What’s the news?

• Leading AI companies are entering a new race to embrace multimodal capabilities.

Central idea

• AI’s next frontier is undoubtedly headed toward multimodal systems, enabling users to interact with AI through various sensory channels. People gain insights and context by interpreting images, sounds, videos, and text, making multimodal AI a natural evolution for comprehensive cognition.

A New Race to Embrace Multimodal Capabilities

• OpenAI, known for ChatGPT, recently announced that GPT-3.5 and GPT-4 models can now understand images and describe them in words.
• Additionally, their mobile apps are equipped with speech synthesis, enabling dynamic conversations with AI.
• OpenAI initially promised multimodality with GPT-4’s release but expedited its implementation following reports of Google’s Gemini, a forthcoming multimodal language model.

Google’s Advantage and OpenAI’s Response

• Google enjoys an advantage in the multimodal realm because of its vast image and video repository through its search engine and YouTube.
• Nevertheless, OpenAI is rapidly advancing in this space. They are actively recruiting multimodal experts, offering competitive salaries of up to $3,70,000 per year.
• OpenAI is also working on a project called Gobi, which aims to build a multimodal AI system from the ground up, distinguishing it from their GPT models.

What is multimodal artificial intelligence?

• Multimodal AI is an innovative approach in the field of AI that aims to revolutionize the way AI systems process and interpret information by seamlessly integrating various sensory modalities.
• Unlike conventional AI models, which typically focus on a single data type, multimodal AI systems have the capability to simultaneously comprehend and utilize data from diverse sources, such as text, images, audio, and video.
• The hallmark of multimodal AI lies in its ability to harness the combined power of different sensory inputs, mimicking the way humans perceive and interact with the world.

The Mechanics of Multimodality

• Multimodal AI Basics: Multimodal AI processes data from various sources simultaneously, such as text, images, and audio.
• DALL.E’s Foundation: DALL.E, a notable model, is built upon the CLIP model, both developed by OpenAI in 2021.
• Training Approach: Multimodal AI models link text and images during training, enabling them to recognize patterns that connect visuals with textual descriptions.
• Audio Multimodality: Similar principles apply to audio, as seen in models like Whisper, which translates speech in audio into plain text.

Applications of multimodal AI

• Image Caption Generation: Multimodal AI systems are used to automatically generate descriptive captions for images, making content more informative and accessible.
• Video Analysis: They are employed in video analysis, combining visual and auditory data to recognize actions and events in videos.
• Speech Recognition: Multimodal AI, like OpenAI’s Whisper, is utilized for speech recognition, translating spoken language in audio into plain text.
• Content Generation: These systems generate content, such as images or text, based on textual or visual prompts, enhancing content creation.
• Healthcare: Multimodal AI is applied in medical imaging to analyze complex datasets, such as CT scans, aiding in disease diagnosis and treatment planning.
• Autonomous Driving: Multimodal AI supports autonomous vehicles by processing data from various sensors and improving navigation and safety.
• Virtual Reality: It enhances virtual reality experiences by providing rich sensory feedback, including visuals, sounds, and potentially other sensory inputs like temperature.
• Cross-Modal Data Integration: Multimodal AI aims to integrate diverse sensory data, such as touch, smell, and brain signals, enabling advanced applications and immersive experiences.

Complex multimodal systems

• Meta introduced ImageBind, a multifaceted open-source AI multimodal system, in May this year. It incorporates text, visual data, audio, temperature, and movement readings.
• The vision is to add sensory data like touch, speech, smell, and brain fMRI signals, enabling AI systems to cross-reference these inputs much like they currently do with text.
• This futuristic approach could lead to immersive virtual reality experiences, incorporating not only visuals and sounds but also environmental elements like temperature and wind.

Real-World Applications

• The potential of multimodal AI extends to fields like autonomous driving, robotics, and medicine. Medical tasks, often involving complex image datasets, can benefit from AI systems that analyze these images and provide plain-language responses. Google Research’s Health AI section has explored the integration of multimodal AI in healthcare.
• Multimodal speech translation is another promising segment, with Google Translate and Meta’s SeamlessM4T model offering text-to-speech, speech-to-text, speech-to-speech, and text-to-text translations for numerous languages.

