The specter of AI domination has haunted us since "The Terminator" (or perhaps even earlier). The evolution of AI goes back to the 1950s. Many of you may now know about its past, but what about its future?
Recent advancements in generative AI, including the development of Generative Adversarial Networks and transformers, have spurred a wave of innovation. These technologies work behind the scenes to help today’s model generate content on par with humans.
In the year 2022, ChatGPT took the internet by storm. It’s been more than a year since the ChatGPT launched and showed the potential of generative AI in business. Since then, business leaders have been busy finding use cases of generative AI in business and testing this technology across different operations.
Basic generative AI modals like ChatGPT and Bard are becoming more powerful and transforming into multimodal tools. The development of smaller LLMs is on the rise. Small businesses are rushing to integrate AI capabilities into their existing digital infrastructure.
Large organizations are moving beyond general-purpose AI applications by developing custom AI solutions. Gartner predicts that by 2027, the adoption of tailored GenAI models within large enterprises will grow from 1% to 50%.
Many business leaders who are unaware of this technology have begun experimenting with it for smaller use cases. This indicates that this technology will take center stage in the coming years, but with this power comes some ethical concerns that we cannot avoid.
In light of these developments, it is crucial to understand the future of generative AI. To truly understand it, we need to explore its ethical concerns, implementation challenges, limitations, and anticipated advancements.
It is not only affecting work related to facts and numbers but creativity and the artist's spirit as well, this is where AI-driven creativity comes into question. So, how will AI affect our daily lives? Should we run from it or adopt it? Questions and opinions are endless. Whatever the outcome is, it is definitely affecting our lives in one way or another. Let’s fast forward and see what the future holds for us and our friend AI.
What is Generative AI Technology?
Generative AI is a technology that generates various kinds of content. The AI models can interpret human language and respond accordingly.
creates content, including images, text, audio, video, and other types of data. The AI models run on generative AI and can interpret human language, take text, video, audio, and other types of data, and provide results. They can generate original content.
Here are the most popular applications of generative AI:
Marketing
You have a lot of customer data; generative AI can help. This technology excels at analyzing a massive amount of data. In your case, it could be customer data and preferences. AI can process this data at a blazing-fast speed and in record time, helping you create targeted marketing messages.
You can leverage generative AI for content creation as it can write compelling product descriptions, ad copy, social media content, and variations to test and optimize content and website landing pages for maximum conversion rates.
Note Taking
Generative AI can transcribe audio or video recordings of meetings and then summarize the key points, action items, and decisions taken.
Video generation
Tasks such as video editing and creating special effects are now much easier with the help of AI. The way AI technology is evolving, it will likely be able to generate entire videos from scratch, based on a script or storyboard. This could revolutionize the way videos are produced, making it faster and more affordable.
Future of Generative AI Technology
1. Advancement of Multimodal AI
Multimodal AI is an ML (machine learning) Model that can process and understand information from various sources, not just one. These sources can be text, audio, images, or videos. The purpose of Multimodal AI is to enable machines to interpret and respond to information in a way that mimics human understanding across different channels.
Initially, early generative AI models such as ChatGPT were categorized as unimodal—they could only process one type of data input and generate outputs of the same type. Gradually, Multimodal AI is advancing. Updated versions of ChatGPT and Gemini now have the ability to process and generate outputs from images as well. Though still in the early stages, these developments are Advancing us toward AGI (Artificial General Intelligence).
Multimodal learning is unleashing new possibilities for AI By training on various data types (text, images, audio), AI models can now understand and respond using multiple formats. This means they can take in text and images, and generate outputs that combine text and visuals. Multimodal learning will increase the capabilities of applications of multimodal AI including:
Augmented Generative AI: Multimodal models like GPT-4 turbo, and Google Gemini, come with new possibilities that can improve user experience both at the input and output sides. Furthermore, Virtual assistants like Siri, Alexa, or Google Assistant will better understand voice commands and gestures.
Autonomous Vehicles: Self-driving cars depend significantly on multimodal AI. Equipped with multiple sensors capturing diverse data formats from their surroundings, these vehicles rely on multimodal learning to integrate and process this information efficiently, enabling them to make intelligent decisions in real-time.
However, Multimodal AI faces hurdles. The massive datasets needed, with their variety of formats (text, images, audio), pose challenges in storage, processing, and even data quality. Additionally, teaching AI nuance, like sarcasm in spoken language, and aligning data from different sources (e.g., ensuring a camera image aligns with a specific car's location data) require further development.
Smaller LLMs
LLMs (Large Language Models) contain gigantic amounts of parameters to make applications of generative AI more accurate and reliable. for example, OpenAI added more and more parameters to their newer versions of ChatGPT. It started with GPT-2 with 1.2 billion and took a massive leap for GPT-3 with 175 billion and as for GPT-4, it is rumored to be more than 1 trillion.
