s CEO, Satya Nadella.
In parallel, Panay is set to join Amazon to head the unit responsible for the company’s Alexa and Echo products, stepping into the role previously held by David Limp, who plans to retire this year. Panos Panay, after nearly two decades at Microsoft, during which he played a crucial role in creating the Surface line of computers and overseeing the launch of Windows 11, steps down.
This leadership shift comes as Microsoft holds its product launch event, where new additions to the Surface lineup are anticipated, alongside the unveiling of the company’s latest AI innovations.
Pavan Davuluri, an alumnus of the Indian Institute of Technology (IIT) Madras and the University of Maryland, has been an integral part of Microsoft since the early stages of his career, starting in 2001 as a Reliability Component Manager.
His most recent role before this promotion was as Corporate Vice President, overseeing Windows Silicon and Systems Integration. In his new role, Davuluri is tasked with leading a team focused on the development of silicon and systems.
Davuluri will play a key role in Microsoft’s emphasis on integrating generative AI into its wide array of products, spanning cloud services, search, and productivity software. This highlights the company’s commitment to advancing technology in an increasingly AI-driven world.
GitHub has announced that it has expanded access to its programming-centric chatbot, Copilot Chat, to all current GitHub Copilot for Individual subscribers using Visual Studio and VS Code. Previously, Copilot Chat was exclusively available to organizations with a Copilot for Business subscription. This move allows individual users to access the chatbot’s features at no additional cost, provided they have the $10/month Copilot for Individual subscription.
Copilot Chat resides in the integrated development environment (IDE) sidebar, facilitating multi turn conversations about coding, including discussions about the code currently being worked on. GitHub emphasizes that Copilot Chat’s ability to understand and provide context-specific assistance sets it apart from general-purpose chat assistants.
Shuyin Zhao, VP of Product Management at GitHub, believes that the integration of Copilot Chat with GitHub Copilot creates a powerful AI assistant, capable of helping developers work efficiently in their preferred natural language. This, in turn, reduces repetitive tasks and positions natural language as a universal programming language.
Common use cases for Copilot Chat include real-time guidance on best practices, tailored code-related tips and solutions, and assistance with code analysis and security issue resolution, all within the IDE, eliminating the need to switch between tools.
GitHub’s goal is to promote “natural language as a new universal programming language” to democratize software development. This aligns with the company’s recent emphasis on enabling developers to work more efficiently and effectively.
OpenAI has unveiled the third iteration of its generative AI visual art platform, DALL-E, with enhanced features and a focus on safety. DALL-E 3, known for converting text prompts into images, has made significant strides in understanding context compared to its predecessor.
One notable addition to DALL-E 3 is its integration with ChatGPT, allowing users to generate prompts more easily. Users can request ChatGPT to provide a detailed prompt, and the chatbot will craft a paragraph that DALL-E 3 can work with, simplifying the creative process.
The new version of DALL-E will first be available to ChatGPT Plus and ChatGPT Enterprise users in October, followed by research labs and its API service in the fall. OpenAI has not provided a specific timeline for a free public release.
DALL-E’s journey began in January 2021, preceding other text-to-image generative AI art platforms. However, with the release of DALL-E 2 in 2022, OpenAI faced criticism for issues like generating explicit images and displaying bias. The platform was initially put on a waitlist but later made accessible to the public.
OpenAI has emphasized robust safety measures in DALL-E 3 to prevent the creation of inappropriate or harmful images. External red team testing and input classifiers have been employed to enhance safety. Additionally, DALL-E 3 will not generate images of public figures unless explicitly mentioned in the prompt.
While OpenAI expresses confidence in the safety measures, it acknowledges that the model continues to improve and is not flawless. DALL-E 3 has also been trained not to mimic the styles of living artists, unlike DALL-E 2, which could replicate certain artistic styles when prompted.
Microsoft has taken immediate action to address a significant security incident that led to the exposure of a staggering 38 terabytes of private data. The breach was identified within the company’s AI GitHub repository and is believed to have occurred inadvertently during the publication of open-source training data, according to Wiz, a cybersecurity research team.
This breach included a backup from the workstations of two former employees, containing sensitive information like secrets, keys, passwords, and over 30,000 internal Teams messages.
The repository, named “robust-models-transfer,” has been made inaccessible. Before its takedown, it housed source code and machine learning models related to a 2020 research paper titled “Do Adversarially Robust ImageNet Models Transfer Better?”
Wiz’s report revealed that the breach resulted from an overly permissive Shared Access Signature (SAS) token, an Azure feature that facilitates data sharing in a challenging-to-track and revoke manner. Specifically, the repository’s README.md file inadvertently allowed developers to download models from an Azure Storage URL that also granted access to the entire storage account, exposing additional private data.
