AI Blog Series
Future directions of AI
Introduction
As artificial intelligence continues to evolve and deepen its
impact, there is a growing discussion about the future
directions of AI. AI has already proven to be a powerful tool
for businesses, so what can we expect from AI in the coming
years?
Tim Davies
5 min read
Predictive AI
Predictive AI is probably the type of AI most people are
familiar with. It involves AI that uses historical data to
anticipate future outcomes. Predictive AI typically uses a
form of machine learning, which means that the system can
learn from previous outcomes and make predictions with
increasing accuracy. This type of AI represents the clearest
form of business utility as it can enable sophisticated
decisions based on a prediction of an element such as
customer behaviour or personal data.
As we look to the future the ability to predict outcomes is limited by 4 factors. Availability of data on historic performance, Quality of data and labelling, bias and legislation. Where we have quality data that is well defined almost anything can be predicted (based on historic performance), but any biases within the data set will be carried into the predictions. Legislation will in certain regions limit the prediction of data associated with protected characteristics (think race, religion etc).
Predictive AI will make a huge impact on business and personal life, but our visibility of it will be limited. In the same way, as we’ve barely noticed Google Maps ETA accuracy improving over time, systems will just become more accurate at predicting and that will result in smarter applications, easy-to-make business decisions and everyday things that just work better!
As we look to the future the ability to predict outcomes is limited by 4 factors. Availability of data on historic performance, Quality of data and labelling, bias and legislation. Where we have quality data that is well defined almost anything can be predicted (based on historic performance), but any biases within the data set will be carried into the predictions. Legislation will in certain regions limit the prediction of data associated with protected characteristics (think race, religion etc).
Predictive AI will make a huge impact on business and personal life, but our visibility of it will be limited. In the same way, as we’ve barely noticed Google Maps ETA accuracy improving over time, systems will just become more accurate at predicting and that will result in smarter applications, easy-to-make business decisions and everyday things that just work better!
Generative AI
Generative AI is a form of AI that creates new data,
typically based on existing data. This type of AI often uses
deep learning and neural networks to generate data such as
images, videos, audio, or language. Generative AI is
becoming increasingly popular in entertainment and
marketing, as it can create realistic data that appears to
come from a human. Generative AI can also be used for jobs
such as data augmentation, which is the process of
increasing the amount of available data.
One major application of generative AI is in image processing. Generative AI models can be used to generate photorealistic images of objects and scenes based on given inputs. This can be used for applications such as applying special effects or textures to real-life images, or for creating new images to be used in computer games and animation.
Another application is language generation. Generative AI models can be used to create natural language text that accurately reflects the style of a specific author or speaker. This has applications in content generation, such as creating video scripts, or in digital marketing, such as generating customer emails.
As we look to the future almost generative AI has the potential to have a huge impact on our lives and jobs. The creative arts have long been seen as challenging for AI, but the last 2 years have proved that this understanding was flawed.
Generative AI will enable anyone to write anything. Images, videos, music, and presentations will be produced from a simple prompt. It’s likely in a few years you will watch a film where the script, animation, voice artists and soundtrack were all produced automatically.
One major application of generative AI is in image processing. Generative AI models can be used to generate photorealistic images of objects and scenes based on given inputs. This can be used for applications such as applying special effects or textures to real-life images, or for creating new images to be used in computer games and animation.
Another application is language generation. Generative AI models can be used to create natural language text that accurately reflects the style of a specific author or speaker. This has applications in content generation, such as creating video scripts, or in digital marketing, such as generating customer emails.
As we look to the future almost generative AI has the potential to have a huge impact on our lives and jobs. The creative arts have long been seen as challenging for AI, but the last 2 years have proved that this understanding was flawed.
Generative AI will enable anyone to write anything. Images, videos, music, and presentations will be produced from a simple prompt. It’s likely in a few years you will watch a film where the script, animation, voice artists and soundtrack were all produced automatically.
Inventive AI
Inventive AI is the application of artificial intelligence
(AI) to the creative process of inventing. This sort of AI
requires more advanced and sophisticated algorithms and is
typically not seen in the commercial space yet. The
possibilities for inventive AI are truly endless and are
likely to be explored more with greater investments in the
space.
The current state of inventive AI is rapidly evolving. One of the key drivers of this evolution is the growth of big data. Much of the data needed to produce inventive AI output is available, but it’s only as good as the methods used to analyze it. In recent years, methods such as deep learning and neural networks have become increasingly popular and are playing a huge role in driving the evolution of inventive AI. Currently, inventive AI is being used in areas such as computer-aided engineering, drug discovery and diagnostics, and marketing.
The potential of inventive AI is enormous. In particular, the ability of AI to analyze and synthesize vast amounts of data to generate new and innovative ideas and solutions could be a game-changer for many industries. As AI systems become increasingly sophisticated, the potential for inventive AI to truly reinvent the way we think about and approach problem-solving is huge.
Expect breakthroughs in Cancer treatments, Gene-based therapies, Materials Science, Nanotechnology and many, many more.