Conclusion

• The future of AI lies in embracing multimodality, opening doors to innovation and practical applications across various domains.

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What’s the news?

• Generative artificial intelligence (AI) has emerged as a potent force in the digital landscape, raising critical questions about regulation, copyright, and potential risks.

Central Idea

• In a remarkably short period, chatbots such as ChatGPT, Bard, Claude, and Pi have demonstrated the remarkable potential of generative AI applications. However, these AI marvels have also exposed their vulnerabilities, prompting policymakers and scientists worldwide to grapple with the question, whether generative AI should be subject to regulation.

What is generative AI?

• Like other forms of artificial intelligence, generative AI learns how to take actions based on past data.
• It creates brand-new content—a text, an image, even computer code—based on that training instead of simply categorizing or identifying data like other AI.
• The most famous generative AI application is ChatGPT, a chatbot that Microsoft-backed OpenAI released late last year.
• The AI powering it is known as a large language model because it takes in a text prompt and, from that, writes a human-like response.

What is the legal framework on which generative AI rests?

• U.S. Copyright Approach:
  • In the United States, copyright law recognizes only humans as copyright holders.
  • Consequently, AI-generated works often fall outside the scope of copyright protection.
  • This situation poses challenges when it comes to attributing authorship to AI-generated content.
• India’s Ambiguity:
  • India’s position on AI-generated content and copyright remains ambiguous.
  • A recent case highlights this ambiguity, where a copyright application for an AI-generated work was initially rejected.
  • The lack of clear guidelines in India regarding copyright protection for AI-generated content adds complexity to the legal landscape.

The European Union’s AI Act

• Individual Rights: The EU AI Act places a strong emphasis on safeguarding individual rights within the AI landscape. It seeks to protect individuals from potential AI-related harm, ensuring that their rights are upheld.
• Leveling the Playing Field: Recognizing the dominance of large tech corporations in AI development, the Act aims to foster a more competitive environment. This involves measures to reduce the concentration of AI development within a select few companies, promoting innovation and diversity.
• Transparency Obligations: The AI Act introduces transparency requirements for AI-generated content. Specifically, it mandates the labeling of AI-generated material as such and requires summaries of the training data used. These provisions aim to enhance transparency and accountability in AI systems.

Contrasting Approaches: Risk-Based vs. Relaxed Regulation

• EU’s Risk-Based Approach:
  • In contrast, the European Union employs a risk-based approach to AI regulation.
  • This approach involves delineating prohibitions on certain AI practices, recommending ex-ante assessments for others, and enforcing transparency requirements for low-risk AI systems.
  • The EU’s approach acknowledges the multifaceted risks posed by AI and seeks to mitigate them effectively.
• U.S. Regulatory Approach:
  • The United States maintains a relatively relaxed approach to AI regulation, which may be attributed to underestimating the associated risks or a general reluctance towards extensive regulation.
  • This approach raises concerns, especially in sectors like education, where there is minimal control over the use of generative AI tools by students, including age and content restrictions.
  • Additionally, discussions regarding the regulation of AI risks, particularly in the context of disinformation campaigns and deepfakes, are notably limited in the U.S.

AI Through an Indian Legal Lens

• Comprehensive Regulatory Framework: India necessitates a comprehensive regulatory framework that spans both horizontal regulations applicable across sectors and vertical regulations specific to distinct industries. The absence of such regulations results in uncertainties and impediments to effectively addressing AI-related issues.
• Data Protection Clarity: The Digital Personal Data Protection (DPDP) Act of 2023 plays a pivotal role in addressing data protection concerns. However, the DPDP Act exhibits certain gaps, such as legitimizing data scraping by AI companies when data is publicly available.

Challenges surrounding trade secrets and transparency in the context of AI

• Trade Secrets:

• Corporations frequently employ trade secrets to safeguard their AI models and training data from disclosure.
• Nevertheless, when AI systems have the potential to cause significant societal harm, there may arise a need to compel companies to divulge these particulars.
• This predicament raises questions about achieving a balance between safeguarding trade secrets and addressing the broader societal consequences of AI.