To address this issue, Microsoft promptly revoked the SAS token and blocked external access to the storage account. The company’s investigation found no unauthorized exposure of customer data and confirmed that no other internal services were compromised.
The company also identified a bug in its scanning system that led to the false flagging of the specific SAS URL in the repository. To enhance future security measures, Microsoft has expanded its secret scanning service to include SAS tokens with overly permissive settings.
Conversation Intelligence Systems, a vital component of the modern tech landscape, play a pivotal role in deciphering and enhancing human-machine interactions. These systems employ advanced AI and natural language processing to comprehend the nuances of conversations, whether they occur between humans or involve machines. With the rising importance of personalized user experiences, startups are increasingly investing in Conversation Intelligence to refine customer support, automate tasks, and bridge the communication gap between users and technology. One such pioneering startup is Mihup.ai.
Mihup.ai
Mihup.ai is a pioneering startup at the forefront of natural language processing and artificial intelligence, poised to revolutionize the way we interact with technology. It is a Conversation Intelligence platform that helps businesses boost their contact center performance. It is built on proprietary ASR technology that offers the best blend of accuracy, speed, and cost-effectiveness. Founded on the principle that human-machine communication should be intuitive and seamless, Mihup.ai has harnessed cutting-edge AI technologies to create a dynamic platform that understands and responds to natural language with unprecedented accuracy and efficiency. The company’s products and services are used by businesses of all sizes, across a variety of industries, including telecommunications, financial services, healthcare, and retail.
Vision to Democratize AI
Mihup.ai was founded in 2016 by Tapan Barman and Biplab Chakraborty. Currently, Barman and Chakraborty are the CEO and COO of the company, respectively.
The initial idea and foundation of Mihup.ai centered around building the most accurate voice interface for the next billion users, particularly in regions where technology adoption was on the rise but faced barriers due to the complexity of the man-machine interface. In 2016, amidst a less prevalent AI landscape, the founders recognized the untapped potential of reaching users in these regions. They identified a significant challenge: despite the growth in technology, user experience had not evolved in line with it.
Priyanka Kamdar, Head of Growth at Mihup.ai said “People, especially in India, had reservations about interacting with machines due to language proficiency concerns and a fear of judgment. Mihup’s vision was to address these barriers by offering a display-less interaction mode for various types of communication, whether between humans and machines, machines and machines, or humans and humans. The company aimed to establish a protocol for adaptive conversations, bridging language and dialect gaps to cater to diverse user preferences.”
In nutshell, Mihup sought to create a platform where developers could leverage their technological prowess to build products tailored to the specific requirements of internet users worldwide, transcending language and cultural differences.
Priyanka Kamdar, a representative of Mihup.ai, explained the company’s three main products and services as follows:
Mihup Interaction Analytics
According to Kamdar, this product is designed for the contact center industry. It offers post-call analytics, addressing the challenges of manual quality assurance processes in call centers. While traditional methods could only sample a small percentage of calls, Mihup Interaction Analytics enables the analysis of 100% of interactions between customers and agents. It captures the voice of the customer, monitors agent performance, and generates reports for actionable insights. This ensures that valuable business insights are derived from all interactions, improving customer service and decision-making.
Mihup Agent Assist
Also serving the contact center industry, Mihup Agent Assist is a real-time assistant for call center agents. It operates during live customer-agent interactions, providing agents with guidance based on customer cues. For example, it can help agents adjust their tone when a customer is angry or suggest appropriate solutions. “This intelligent assistant enhances the agent’s performance and ensures a successful call outcome by bridging the gap between human-to-human and human-to-machine conversations,” said Kamdar.
Mihup AVA
Mihup’s third product is Mihup Automated Voice Assistant (AVA), a voice bot tailored for the automotive industry. It resides within a vehicle’s infotainment system and operates offline as well as online, making it suitable for use with and without an internet connection. This voice bot enhances the driving experience by performing in-vehicle functions and providing assistance to users. It is designed to work in multiple languages, including Hindi, English, Bengali, Tamil, Marathi, Haryanvi, and Malwari, making it versatile and user-friendly for a wide range of scenarios and users.
The Future of Customer Service with AI
Mihup.ai utilizes artificial intelligence extensively in its products and services. The company’s AI technology encompasses a wide range of components that contribute to the functionality of their offerings. For instance, in their voice bot product, AI components such as speech recognition, speech-to-text conversion, dialogue management, advanced natural language processing (NLP), and text-to-speech conversion are crucial. Mihup’s Interaction Analytics and Agent Assist uses speech recognition technology to transcribe customer conversations. This allows businesses to analyze customer interactions and identify areas for improvement. Mihup.ai uses natural language processing (NLP) to understand the meaning of customer conversations. The platform also offers a chatbot platform that businesses can use to automate customer interactions.
“Importantly, Mihup.ai has developed each of these components in-house, without relying on third-party solutions. This approach allows them to have complete control over their AI models and technology stack, ensuring that their products are tailored to their specific needs and requirements,” said Kamdar.
