ChatGPT and similar tools are powered by generative artificial intelligence (AI), which facilitates the virtual creation of new content in any format – images, textual content, audio, video, code, and simulations. While the adoption of AI has been on the rise, Generative AI is expected to bring in another level of transformation, changing how we approach many business processes.

ChatGPT (generative pretrained transformer), for instance, was launched only in November 2022 by Open AI. But, from then to now, it has become very popular because it generates decent responses to almost any question. In fact, in just 5 days, more than a million users signed up. Its effectiveness in creating content is, of course, raising questions about the future of content creators!

Some of the most popular examples of Generative AI are images and chatbots that have helped the market grow by leaps and bounds. The generative AI market is estimated at USD 10.3 billion in 2022, and will grow at a CAGR of 32.2% to touch $53.9 billion by 2028.

Despite the hype and excitement around it, there are several unknown factors that pose a risk when using generative AI. For example, governance and ethics are some of the areas that need to be worked on due to the potential misuse of technology.

Check out this informative blog on deep fakes: Your voice or face can be changed or altered.

Decoding the secrets of Generative AI: Unveiling the learning process 

Generative AI leverages a powerful technique called deep learning to unveil the intricate patterns hidden within vast data troves. This enables it to synthesize novel data that emulates human-crafted creations. The core of this process lies in artificial neural networks (ANNs) – complex algorithms inspired by the human brain’s structure and learning capabilities. 

Imagine training a generative AI model on a massive dataset of musical compositions. Through deep learning, the ANN within the model meticulously analyzes the data, identifying recurring patterns in melody, rhythm, and harmony. Armed with this knowledge, the model can then extrapolate and generate entirely new musical pieces that adhere to the learned patterns, mimicking the style and characteristics of the training data. This iterative process of learning and generating refines the model’s abilities over time, leading to increasingly sophisticated and human-like outputs. 

In essence, generative AI models are not simply copying existing data but learning the underlying rules and principles governing the data. This empowers them to combine and manipulate these elements creatively, resulting in novel and innovative creations. As these models accumulate data and experience through the generation process, their outputs become increasingly realistic and nuanced, blurring the lines between human and machine-generated content.

Evolution of Machine Learning & Artificial Intelligence

From the classical statistical techniques of the 18th century for small data sets, to developing predictive models, machine learning has come a long way. Today, machine learning tools are used to classify large volumes of complex data and to identify patterns. These data patterns are then used to develop models to create artificial intelligence solutions.

Initially, the learning models are trained by humans. This process is called supervised learning. Soon after, they evolve towards self-supervised learning, wherein they learn by themselves using predictive models. In other words, they become capable of imitating human intelligence, thus contributing to process automation and performing repetitive tasks.

Generative AI is one step ahead in this process, wherein machine learning algorithms can generate the image or textual description of anything based on the key terms. This is done by training the algorithms using massive volumes of calibrated combinations of data. For example, 45 terabytes of text data were used to train GPT-3, to make the AI tool seem ‘creative’ when generating responses.

The models also use random elements, thereby producing different outputs from the same input request, making it even more realistic. Bing Chat, Microsoft’s AI chatbot, for instance, became philosophical when a journalist fed it a series of questions and expressed a desire to have thoughts and feelings like a human!

Microsoft later clarified that when asked 15 or more questions, Bing could become unpredictable and inaccurate.

Here’s a glimpse into some of the leading generative AI tools available today: 

ChatGPT: This OpenAI marvel is an AI language model capable of answering your questions and generating human-like responses based on text prompts. 

DALL-E 3: Another OpenAI creation, DALL-E 3, possesses the remarkable ability to craft images and artwork from textual descriptions. 

Google Gemini: Formerly known as Bard, this AI chatbot from Google is a direct competitor to ChatGPT. It leverages the PaLM large language model to answer questions and generate text based on your prompts. 