• Transparency:

• Guaranteeing transparency in AI systems holds paramount importance, particularly when AI-generated content is disseminated.
• The societal imperative for transparency, particularly in instances where AI-generated content might be exploited for malicious purposes or cause harm,

Way forward

• Continued Dialogue: Policymakers, legal experts, industry leaders, and stakeholders should engage in ongoing discussions and collaboration to develop effective regulations and guidelines for generative AI.

• Ethical Considerations: The development and deployment of AI systems should prioritize ethical principles to ensure responsible use and mitigate potential harms.
• Transparency and Accountability: There should be efforts to promote transparency in AI systems, especially when AI-generated content is involved. Accountability mechanisms should also be in place to address issues arising from AI use.
• Comprehensive Regulation: Governments and international bodies may consider developing comprehensive regulatory frameworks that encompass various aspects of AI, including data protection, transparency, accountability, and liability.
• Public Education: Initiatives to educate the public about AI’s implications, benefits, and limitations should be developed, particularly in sectors where AI is extensively used, such as education.

Conclusion

• The global regulation of generative AI emerges as a pressing concern. Adaptive and thoughtful regulatory approaches are essential to address the evolving challenges and opportunities introduced by generative AI on a global scale.

Also read:

AI generative models and the question of Ethics

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Central Idea

• Artificial Intelligence (AI) has gained worldwide attention, and many mature democracies are using it for better legislative procedures. In India, AI can be used to assist parliamentarians in preparing responses for legislators, enhancing research quality, and obtaining information about any Bill, legislative drafting, amendments, interventions, and more. However, before AI can work in India, there is a need to codify the country’s laws, which are opaque, complex, and face a huge translation gap between law-making, law-implementing, and law-interpreting organizations.

What is Artificial Intelligence?

• AI is a constellation of technologies that enable machines to act with higher levels of intelligence and emulate the human capabilities of sense, comprehend and act.
• The natural language processing and inference engines can enable AI systems to analyze and understand the information collected.
• An AI system can also take action through technologies such as expert systems and inference engines or undertake actions in the physical world.
• These human-like capabilities are augmented by the ability to learn from experience and keep adapting over time.
• AI systems are finding ever-wider application to supplement these capabilities across various sectors

Need to Codify Laws

• Current laws are complex and opaque: Current laws in India pose many challenges, such as their complexity, opaqueness, and lack of a single source of truth.
• The India Code portal does not provide complete information: The India Code portal is not enough to provide complete information about parent Acts, subordinate legislation, and amendment notifications.
• AI can be used to provide comprehensive information: There is a need to make laws machine-consumable with a central law engine, which can be a single source of truth for all acts, subordinate pieces of legislation, gazettes, compliances, and regulations. AI can use this engine to provide information on applicable acts and compliances for entrepreneurs or recommend eligible welfare schemes for citizens.

Assisting Legislators

• Potential of AI for legislators: AI can help Indian parliamentarians manage constituencies with a huge population by analysing citizens’ grievances and social media responses, flagging issues that need immediate attention and assisting in seeking citizen inputs for public consultation of laws and preparing a manifesto.
• AI-powered assistance: Many Parliaments worldwide are now experimenting with AI-powered assistants.
• For instance:
• Netherlands’s Speech2Write system: The Speech2Write system in the Netherlands House of Representatives, which converts voice to text and translates voice into written reports.
• AI tools Japan: Japan’s AI tool assists in preparing responses for its legislature and helps in selecting relevant highlights in parliamentary debates.
• Brazil: Brazil has developed an AI system called Ulysses, which supports transparency and citizen participation.
• NeVA portal India: India is also innovating and working towards making parliamentary activities digital through the ‘One Nation, One Application’ and the National e-Vidhan (NeVA) portal.

