Artificial Intelligence (AI) has become an integral part of modern business strategy, with various models available to meet different needs. Among the most prominent types are discriminative and generative models, each offering unique advantages and applications. Discriminative AI is designed to classify input data into predefined categories, making it ideal for tasks like image recognition and sentiment analysis. In contrast, generative AI focuses on creating new data, which opens up possibilities for content generation, design innovation, and more. Understanding the fundamental differences between these two forms of AI is crucial for organisations looking to harness their power effectively.&nbsp;

Discriminative AI focuses on classifying input data into specific categories. It excels in tasks like image recognition, spam detection, and sentiment analysis. These models learn the boundaries between classes and are typically more efficient for tasks requiring accurate predictions based on existing data.&nbsp;

Generative AI, on the other hand, creates new data based on the patterns it has learned from existing datasets. This model can generate text, images, or even music. It’s useful for content creation, design, and simulations, where innovation and creativity are key.&nbsp;

Now, let's explore how to choose the right AI model for your specific needs. 
&nbsp; 
Define Your Objectives - Start by clearly defining the goals you want to achieve with AI. Are you looking to enhance customer experience, automate processes, or innovate your product offerings? Your objectives will guide the type of AI that best suits your needs.&nbsp;

Assess Data Availability - Consider the data you have at your disposal. Discriminative models require labelled data for training, while generative models can often work with unlabelled data. Evaluate whether you have sufficient and high-quality data to support your AI initiatives.&nbsp;

Evaluate Performance Needs - Different applications may require varying levels of accuracy and speed. Discriminative models might be preferred for tasks that demand high accuracy in classification, whereas generative models can be better for creative tasks where the output is not strictly defined.&nbsp;

Consider Resource Constraints - Implementing AI models can be resource-intensive. Assess your budget, computational resources, and personnel expertise. Generative models, particularly those that rely on deep learning, may require more computational power and time to train.&nbsp;

Be Cost-Effective - Before committing to developing your own models, consider trying cloud-based generative AI solutions as a trial. This approach allows you to explore their capabilities without the upfront investment of building your own. Once you have a clearer understanding of your needs and the benefits, you can decide if a custom solution is warranted. Also, run pilot programs with both types of AI models. This allows you to test their effectiveness in real-world scenarios and refine your approach based on the results.&nbsp;

Stay Adaptable - AI technology is rapidly evolving. Keep an eye on emerging trends and advancements in both discriminative and generative AI. Your organization should remain flexible to adapt and integrate new solutions as they become available.&nbsp;

By thoughtfully considering these factors, companies can make informed decisions about the AI model that aligns best with their strategic goals, resources, and unique challenges. The right AI can unlock new opportunities and drive significant improvements in operational efficiency and customer engagement.&nbsp;

Artificial Intelligence (AI) has become an integral part of modern business strategy, with various models available to meet different needs. Among the most prominent types are discriminative and generative models, each offering unique advantages and applications.