Claude 2.1: Developed by Anthropic, Claude boasts a 200,000 token context window, allowing it to process and handle more data compared to its counterparts, as claimed by its creators. 

Midjourney: This AI model, created by Midjourney Inc., interprets text prompts and transforms them into captivating images and artwork, similar to DALL-E’s capabilities. 

Sora: This model creates realistic and imaginative scenes from text instructions. It can generate videos up to a minute long while maintaining visual quality and adherence to the user’s prompt. 

GitHub Copilot: This AI-powered tool assists programmers by suggesting code completions within various development environments, streamlining the coding process. 

Llama 2: Meta’s open-source large language model, Llama 2, empowers developers to create sophisticated conversational AI models for chatbots and virtual assistants, rivalling the capabilities of GPT-4. 

Grok: Founded by Elon Musk after his departure from OpenAI, Grok is a new venture in the generative AI space. Its first model, Grok, known for its irreverent nature, was released in November 2023. 

These are just a few examples of the diverse and rapidly evolving landscape of generative AI. As the technology progresses, we can expect even more innovative and powerful tools to emerge, further blurring the lines between human and machine creativity. 

Underlying Technology

There are three techniques used in generative AI.

Generative Adversarial Networks (GANs)

GANs are powerful algorithms that have enabled AI to be creative by making two algorithms compete to achieve equilibrium.

Variational Auto-Encoders (VAE)

To enable the generation of new data, the autoencoder regularizes the distribution of encodings during training to ensure good properties of latent space. The term “variational” is derived from the close relationship between regularization and variational inference methods in statistics.

Transformers

A deep learning model, transformers use a self-attention mechanism to weigh the importance of each part of the input data differentially and are also used in natural language processing (NLP) and computer vision (CV).

Prior to ChatGPT, the world had already seen OpenAI’s GPT-3 and Google’s BERT, though they were not as much of a sensation as ChatGPT has been. Training models of this scale need deep pockets.

Generative AI Use Cases

Content writing has been one of the primary areas where ChatGPT has seen much use. It can write on any topic within minutes by pulling in inputs from a variety of online sources. Based on feedback, it can finetune the content. It is useful for technical writing, writing marketing content, and the like.

Generating images such as high-resolution medical images is another area where it can be used. Artwork can be created using AI for unique works, which are becoming popular. By extension, designing can also benefit from AI inputs.

Generative AI can also be used for creating training videos that can be generated without the need for permission from real people. This can accelerate content creation and lower the cost of production. This idea can also be extended to creating advertisements or other audio, video, or textual content.

Code generation is another area where generative AI tools have proved to be faster and more effective. Gamification for improving responsiveness and adaptive experiences is another potential area of use.

Governance and Ethics

The other side of the Generative AI coin is deep fake technology. If used maliciously, it can create quite a few legal and identity-related challenges. It can be used to implicate somebody wrongly or frame someone unless there are checks and balances that can help prevent such malicious misuse.

It is also not free of errors, as the media website CNET discovered. The financial articles written using generative AI had many factual mistakes.

OpenAI has already announced GPT4 but tech leaders such as Elon Musk and Steve Wozniak have asked for a pause in developing AI technology at such a fast pace without proper checks and balances. It also needs security to catch up and appropriate safety controls to prevent phishing, social engineering, and th generation of malicious code.

There is a counter-argument to this too which suggests that rather than pausing the development, the focus should be on developing a consensus on the parameters concerning AI development. Identifying risk controls and mitigation will be more meaningful.

Indeed, risk mitigation strategies will play a critical role in ensuring the safe and effective use of generative AI for genuine needs. Selecting the right kind of input data to train the models, free of toxicity and bias, will be important. Instead of providing off-the-shelf generative AI models, businesses can use an API approach to deliver containerized and specialized models. Customizing the data for specific purposes will also help improve control over the output. The involvement of human checks will continue to play an important role in ensuring the ethical use of generative AI models.

