Despite the sea changes wrought by artificial intelligence (AI) over the past few years, most popular large language models (LLMs) to date have focused primarily on generating novel content (hence the term “generative AI). Yet AI’s real lasting power lies, not in its capacity to create, but rather, in its ability to identify patterns across vast data sets, draw insights, and even predict the future.
Simply put, this second wave of generative AI – which experts call “synthesis AI” (or synth AI) – is moving away from content creation and towards insight generation. In other words, generative AI involves creating new content from scratch, while synthesis AI involves combining existing content to create something new.
In the wake of generative AI’s rise to prominence, there is simply too much information on any given topic or technology for the human brain to process. Every single day billions of new market data points are published across millions of sources.
As you might expect, synthesis AI plays a large role in the work we do here at entrapeer. Trends in the technology sector shift, advance, and collapse at an increasingly rapid pace. So leveraging AI to determine which technologies have lasting power, and which are likely to fall by the wayside can mean the difference between folding, and series B funding.
In other words, synthesis AI is no longer just “nice to have” – it’s a must if startups, enterprises, and VCs want to remain relevant in this rapidly shifting innovation ecosystem. This blog explores the key differences between these two waves of AI and examines the distinct advantages of synthesis AI for innovation stakeholders seeking to redefine their workflows and accelerate innovation.
- Generative AI involves creating new content from scratch, while synthesis AI involves combining existing content to create something new.
- The volume of content published daily as a result of the generative AI revolution means that synthesis AI is no longer just “nice to have,” but a necessity to compete.
- Synth AI has several benefits, including the ability to quickly analyze and combine large amounts of data, the ability to extrapolate from existing data and predict trends, as well as the ability to automate certain tasks.
- While Synth AI has its limitations, it can still be a valuable tool for information synthesis in a variety of sectors – especially in the fast-paced startup ecosystem and in startup-enterprise partnerships.
Generative AI vs Synthesis AI
Based on all the hype around ChatGPT, you might think that generative AI is an entirely new technology. In fact, you can trace generative AI’s origins back to 1966 when MIT developed ELIZA, a chatbot that simulated talking to a therapist.
Of course, substantial progress has been made since those early days of conversational AI. Today’s market is flooded with new AI tools promising to save you countless hours of research, writing, coding, image sourcing, and so on. Yet, that barrage of unique content is only helpful if you know where and how to apply it. Content without strategy is just noise, and research without insights is just a sea of uncharted potential.
This is where synth AI comes in.
Differences Between Generative AI and Synthesis AI
While both generative AI and synthesis AI are based on machine learning systems, there are some key differences between these two types of AI systems.
Where generative AI is designed to generate new content based on a set of input data, synthesis AI is built to synthesize information from multiple sources and create fresh insights. Generative AI is often used for creative purposes, such as creating new music or art, while synthesis AI is often used for analytical purposes, such as analyzing large sets of data to identify patterns and trends. Some of the most popular synthesis AI systems include IBM Watson, which can analyze large sets of data to identify patterns and insights, and Google’s DeepMind, which can generate new insights based on multiple data sources.
Overall, both generative AI and synthesis AI are powerful tools that can be used for a variety of purposes, from developing creative content to analyzing technical documents. Depending on your needs, you may find that one type of AI system is more suitable for your needs than the other.
Benefits of Synth AI Explained
Synth AI has the potential to give companies of any size a competitive edge by rendering market research more accurate and actionable, enhancing productivity, and ultimately providing stakeholders with unique, data-driven insights that drive informed decision-making. Let’s take a closer look at each of these benefits:
One of the primary benefits of synth AI is that it can improve accuracy by synthesizing information from vast data sets and diverse sources. This can be particularly useful in decision-making processes, where having access to a wide range of relevant information can help us make more informed decisions. For example, entrapeer leverages synth AI (via our AI engine, entramind) to analyze market and investment data from thousands of sources to identify patterns and trends that may be difficult to detect using traditional methods.
Another benefit of Synth AI is that it can enhance productivity by automating repetitive tasks. For example, entrapeer’s instant research feature generates custom market research reports on a topic of your choosing within minutes. A process that formerly took months of human effort has been reduced to mere minutes, making this information more actionable and freeing up time for humans to focus on more complex tasks.
Informed Decision Making
Synth AI offers enterprises the power to harness unique, data-driven insights, opening up new business opportunities. For instance, entramind can extrapolate data and make predictions that go beyond mere repetitions of existing content, which proves invaluable in applications such as market research, startup scouting, and uncovering novel use cases.
Limitations of Synth AI
While Synth AI has many benefits, it also has some limitations and risks that should be taken into consideration.
Synth AI is limited in its ability to create new and original content. It can only generate content and make predictions based on the data it has been trained on. This means that it may not be able to create content that is truly unique or groundbreaking. But after all, that’s what human ingenuity is for – and why would we want to automate away our creativity?
Dependence on data
As stated, synth AI is dependent on data to generate content. This means that it may not be able to generate content in areas where there is a lack of data. For example, it may not be able to generate content in a language that has very little written data or in a field that has not been extensively studied.
Of course, there are ethical concerns surrounding the use of Synth AI. For example, it may be used to create fake news or misleading information. It may also be used to create deepfake videos or images that can be used to manipulate people.
Popular demand for greater regulation of this technology is warranted. But, so long as we approach AI-generated content with a dose of healthy skepticism, we’re unlikely to befall the fate of Marvel’s Kree and become the servants of some Supreme Intelligence any time soon…
Overall, while Synth AI has many benefits, it is important to be aware of its limitations and use it responsibly.