Artificial Intelligence (AI) has become an integral part of modern business strategy, with various models available to meet different needs. Among the most prominent types are discriminative and generative models, each offering unique advantages and applications. Discriminative AI is designed to classify input data into predefined categories, making it ideal for tasks like image recognition and sentiment analysis. In contrast, generative AI focuses on creating new data, which opens up possibilities for content generation, design innovation, and more. Understanding the fundamental differences between these two forms of AI is crucial for organisations looking to harness their power effectively. Discriminative AI focuses on classifying input data into specific categories. It excels in tasks like image recognition, spam detection, and sentiment analysis. These models learn the boundaries between classes and are typically more efficient for tasks requiring accurate predictions based on existing data. Generative AI, on the other hand, creates new data based on the patterns it has learned from existing datasets. This model can generate text, images, or even music. It’s useful for content creation, design, and simulations, where innovation and creativity are key. Now, let's explore how to choose the right AI model for your specific needs. Define Your Objectives - Start by clearly defining the goals you want to achieve with AI. Are you looking to enhance customer experience, automate processes, or innovate your product offerings? Your objectives will guide the type of AI that best suits your needs. Assess Data Availability - Consider the data you have at your disposal. Discriminative models require labelled data for training, while generative models can often work with unlabelled data. Evaluate whether you have sufficient and high-quality data to support your AI initiatives. Evaluate Performance Needs - Different applications may require varying levels of accuracy and speed. Discriminative models might be preferred for tasks that demand high accuracy in classification, whereas generative models can be better for creative tasks where the output is not strictly defined. Consider Resource Constraints - Implementing AI models can be resource-intensive. Assess your budget, computational resources, and personnel expertise. Generative models, particularly those that rely on deep learning, may require more computational power and time to train. Be Cost-Effective - Before committing to developing your own models, consider trying cloud-based generative AI solutions as a trial. This approach allows you to explore their capabilities without the upfront investment of building your own. Once you have a clearer understanding of your needs and the benefits, you can decide if a custom solution is warranted. Also, run pilot programs with both types of AI models. This allows you to test their effectiveness in real-world scenarios and refine your approach based on the results. Stay Adaptable - AI technology is rapidly evolving. Keep an eye on emerging trends and advancements in both discriminative and generative AI. Your organization should remain flexible to adapt and integrate new solutions as they become available. By thoughtfully considering these factors, companies can make informed decisions about the AI model that aligns best with their strategic goals, resources, and unique challenges. The right AI can unlock new opportunities and drive significant improvements in operational efficiency and customer engagement.

Discriminative or Generative: Know the AI that you need

At least two prominent economists, one of them a Nobel laureate, have warned against hyping up artificial intelligence, just as Microsoft-backed OpenAI, at the forefront of generative AI, was declared to be worth over $157 billion after a new funding round.

In recent interviews and articles, MIT economist Daron Acemoglu has criticised the hype around AI. Fellow economist and Nobel Laureate Paul Romer is also sceptical about any economic windfalls accruing from AI.

Acemoglu&nbsp;warned that the hype could lead to wasted investments and unkept promises and even a market crash akin to the crypto crash.&nbsp;

Watch: AI-boom leaves mega Indian software stocks in the dust 
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In spite of the exuberance, the Turkish-American economist feels that only five per cent of global jobs will be impacted by AI in next decade.&nbsp;

Such a minial disruption is not going to lead to any economic revolution, he argued.&nbsp;

Possible future scenarios for AI include some successful apps surviving and the remaining hype cooling off, and a volatile cycle of excitement and disillusionment, he predicted.&nbsp;

He said that companies that replace humans with AI at the workplace will only end up re-hiring people when they realise AI's limitations.&nbsp;

While apps like ChatGPT, the large language model from OpenAI, are appreciated, Acemoglu said AI would not be able to replace human workers in jobs needing nuanced judgement.&nbsp;

<a href="https://www.wionews.com/business-economy/openai-secures-66-billion-in-funding-now-valued-at-157-billion-764082">Also read: OpenAI secures $6.6 billion in funding, now valued at $157 Billion</a>

"You need highly reliable information or the ability of these models to faithfully implement certain steps that previously workers were doing. They can do that in a few places with some human supervisory oversight like coding, but in most places they cannot. That's a reality check for where we are right now,” he said.

Studies that forecast significant economic growth from AI lack any empirical evidence, he pointed out, while adding that AI might eventually exacerbate inequality.

He advocated a more regulated approach to AI, as opposed to uncritical optimism about the technology.

He warned that the AI frenzy could eventually end up in a tech stocks crash in what he called an “AI spring followed by AI winter”.

"If you listen to tech industry leaders, business-sector forecasters, and much of the media, you may believe that recent advances in generative AI will soon bring extraordinary productivity benefits, revolutionising life as we know it. Yet neither economic theory nor the data support such exuberant forecasts," he said.