Simulating Potential Effects of Laws

• Dataset modelling: AI can simulate the potential effects of laws by modelling various datasets such as the Census, data on household consumption, taxpayers, beneficiaries from various schemes, and public infrastructure.
• Flag outdated laws: In that case, AI can uncover potential outcomes of a policy and flag outdated laws that require amendment.
• For example: During the COVID-19 pandemic, ‘The Epidemic Diseases Act, 1897’ failed to address the situation when the virus overwhelmed the country. Several provisions in the Indian Penal Code (IPC) are controversial and redundant, such as Article 309 (attempted suicide) of the IPC continues to be a criminal offense. Many criminal legislation pieces enacted more than 100 years ago are of hardly any use today.

Conclusion

• The COVID-19 pandemic has given a strong thrust to the Digital India initiative, and a digitization of services needs to be kept up in the field of law, policy-making, and parliamentary activities, harnessing the power of AI. However, the use of AI must be encouraged in an open, transparent, and citizen-friendly manner, as AI is a means to an end, not an end in itself. Therefore, it is necessary to address the current challenges faced by India’s laws before AI can be effectively used to assist parliamentarians in their legislative duties.

Mains Question

Q. Artificial Intelligence (AI) has gained worldwide attention, and many mature democracies are using it for better legislative procedures. In this light evaluate the potential of AI in assisting Indian parliamentarians.

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Central Idea

• OpenAI’s GPT-4, the latest AI model, is creating shock waves around the world. It has incredible capabilities, but also raises ethical questions and concerns about its potential misuse.

Capabilities of GPT-4

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Context

• With the launch of Open AI’s ChatGPT late last year, the impending changes in the nature of work, creativity and economy as a whole have moved from being the subject of futuristic jargon to an immediate challenge.

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Background

• Since at least 2015 when Klaus Schwab popularised the term Fourth Industrial Revolution at that year’s World Economic Forum terms like 4IR, Artificial Intelligence (AI), Internet of Things, Future of Work, entered the lexicon of politicians, bureaucrats, consultants and policy analysts.

Sample developments over just the last few days

• A judge in Colombia included his conversations with ChatGPT in a ruling;
• Microsoft is integrating the bot with its search engine, Bing, and other products;
• Google is reportedly trying to launch a similar tool and there are reports that ChatGPT can already code at entry level for Google engineers.

What are the Concerns?

• Lifestyle may become redundant: Concerns about plagiarism in universities and beyond, as well as the fear that many white-collar jobs may become redundant in the coming years, as AI becomes more ubiquitous and sophisticated.
• Implications on labour, education and authenticity: The AI revolution is likely to have serious implications on labour, education, authenticity of content and its authorship, and much else.
• Case of Social media’s influence in US elections: The concerns around social media’s influence on politics and society became sharp in the aftermath of the 2016 US presidential elections and accusations of voter manipulation by foreign agents. Much of the world is still struggling with the questions raised then.

Do you what exactly ChatGPT is?

• Simple definition: ChatGPT is a chatbot built on a large-scale transformer-based language model that is trained on a diverse dataset of text and is capable of generating human-like responses to prompts.
• A human like language model: It is based on GPT-3.5, a language model that uses deep learning to produce human-like text.
• It is more engaging with details: However, while the older GPT-3 model only took text prompts and tried to continue on that with its own generated text, ChatGPT is more engaging. It’s much better at generating detailed text and can even come up with poems.
• Keeps the memory of the conversations: Another unique characteristic is memory. The bot can remember earlier comments in a conversation and recount them to the user.
• Human- like resemblance: A conversation with ChatGPT is like talking to a computer, a smart one, which appears to have some semblance of human-like intelligence.

Anticipating possible futures requires engagement with the opportunities

• The Struggle to keep up with technology in policymaking:

1. Governments worldwide face a challenge in creating policies that keep up with the rapid pace of technological advancement.
2. Policymakers should understand that they must work to bridge the gap between technology and regulation, as a growing divide could lead to problems.

• Preparing for technological change in education and workforce:

1. In addition to creating regulations that support innovation, it’s crucial to plan for the changes that new technology will bring to education and employment.
2. This includes anticipating new job types and skills required, as well as updating the education system to prepare future workers.