Unique Approach to Conversational AI
Mihup.ai differentiates itself from other startups in the conversational AI space through several key aspects. Firstly, their proprietary tech stack is entirely developed in-house, granting them full control over their product direction, security, and scalability. “Unlike language-based models, Mihup.ai’s approach is vocabulary-based and even goes beyond words to consider phonetic imprints and modulation, allowing them to detect not just keywords but also the context of interactions,” explained Kamdar.
The company’s focus is on speech-to-intent and meaning, addressing the challenge where machines are getting smarter but users struggle to use them effectively. “Mihup.ai aims to provide a horizontal platform that spans various industries, including contact centers, automotive, and government services, enabling developers to create tailored solutions,” she said. Their unique approach and adaptability position them as pioneers in the field of conversational AI, according to her.
Mihup.ai’s Funding Journey
“Mihup.ai’s funding journey began in 2016 when the company was formally established. However, securing initial funding for a deep tech startup like Mihup was challenging, as the concept was ahead of its time in India. Despite facing multiple rejections, Mihup.ai found its early investors in Accel and IdeaSpring Capital,” said Kamdar.
Mihup.ai has raised a total of $5 million in funding over 6 rounds. The funding has been used to expand Mihup.ai’s product offerings, grow its team, and expand into new markets, according to Kamdar.
Vision for the Future
“Mihup.ai’s future plans revolve around their larger vision, which is to become the interpreter for all types of conversations in the man-machine world,” says Kamdar. While they have short-term tactical goals related to product advancement and upgradation, their ultimate objective is to develop an interface that can handle verbal, non-verbal, written, or visual interactions between humans and machines.They foresee Mihup.ai playing a significant role in the IoT world of tomorrow, potentially becoming a key player in chatty operating systems, aligning with their vision of bridging the gap between human and machine communication.
Researchers at Tsinghua University, TAL AI Lab, and Zhipu.AI have introduced MathGLM, a robust mathematical model designed to handle a wide range of complex arithmetic operations. MathGLM’s performance rivals industry-leading models like GPT-4, excelling in tasks such as addition, subtraction, multiplication, division, and exponentiation.
What sets MathGLM apart is its versatility, as it effortlessly manages various number types, including decimals, fractions, percentages, and negative numbers. To train MathGLM, the team utilized the Ape210K dataset, a comprehensive collection of math word problems from across the internet.
The Ape210K dataset compiles math word problems sourced from the internet, serving as a rich repository of diverse mathematical challenges. It proves invaluable for training MathGLM due to its wide array of problem types and complexities.
Unlike traditional datasets, Ape210K contains explicitly calculated answers. However, to address potential limitations, the researchers employed a step-by-step approach to reconstruct the dataset, enhancing MathGLM’s ability to solve math word problems.
MathGLM’s unique strength lies in its ability to break down complex arithmetic calculations into sequential phases. This method significantly improved accuracy, with MathGLM outperforming GPT-4 by an impressive 42.29% when fine-tuned on the original dataset.
By dissecting arithmetic word problems into manageable steps, MathGLM demonstrates superior mathematical reasoning, learning underlying calculation principles, and delivering more dependable results. These discoveries profoundly challenge the traditional belief that LLMs are incapable of tackling complex arithmetic tasks, highlighting their remarkable capacity for advanced mathematical reasoning.
IBM has unveiled an ambitious initiative aimed at training a staggering two million learners in artificial intelligence (AI) by the end of 2026. This initiative is driven by the goal of addressing the global AI skills gap, with a specific focus on underrepresented communities.
As part of this initiative, IBM has introduced a new generative AI coursework accessible through IBM SkillsBuild, designed to provide AI education developed by IBM experts to learners worldwide. The curriculum covers a wide range of topics, including Prompt-Writing, Getting Started with Machine Learning, Improving Customer Service with AI, and Generative AI in Action. These courses are offered free of charge.
IBM is enhancing the learning experience with AI-enabled features, including improved chatbots to support learners and personalized learning paths tailored to individual preferences and experiences. Upon course completion, participants can earn IBM-branded digital credentials recognized by potential employers.
IBM is collaborating with universities worldwide to bolster AI capacity, providing faculty with access to IBM-led training, courseware, and immersive skill experiences. Additionally, students will benefit from flexible and adaptable resources, including free online courses on generative AI and Red Hat open-source technologies.
This initiative aligns with IBM’s broader commitment to upskill 30 million individuals by 2030 and addresses the pressing skills gap in the application of AI and digitalization across various industries. IBM’s endeavor is in line with similar initiatives by tech giants like MongoDB and Infosys, who are also offering complimentary courses to empower individuals through self-learning, opening doors to employment opportunities based on performance in tasks and certifications.