<a href="https://www.wionews.com/entertainment/hollywood/news-meta-launches-ai-chatbot-in-voices-of-judi-dench-john-cena-other-celebrities-762241">Also read: Meta launches AI chatbot in voices of Judi Dench, John Cena, other celebrities</a> 
 
In a recent podcast, Phil Romer, a professor at Boston College also echoed similar sentiments. The former chief economist of World Bank highlighted that some of the recent hyped-up technologies like autonomous vehicles have failed.

"I think people are buying a little bit too much of the hype and they’re losing perspective," he said. "I don’t think AI is actually the big revolution that we’re living through right now."&nbsp;

"The autonomous vehicles were supposed to be the killer application of AI, and it’s turning out to be a bust. There’s a reason Apple just canceled its car project," Romer added.

(With inputs from agencies)

At least two prominent economists, one of them a Nobel laureate, have warned against hyping up artificial intelligence, just as Microsoft-backed OpenAI, at the forefront of generative AI, was declared to be worth over $157 billion after a new funding round.

At least two prominent economists, one of them a Nobel laureate, have warned against hyping up artificial intelligence, just as Microsoft-backed OpenAI, at the forefront of generative AI, was declared to be worth over $157 billion after a new funding round. In recent interviews and articles, MIT economist Daron Acemoglu has criticised the hype around AI. Fellow economist and Nobel Laureate Paul Romer is also sceptical about any economic windfalls accruing from AI. Acemoglu warned that the hype could lead to wasted investments and unkept promises and even a market crash akin to the crypto crash. Watch: AI-boom leaves mega Indian software stocks in the dust × In spite of the exuberance, the Turkish-American economist feels that only five per cent of global jobs will be impacted by AI in next decade. Such a minial disruption is not going to lead to any economic revolution, he argued. Possible future scenarios for AI include some successful apps surviving and the remaining hype cooling off, and a volatile cycle of excitement and disillusionment, he predicted. He said that companies that replace humans with AI at the workplace will only end up re-hiring people when they realise AI's limitations. While apps like ChatGPT, the large language model from OpenAI, are appreciated, Acemoglu said AI would not be able to replace human workers in jobs needing nuanced judgement. Also read: OpenAI secures $6.6 billion in funding, now valued at $157 Billion "You need highly reliable information or the ability of these models to faithfully implement certain steps that previously workers were doing. They can do that in a few places with some human supervisory oversight like coding, but in most places they cannot. That's a reality check for where we are right now,” he said. Studies that forecast significant economic growth from AI lack any empirical evidence, he pointed out, while adding that AI might eventually exacerbate inequality. He advocated a more regulated approach to AI, as opposed to uncritical optimism about the technology. He warned that the AI frenzy could eventually end up in a tech stocks crash in what he called an “AI spring followed by AI winter”. "If you listen to tech industry leaders, business-sector forecasters, and much of the media, you may believe that recent advances in generative AI will soon bring extraordinary productivity benefits, revolutionising life as we know it. Yet neither economic theory nor the data support such exuberant forecasts," he said. Also read: Meta launches AI chatbot in voices of Judi Dench, John Cena, other celebrities In a recent podcast, Phil Romer, a professor at Boston College also echoed similar sentiments. The former chief economist of World Bank highlighted that some of the recent hyped-up technologies like autonomous vehicles have failed. "I think people are buying a little bit too much of the hype and they’re losing perspective," he said. "I don’t think AI is actually the big revolution that we’re living through right now." "The autonomous vehicles were supposed to be the killer application of AI, and it’s turning out to be a bust. There’s a reason Apple just canceled its car project," Romer added. (With inputs from agencies)

AI enthusiasm is a hype: Economists sound alarm on possible market crash like crypto

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Discriminative or Generative: Know the AI that you need

AI enthusiasm is a hype: Economists sound alarm on possible market crash like crypto