This is a promising technology that can simplify and improve several processes when used responsibly and with enough controls for risk management. It will be an interesting space to watch as new developments and use cases emerge.

FAQ’s

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THE RISE OF GENERATIVE AI

Generative AI has emerged as a groundbreaking field, enabling machines to create new content that mimics human creativity. This technology has been applied in various domains, including natural language processing, computer vision, and music composition. By training AI models on vast amounts of text data, they can learn patterns, generate coherent sentences, and even produce original pieces of writing. However, as the field progresses, it confronts a roadblock: the scarcity of quality training data.

THE WARNING FROM UC BERKELEY

Recently, a UC Berkeley professor raised concerns about generative AI tools “running out of text” to train on. The explosion of AI applications has consumed an enormous amount of text, leaving fewer untapped resources for training future models. The professor cautioned that if this trend continues, AI systems may reach a point where they struggle to generate high-quality outputs or, worse, produce biased and misleading content.

IMPLICATIONS FOR GENERATIVE AI

The shortage of training text could have significant consequences for the development of generative AI. First and foremost, it may limit the potential for further advancements in natural language processing. Generative models heavily rely on the availability of diverse and contextually rich text, which fuels their ability to understand and generate human-like content. Without a steady supply of quality training data, AI systems may face challenges in maintaining accuracy and coherence.

Moreover, the shortage of text data could perpetuate existing biases within AI models. Bias is an ongoing concern in AI development, as models trained on biased or incomplete data can inadvertently reinforce societal prejudices. With limited text resources, generative AI tools may be unable to overcome these biases effectively, resulting in outputs that reflect or amplify societal inequalities.

SOLUTIONS AND FUTURE DIRECTIONS

Addressing the challenge of running out of text requires a multi-pronged approach. First, it is crucial to invest in research and development to enhance text generation techniques that can make the most out of limited data. Techniques such as transfer learning, data augmentation, and domain adaptation can help models generalize from smaller datasets.

Another avenue is the responsible and ethical collection and curation of text data. Collaborative efforts involving academia, industry, and regulatory bodies can ensure the availability of diverse and representative datasets, mitigating the risk of bias and maintaining the quality of AI outputs. Open access initiatives can facilitate the sharing of high-quality data, fostering innovation while preserving privacy and intellectual property rights.

Furthermore, there is a need for continuous monitoring and evaluation of AI models to detect and mitigate biases and inaccuracies. Feedback loops involving human reviewers and automated systems can help identify problematic outputs and refine the training process.

FIVE INDUSTRY USE CASES FOR GENERATIVE AI

Generative AI presents itself with five compelling use cases across various industries. One of its primary applications is in exploring diverse designs for objects, facilitating the identification of the optimal or most suitable match. This not only expedites and enhances the design process across multiple fields but also possesses the potential to introduce innovative designs or objects that might otherwise elude human discovery.

The transformative influence of generative AI is notably evident in marketing and media domains. According to Gartner’s projections, the utilization of synthetically generated content in outbound marketing communications by prominent organizations is set to surge, reaching 30% by 2025—an impressive ascent from the mere 2% recorded in 2022. Looking further ahead, a significant milestone is forecasted for the film industry, with a blockbuster release expected in 2030 to feature a staggering 90% of its content generated by AI, encompassing everything from textual components to video elements. This leap is remarkable considering the complete absence of such AI-generated content in 2022.

The ongoing acceleration of AI innovations is spawning a myriad of use cases for generative AI, spanning diverse sectors. The subsequent enumeration delves into five prominent instances where generative AI is making its mark:

Source: Gartner

NOTHING TO WORRY

Organisations see generative AI as an accelerator rather than a disruptor, but why?

Image Source: Grandview research/industry-analysis/generative-ai-market-report

Generative AI has changed from being viewed as a possible disruptor to a vital accelerator for businesses across industries in the world of technology. Its capacity to boost creativity, expedite procedures, and expand human capacities is what is driving this shift. A time-consuming job like content production can now be sped up with AI-generated draughts, freeing up human content creators to concentrate on editing and adding their own distinctive touch.