Use Cases: AI for Information Synthesis
As synth AI continues to evolve, it has become increasingly useful in the interpretation and extrapolation of data. With the ability to analyze large amounts of data quickly and accurately, AI has proven to be a valuable tool in corporate innovation, market research, startup scouting, investment decisions, and more.
In this section, we explore the role of AI for diverse stakeholders in this field, namely: startups, enterprises, VCs, and accelerators.
Benefits of Synthesis AI for Startups
Synthesis AI empowers startups with a competitive advantage in market research interpretation. By synthesizing data from diverse sources, startups attain a comprehensive understanding of emerging technologies and discover new markets. This capability provides startups with unique, data-driven insights, optimizing decision-making in strategic business development. It attracts potential investors and uncovers novel use cases, making synthesis AI an invaluable tool for startups navigating the dynamic innovation landscape.
Benefits of Synthesis AI for Enterprises
Enterprises leverage synthesis AI to enhance decision-making and gain a competitive edge. By synthesizing information from millions of sources, the technology improves information accuracy, enabling more informed decisions. Automation of repetitive tasks, such as report generation, enhances productivity. Informed decision-making, powered by synthesis AI, opens new business opportunities, allowing enterprises to adapt to market changes, innovate efficiently, and remain at the forefront of their industries.
Benefits of Synthesis AI for Venture Capitalists (VCs)
Venture capitalists stand to benefit significantly from synthesis AI in their investment strategies. The technology enables VCs to analyze vast amounts of market and investment data from diverse sources, helping them make informed decisions on where to allocate their investments. Synthesis AI assists VCs in identifying emerging trends, potential risks, and lucrative opportunities in the market. By providing valuable insights and predictions, synthesis AI becomes an indispensable tool for VCs seeking to optimize their investment portfolios and stay ahead in the competitive landscape of venture capital.
Benefits of Synthesis AI for Accelerators
Accelerators can also leverage synthesis AI to accelerate investment decision-making. By analyzing data from various sources, synthesis AI identifies trends and patterns that contribute to improved business operations. This streamlined decision-making process helps accelerators validate investment opportunities in new products and services more efficiently. Additionally, synthesis AI aids in predicting ROI based on past proofs of concept, offering accelerators a data-driven approach to assessing the viability and potential success of ventures they support.
Future of Generative and Synthesis AI
As generative AI accelerates the pace of content production, synthesis AI emerges as a critical component to match this speed with our intelligent interpretation and application of this AI-generated data. This evolution holds promising prospects for the digital landscape, offering transformative possibilities in problem-solving, predictive analytics, personalized content, pattern recognition, and beyond.
The coexistence of generative and synthesis AI signifies a dynamic future, revolutionizing our interactions with the digital world and providing innovative solutions to intricate challenges. As artificial intelligence technology advances, particularly in deep learning and other realms, we anticipate continuous enhancements that will further elevate the capabilities of both generative and synthesis AI.
Entrapeer stands at the forefront of empowering organizations to harness the transformative potential of synthesis AI today. Our cutting-edge solutions enable you to develop data-driven go-to-market strategies, streamline startup scouting, accelerate innovation pipelines, and so much more. By leveraging entrapeer’s AI engine, you can unlock unprecedented insights, ensuring that your decision-making processes are not only informed but also agile in the face of evolving market dynamics.
Get your head start in the innovation race and revolutionize the way your organization interprets data by signing up for your free trial today.
Entrapeer is proud to be your strategic partner in navigating the complexities of the digital landscape, ensuring you are well-equipped to thrive in the era of synthesis AI.
Frequently Asked Questions
What are the two main types of AI models?
There are two main types of AI models: generative AI and discriminative AI. Generative AI models learn the underlying patterns in a set of data and then generate new data that mirrors the training set. Discriminative AI models, on the other hand, learn to distinguish between different classes of data.
What is the difference between generative AI and AI?
Generative AI is a type of AI that can learn from existing artifacts to generate new, realistic artifacts that reflect the characteristics of the training data but don’t repeat it. It can produce a variety of novel content, such as images, video, music, speech, text, software code, and product designs. AI, on the other hand, is a broad term that encompasses all types of artificial intelligence, including generative AI.
What benefits does generative AI offer?
Generative AI offers several benefits, including the ability to create new content that is similar to existing content, but not identical. This can be useful in a variety of applications, such as art and design, entertainment, and advertising. Generative AI can also help businesses automate certain tasks, such as content creation, which can save time and money.
How does synthesis AI work?
Synthesis AI, also known as Synth AI, is a type of AI that is designed to help users synthesize information from a variety of sources. Synth AI works by analyzing data from multiple sources, identifying patterns and relationships, and then presenting the information in a way that is easy to understand and use.
What are the benefits of synthesis AI?
Synthesis AI offers several benefits, including the ability to quickly and easily synthesize information from a variety of sources. This can be useful in a variety of applications, such as research, data analysis, and decision-making. Synth AI can also help businesses automate certain tasks, such as data analysis, which can save time and money.
What are some commonly used applications of generative AI?
Generative AI has several commonly used applications, including art and design, entertainment, and advertising. For example, generative AI can help artists and designers create new works by generating new ideas and concepts. It can also be used to create new video games, movies, and TV shows. In advertising, generative AI can create new advertisements based on existing ones, making it easier for companies to reach new audiences.