• Importance of Preparing for technological change for India:

1. India has been facing the challenge of balancing privacy and regulation in the handling of data for several years.
2. Successfully adapting to technological changes is crucial for India to make the most of its large, young workforce. If not addressed in time, the consequences could be severe

Conclusion

• The transformations the new technology is bound to bring about must be met with swift adjustments in the broader national and international legal and policy architecture. The lag between technology innovation and policy that was seen with the rise of Big Data and social media can serve as a lesson.

Mains Question

Q. With the rapid innovations and launching of Artificial intelligence models everyday will change the nature of work, creativity and economy as a whole. comment

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Context

• Around the end of last year, social media spaces were trending with Lensa-generated images of online users. A subscription app, Lensa, makes graphic portraits, called “Magic Avatar” images, using selfies uploaded by its users. As AI takes a strong foothold over the realm of art, are we equipped with mechanisms to define what is right and what is wrong in this domain in the first place?

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The case of Lensa app

• A subscription app, Lensa, makes graphic portraits, called Magic Avatar images, using selfies uploaded by its users.
• Celebrities worldwide stepped in to show how they looked so perfect in their avatars in a Lensa world.
• However, a few days later, hundreds of women netizens worldwide started flagging issues with their avatars. They pointed out how their avatar images had their waists snatched and showed sultry poses.
• Even after these women uploaded different pictures, Lensa generated hyper-sexualised, semi-pornographic images.

How art is generated using Artificial Intelligence?

• Uses algorithms based on textual prompts: AI art is any art form generated using Artificial Intelligence. It uses algorithms that learn a specific aesthetic based on textual prompts and, after that, go through vast amounts of data in the form of available images as the first step.
• Algorithms generate new images: In the next step, the algorithm tries to generate new images that tally with the kind of aesthetics that it has learnt.
• Role of artists with right keystrokes: The artist becomes more like a curator who inputs the right prompt to develop an aesthetically-fulfilling output. While artists use brush strokes in other digital platforms like Adobe Photoshop, in programmess like Dall-E and Midjourney, all it takes are keystrokes.
• For example: The generation of an artwork like Starry Night in the digital era. While Van Gogh would have taken days of effort to conceptualise and get the correct strokes and paint, in the AI art era, it is just a matter of the right textual prompts.

Can it truly capture the essence of humanity?

• The impact of AI-generation on the masses’ experience of art: Art is one of the few pursuits that makes life meaningful. It remains to be seen if AI-generated art will alienate the experience of art from the masses.
• AI takes away the satisfaction of creating artworks: AI-generated art dehumanises artworks. Perhaps the most satisfying aspect of generating an artwork lies in making it.
• The questions over the capability of AI to capture subtle human emotions: It is also doubtful whether AI art will capture the most subtle of human emotions. How much humour is “humorous” for AI? Can AI express grief and pain in the most profound ways as described by our poets? Can AI capture the enigmatic smile of Mona Lisa that makes one believe that she is shrouded in mystery?

Have you heard about Midjourney?

• Midjourney is an AI based art generator that has been created to explore new mediums of thought.
• It is an interactive bot, which uses machine learning (ML) to create images based on texts. This AI system utilises the concepts and tries to convert them into visual reality.
• It is quite similar to other technologies such as DALL-E 2.

Arguments in favor of such art

• Thatre D opera Spatial generated by Midjourney: The question of whether AI art is causing “a death of artistry” was raised, last year, when an entry called “Théâtre D’opéra Spatial” generated from Midjourney (an artificial intelligence programme) by Jason M Allen won the Blue Ribbon at the Colorado State Fair.
• Finding suitable prompts is no less than a genius art: AI artists like Allen think finding suitable prompts to create an artwork amounts to creativity and qualifies AI art as genuine or authentic.
• AI could democratise art world: Some artists believe AI art could democratise the art world by removing gatekeepers.

Concerns over the biases in data

• There is bias in this data available for AI inputs due to a lack of representation of the less privileged communities’ women, people of colour and other marginalised groups.
• Most of the training data for AI art currently emerges in the Global North and is often mired by the stereotypes of ableism, racism and sexism.
• Historically, art has performed a political function as a venue for dissent. Can AI art overcome these inherent biases in data to bring out meaningful political engagement?