Consider the healthcare sector, where Generative AI aids in drug discovery. It rapidly simulates and analyses vast chemical interactions, expediting the identification of potential compounds. This accelerates the research process, potentially leading to breakthrough medicines.

Additionally, in finance, AI algorithms analyze market trends swiftly, aiding traders in making informed decisions. This accelerates investment strategies, responding to market fluctuations in real-time.

Generative AI’s transformation from disruptor to accelerator is indicative of its capacity to collaborate with human expertise, offering a harmonious fusion that maximizes productivity and innovation.

Image Source: Grandview research/industry-analysis/generative-ai-market-report

AI BOARDROOM FOCUS

Generative AI has taken a prominent position on the agendas of boardrooms across industries, with its potential to revolutionize processes and drive growth. In the automotive sector, for example, leading companies allocate around 15% of their innovation budgets to AI-driven design and simulation, enabling them to accelerate vehicle development by up to 30%.

Retail giants also recognize Generative AI’s impact, dedicating approximately 10% of their operational budgets to AI-powered demand forecasting. This investment yields up to a 20% reduction in excess inventory and a significant boost in customer satisfaction through accurate stock availability.

Architectural firms and construction companies channel nearly 12% of their resources into AI-generated designs, expediting project timelines by up to 25% while ensuring energy-efficient and sustainable structures.

WRAPPING UP

The warning from the UC Berkeley professor serves as a reminder of the evolving challenges faced by generative AI. The scarcity of training text poses a threat to the future development of AI models, potentially hindering their ability to generate high-quality, unbiased content. By investing in research, responsible data collection, and rigorous evaluation processes, we can mitigate these challenges and ensure that generative AI continues to push the boundaries of human creativity while being mindful of ethical considerations. As the field progresses, it is essential to strike a balance between innovation and responsible AI development, fostering a future where AI and human ingenuity coexist harmoniously.

Despite the challenges highlighted by the UC Berkeley professor, the scope of generative AI remains incredibly promising. Industry leaders and researchers are actively engaged in finding innovative solutions to overcome the text scarcity issue. This determination is a testament to the enduring value that generative AI brings to various sectors, from content creation to scientific research.

As organizations forge ahead, it is evident that the positive trajectory of generative AI is unwavering. The collaboration between AI technologies and human intellect continues to yield groundbreaking results. By fostering an environment of responsible AI development, where ethical considerations are paramount, we can confidently navigate the evolving landscape. This harmonious synergy promises a future where generative AI amplifies human potential and drives innovation to unprecedented heights.

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A recent research paper on “Explainable AI (XAI)” suggested the methodologies for data visualization and data interpretation in AI models has attracted increasing attention from business heads. Now the global entrepreneurial community is exploring its vision for “responsible AI” that has been thoroughly tested for “ethical AI practices” that are explainable to its clients.

Interestingly, most of the AI applications used are black-box in nature. Artificial intelligence models that give you a result or make a judgement without explaining or providing evidence of their reasoning are referred to as “black boxes.“

Artificial intelligence models must be transparent and trustworthy.

Why should an investor trust the next successful cryptocurrency based on probabilistic machine learning analysis? Similar to this, why should a fight promoter trust a decision tree model flow chart that reveals the next pound-for-pound fighter on the UFC roster? And why, instead of relying on his knowledge and competence, should a medical professional believe in an AI-generated report that a patient has cancer that is terminal?

Here, transparency and explainability are required from the artificial intelligence models when tons of money are at stake and human lives are on the line.

What is Explainable AI (XAI)

A group of methods and approaches known as ‘Explainable Artificial Intelligence’ (XAI) makes it possible for human users to comprehend and accept the output and results generated by AI algorithms. A company must first build trust and confidence before implementing AI models. With the help of AI explainability, a business may approach AI development responsibly.