These large language models require massive funds and server space to train and maintain energy-hungry models, which only some giant companies can do like OpenAI and Google or Microsoft. But many recent findings say otherwise.
For example, DeepMind's Chinchilla, an ensemble of large language models with 12 billion parameters, achieved comparable accuracy to GPT-3.5 on specific NLP datasets, despite GPT-3.5 having ten times more parameters. A research paper by Deepmind showed that training smaller models on more data gives better results. The “Quality over Quantity” approach has started to be adopted in the Generative AI technology world.
These smaller models will not only increase performance benchmarks but also help save energy. Sam Altman CEO of Open AI told the audience at an MIT event “I think we're at the end of the era where it's going to be these, like, giant, giant models,”
3. Impact on different industries
As AI becomes more accurate, efficient, and reliable businesses and individuals may become irresistible from implementing its use in their work. Different industries will start to adopt it as a tool to enhance their work productivity, cost efficiency, and time consumption.
Let’s look at how different industries will be impacted by the future gen AI.
Healthcare
Generative AI will accelerate diagnosis for the patient as it will analyze medical data (like scans and test results) to identify potential diseases much earlier, potentially saving lives. AI analyzes patient medical history and genetic data to recommend personalized medications and therapies, leading to more effective treatment. AI can analyze vast datasets to accelerate the discovery of new drugs and even predict potential side effects, ensuring safety for human consumption.
Finance
Generative AI presents a paradigm shift in data analysis, particularly within the financial sector. Its ability to process massive datasets in mere minutes promises to streamline cumbersome numerical tasks. Financial institutions will leverage this technology to generate comprehensive reports, potentially identifying lucrative investment opportunities that may have been previously overlooked. Furthermore, the implementation of AI chatbots within banks signifies a move towards enhanced customer service. These chatbots can efficiently handle account inquiries, and loan applications, and even offer basic financial planning assistance.
Education
AI is set to change education by offering personalized learning experiences tailored to individual student needs. Through advanced algorithms, AI will analyze students' learning patterns and preferences, allowing for adaptive learning platforms that adjust content and pace accordingly. Virtual tutors powered by AI will provide real-time feedback and support, enhancing traditional teaching methods.
Ethical Concerns
As with any powerful tool, AI comes with a double-edged sword. While it promises to revolutionize work, making tasks faster and less time-consuming, concerns about its responsible use are mounting. Threats such as deepfakes, which blur the line between reality and fabrication can be used to create life-like content from images to videos such power can be used to spread misinformation and manipulate the public.
Additionally, using AI for content creation can lead to some copyrighted legal battles, especially in the music industry where AI creates music that closely resembles the copyrighted music of the original artist.
Because of these Ethical concerns ongoing dialogue has been taken into account by the government. The current opacity of some AI models raises concerns about accountability and bias. Future AI development will prioritize explainability, allowing humans to understand the reasoning behind AI decisions. AI algorithms can perpetuate societal biases present in
their training data. The future prioritizes identifying and reducing bias in AI for fair treatment. Regulations will be built requiring AI systems to provide clear explanations for their outputs, empowering users to trust and assess the fairness of AI-driven decisions.
Economic Potential
Generative AI has the potential to boost global GDP by trillions of dollars. Researchers at Mckinsey found that across 63 use cases suggest it could generate an annual value equivalent to $2.6 trillion to $4.4 trillion – eclipsing the entire GDP of the United Kingdom in 2021 ($3.1 trillion). This translates to a 15-40% boost in the overall impact of artificial intelligence.
In specific industries, Generative AI is projected to significantly enhance productivity. In retail and consumer packaged goods, it could boost annual revenues by 1.2% to 2.0%, equating to an additional $400 billion to $660 billion. Similarly, in banking, it is expected to increase productivity by 2.8% to 4.7%, adding $200 billion to $340 billion annually to industry revenues. Meanwhile, Generative AI could contribute from 2.6% to 4.5% of annual revenues in the pharmaceutical and medical-product sectors, translating from $60 billion to $110 billion annually.
Conclusion
Generative AI holds immense promise for the future across various industries, promising exceptional levels of productivity, efficiency, and innovation. As this technology continues to advance, it will reshape how businesses operate, offering personalized solutions, automating complex tasks, and unleashing better opportunities for growth and development.
Adopting Generative AI represents not just a technological advancement, but a more liberating creative expression towards art. While challenges remain, like ensuring responsible development and addressing potential biases, the vast potential benefits outweigh them. In making sure that Generative AI technology not replacing humans but enhancing human capabilities, we can use this cutting-edge technology for the betterment of society.
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