Conclusion

• AI-generated art can bring new ideas and possibilities to the art world, but it is important to think about how it might change people’s experience of art and if it takes away the human touch. It is also important to question if AI can truly capture the emotions that make art so special. It’s best to approach AI-generated art with an open mind and consider both the good and bad.

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Context

• 2022 had an unusual blue-ribbon winner for emerging digital artists; Jason Allen’s winning work Théâtre D’opéra Spatial was created with an AI Generative model called Midjourney.

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What is Midjourney?

• Midjourney is an AI based art generator that has been created to explore new mediums of thought.
• It is an interactive bot, which uses machine learning (ML) to create images based on texts. This AI system utilises the concepts and tries to convert them into visual reality.
• It is quite similar to other technologies such as DALL-E 2.

The journey of AI generative models so far

• Midjourney generator: Midjourney is one of the rash of AI-generated Transformer or Generative or Large Language Models (LLMs) which have exploded onto our world in the last few years.
• Earlier models: Models like BERT and Megatron (2019) were relatively small models, with up to 174 GB of dataset size, and passed under the collective public radar.
• Composition skills of GPT3: GPT3, released by OpenAI with a 570 GB dataset and 175bn parameters was the first one to capture the public consciousness with some amazing writing and composition skills.
• Models that creat images or videos based on texts: The real magic, however, started with Transformers which could create beautiful and realistic pieces of art with just a text prompt OpenAI’s DALL-E2, Google’s Imagen, the open-source Stable Diffusion and, obviously, Midjourney. Not to be left behind, Meta unleashed a transformer which could create videos from text prompts.
• ChatGPT, a latest and more evolved, like real communication: Recently in late 2022 came the transformer to rule them all ChatGPT built on GPT3, but with capabilities to have real conversations with human beings.

Are these models ethical?

• Ethics is too complex a subject to address in one short article. There are three big ethical questions on these models that humanity will have to address in short order.

1. Environmental: Most of the bad rap goes to crypto and blockchain, but the cloud and these AI models running on it take enormous amounts of energy. Training a large transformer model just once would have CO2 emissions equivalent to 125 roundtrips from New York to Beijing. This cloud is the hundreds of data centres that dot our planet, and they guzzle water and power at alarming rates.
2. Bias; as it do not understand meaning and its implications: The other thorny ethical issue is that sheer size does not guarantee diversity. Timnit Gebru was with Google when she co-wrote a seminal research paper calling these LLMs ‘stochastic parrots’, because, like parrots, they just repeated a senseless litany of words without understanding their meaning and implications.
3. Plagiarism, question of who owns the original content: The third prickly ethical issue, which also prompted the artist backlash to Allen’s award-winning work is that of plagiarism. If Stable Diffusion or DALL-E 2 did all the work of scouring the web and combining multiple images (a Pablo Picasso Mona Lisa, for example), who owns it. Currently, OpenAI has ownership of all images created with DALL-E, and their business model is to allow paid users to have rights to reproduce, paint, sell and merchandise images they create. This is a legal minefield the US Copyrights office recently refused to grant a copyright to a piece created by a generative AI called Creativity Machine, but South Africa and Australia have recently announced that AI can be considered an inventor.

Do you know ChatGPT?

• ChatGPT is a chatbot built on a large-scale transformer-based language model that is trained on a diverse dataset of text and is capable of generating human-like responses to prompts.
• A conversation with ChatGPT is like talking to a computer, a smart one, which appears to have some semblance of human-like intelligence.

What are the other concerns?

• Besides the legal quagmire, there is a bigger fear: This kind of cheap, mass-produced art could put artists, photographers, and graphic designers out of their jobs.
• Machine does not have human like sense: A machine is not necessarily creating art, it is crunching and manipulating data and it has no idea or sense of what and why it is doing so.
• As it is cheap, corporate might consider using it at a large scale: But it can do so cheaply, and at scale. Corporate customers might seriously consider it for their creative, advertising, and other needs.