How to decode the Blackbox models

Local Interpretable Model-agnostic Explanations (LIME)

It is a tough task to explain a deep neural network or a complex ML model. The methods of using local approximations with a simple and understandable surrogate function can aid in explaining the predictions of complex models. 

LIME can be used to explain any black-box model without having access to the model’s internal workings, making it “model-agnostic.” LIME is also “interpretable” since it develops a straightforward, understandable model to simulate the behavior of the complex model in a particular area of the feature space. In applications including image classification, natural language processing, and recommendation systems, LIME is a common technique for giving AI explanations.

Prediction Difference Analysis (PDA)

Prediction Difference Analysis (PDA) is an approach for deep visualization of hidden layers in the deep neural network. With this analysis, we can understand how the units of the layers influence the nodes of the layers. PDA is conditional sampling, which leads to results that are more refined in the sense that they concentrate more around the object.

The layer propagation strategy and the SPRAY approach are two additional noteworthy and dependable methods that can find patterns in the data throughout the training of deep neural networks and have the capacity to present visual explanations like heatmaps.

These XAI techniques can give textual and visual explanations of the predictions made by AI systems.

XAI in Ethical Artificial Intelligence process

The ethical artificial intelligence approach lays forth the guiding ideals that ought to be adhered to during all stages of the development of AI systems.

Two such fundamental principles are transparency and accountability. The core philosophy behind Explainable Artificial Intelligence (XAI) is to build AI systems that are accountable for their decisions and show transparency in their decision-making abilities.

Check out this article: Generative AI: Scope, Risks, and Future Potential.

Few use cases of explainable AI with ethical AI practices in well-known sectors

Artificial intelligence is being used in a variety of applications, ranging from robotics in large industrial businesses to humanness operations in the defense sector. All these industries demand the design and development of AI systems that are transparent, accountable and trustworthy.

Pharma Industry

Drug development is costly, time-consuming, and has a low likelihood of receiving FDA clearance. Explainable AI (XAI) supports the pharmaceutical industry’s “drug reposting methodologies” by describing the behavioral patterns of patient clusters generated by AI algorithms.

HealthCare Industry

When XAI is used in healthcare, the “principles of biomedical ethics” must be followed from early disease detection to smart disease detection. Metrics that are used while evaluating the model’s explainability are critical, along with decision-making metrics.

Insurance

XAI principles can be used in insurance pricing, claims administration, and underwriting procedures. The main XAI approach employed within the insurance value chain is identified as simplification techniques known as “information distillation’ and ‘rule extraction’. 

What makes XAI challenging?

There are several factors that make the incorporation of XAI challenging for the ethical AI process, including:

Trade-off between accuracy and interpretability

more accurate models may occasionally be less interpretable, whereas more interpretable models may occasionally trade off some accuracy. Finding a balance between accuracy and interpretability can be challenging.

Lack of transparency

Several AI models are “black boxes,” offering little to no information on how they make decisions. The decision-making process of the model may be challenging to comprehend and justify due to this lack of openness.

Legal and ethical concerns

It may be necessary to defend an AI model’s decision in some situations, such as those involving loan or employment applications.  If the model cannot be communicated, fulfilling legal or ethical obligations may be difficult.

By now, it is understood that the tunes on Explainable AI have the hardest riffs to play.

Conclusion

In conclusion, the development of Explainable Artificial Intelligence (XAI) is crucial for ensuring that it adheres to critical ethical AI fundamentals like trust, reliability, and accountability.

With the increasing use of AI in various fields, such as healthcare, finance, and transportation, explainability becomes even more critical to ensure ethical, legal, and social implications. While the development of explainable AI poses many challenges, it is an active area of research and development in the AI community. As we continue to advance AI technologies, we must also prioritize the development of explainable AI to ensure that AI serves us in the best possible ways.

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