Conclusion

• Legal and political leaders across the world are sounding the alarm about the ethics of large generative models, and for good reason. As these models become increasingly powerful in the hands of Big Tech, with their unlimited budgets, brains and computing power, these issues of bias, environmental damage and plagiarism will become even more fraught. Such AI models should not be used to create chaos rather a harmonious existence.

Mains question

Q. Name some of the models of AI based art generators. Discuss the ethical concerns of such models.

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Context

• Many of us are familiar with the concept of what a “chatbot” is and what it is supposed to do. But this year, OpenAI’s ChatGPT turned a simple experience into something entirely different. ChatGPT is being seen as a path-breaking example of an AI chatbot and what the technology could achieve when applied at scale.

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Background

• ChatGPT by OpenAI: Artificial Intelligence (AI) research company OpenAI on recently announced ChatGPT, a prototype dialogue-based AI chatbot capable of understanding natural language and responding in natural language.
• Will be able to implement in softwares soon: So far, OpenAI has only opened up the bot for evaluation and beta testing but API access is expected to follow next year. With API access, developers will be able to implement ChatGPT into their own software.
• Remarkable abilities: But even under its beta testing phase, ChatGPT’s abilities are already quite remarkable. Aside from amusing responses like the pumpkin one above, people are already finding real-world applications and use cases for the bot.

What is Chatbot?

• A chatbot (coined from the term “chat robot”) is a computer program that simulates human conversation either by voice or text communication, and is designed to help solve a problem.
• Organizations use chatbots to engage with customers alongside the classic customer service channels like phone, email, and social media.

What is ChatGPT?

• Simple definition: ChatGPT is a chatbot built on a large-scale transformer-based language model that is trained on a diverse dataset of text and is capable of generating human-like responses to prompts.
• A human like language model: It is based on GPT-3.5, a language model that uses deep learning to produce human-like text.
• It is more engaging with details: However, while the older GPT-3 model only took text prompts and tried to continue on that with its own generated text, ChatGPT is more engaging. It’s much better at generating detailed text and can even come up with poems.
• Keeps the memory of the conversations: Another unique characteristic is memory. The bot can remember earlier comments in a conversation and recount them to the user.
• Human- like resemblance: A conversation with ChatGPT is like talking to a computer, a smart one, which appears to have some semblance of human-like intelligence.

The Question arises: will AI replace all of our daily writing?

• ChatGPT is not entirely accurate: It is not entirely accurate, something even OpenAI has admitted. It is also evident that some of the essays written by ChatGPT lack the depth that a real human expert might showcase when writing on the same subject.
• ChatGPT lacks depth like human mind: It doesn’t quite have the nuance that a human would often be able to provide. For example, when asked ChatGPT how one should cope with a cancer diagnosis. The responses were kind but generic. The type of responses you would find in any general self-help guide.
• It lacks same experiences as humans: AI has a long way to go. After all, it doesn’t have the same experiences as a human.
• ChatGPT doent excel in code: ChatGPT is writing basic code. As several reports have shown, ChatGPT doesn’t quite excel at this yet. But a future where basic code is written using AI doesn’t seem so incredible right now.

Limitations of ChatGPT

• ChatGPT is still prone to Misinformation: Despite of abilities of the bot there are some limitations. ChatGPT is still prone to misinformation and biases, which is something that plagued previous versions of GPT as well. The model can give incorrect answers to, say, algebraic problems.
• ChatGPT can write incorrect answers: OpenAI understands some flaws and has noted them down on its announcement blog that “ChatGPT sometimes writes plausible-sounding but incorrect or nonsensical answers.

Conclusion

• OpenAI’s ChatGPT turned that simple experience into something entirely different. ChatGPT is a path-breaking example of an AI chatbot and what the technology could achieve when applied at scale. Limitations aside, ChatGPT still makes for a fun little bot to interact with. However, there are some challenges that needs to be addressed before it becomes a unavoidable part of human life.

Manis question

Q. What is ChatGPT? Discuss why it is seen as pathbreaking example of an AI chatbot and the limitations?

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