The current state of inventive AI is rapidly evolving. One of the key drivers of this evolution is the growth of big data. Much of the data needed to produce inventive AI output is available, but it’s only as good as the methods used to analyze it. In recent years, methods such as deep learning and neural networks have become increasingly popular and are playing a huge role in driving the evolution of inventive AI. Currently, inventive AI is being used in areas such as computer-aided engineering, drug discovery and diagnostics, and marketing.
The potential of inventive AI is enormous. In particular, the ability of AI to analyze and synthesize vast amounts of data to generate new and innovative ideas and solutions could be a game-changer for many industries. As AI systems become increasingly sophisticated, the potential for inventive AI to truly reinvent the way we think about and approach problem-solving is huge.
Expect breakthroughs in Cancer treatments, Gene-based therapies, Materials Science, Nanotechnology and many, many more.
Artificial General Intelligence (AGI)
The final area of AI development to consider is Artificial
General Intelligence (AGI). AGI refers to machines that can
match or exceed the cognitive capabilities of humans. This
type of AI is still in its early stages of development and
is considered to be the “holy grail” of AI. AGI is often
seen as the ultimate goal of AI research and will likely
require massive leaps and bounds in both hardware and
software capability.
AGI is being pursued by researchers in both academia and industry, using a variety of approaches, such as deep learning, evolutionary algorithms, and other specialized techniques. Research into AGI is generally focused on developing systems that can reason and learn like humans, that can interact with their environment more dynamically, and that can understand and use language across a wide range of applications. Such systems would be capable of autonomous problem-solving, making decisions that are appropriate to the task and situation, and developing an understanding of the world that allows it to recognize objects and relationships (ontology).
The potential for AGI is often discussed with much enthusiasm, yet its realization is still far off. There are several reasons for this. First, the range and depth of experiences necessary for a machine to exhibit human-level intelligence erases the clear boundaries between AI research and cognitive science. This makes it extremely difficult to precisely define an AGI-capable system, to identify useful performance metrics, and to develop reliable evaluation mechanisms.
Second, current AI research has largely been limited by the huge datasets and computing power required for AGI-relevant experiments. Although massive datasets might be accessible to large companies, most academic and small business initiatives are limited to smaller datasets. This furthers the challenge of reliably evaluating machine performance.
Third, there may be fundamental problems in building a machine that can reason in arbitrary ways, assess information in new and unpredictable situations, and respond promptly and appropriately to its environment. These problems are inherently difficult and will require in-depth research and experimentation to solve.
Finally, AGI research is hindered by limited resources and a lack of broad consensus on how to go about developing such a system. Researchers in the field are only beginning to understand the variety of discrete tasks it will take to achieve an AGI-capable system, and any significant advances will involve significant amounts of funding and interdisciplinary collaboration.
Despite the obstacles, AGI is a practical and attainable goal that researchers are excited to explore. As computing power and available data continue to increase and researchers develop increasingly powerful methods for extracting intelligence from large datasets, the project of creating a human-level general intelligence becomes ever more realistic. At present, the field of AI is in an exciting and dynamic phase, and it is certain that continued progress in AGI and other forms of AI will revolutionize the way we interact with the world.
AGI is being pursued by researchers in both academia and industry, using a variety of approaches, such as deep learning, evolutionary algorithms, and other specialized techniques. Research into AGI is generally focused on developing systems that can reason and learn like humans, that can interact with their environment more dynamically, and that can understand and use language across a wide range of applications. Such systems would be capable of autonomous problem-solving, making decisions that are appropriate to the task and situation, and developing an understanding of the world that allows it to recognize objects and relationships (ontology).
The potential for AGI is often discussed with much enthusiasm, yet its realization is still far off. There are several reasons for this. First, the range and depth of experiences necessary for a machine to exhibit human-level intelligence erases the clear boundaries between AI research and cognitive science. This makes it extremely difficult to precisely define an AGI-capable system, to identify useful performance metrics, and to develop reliable evaluation mechanisms.
Second, current AI research has largely been limited by the huge datasets and computing power required for AGI-relevant experiments. Although massive datasets might be accessible to large companies, most academic and small business initiatives are limited to smaller datasets. This furthers the challenge of reliably evaluating machine performance.
Third, there may be fundamental problems in building a machine that can reason in arbitrary ways, assess information in new and unpredictable situations, and respond promptly and appropriately to its environment. These problems are inherently difficult and will require in-depth research and experimentation to solve.
Finally, AGI research is hindered by limited resources and a lack of broad consensus on how to go about developing such a system. Researchers in the field are only beginning to understand the variety of discrete tasks it will take to achieve an AGI-capable system, and any significant advances will involve significant amounts of funding and interdisciplinary collaboration.
Despite the obstacles, AGI is a practical and attainable goal that researchers are excited to explore. As computing power and available data continue to increase and researchers develop increasingly powerful methods for extracting intelligence from large datasets, the project of creating a human-level general intelligence becomes ever more realistic. At present, the field of AI is in an exciting and dynamic phase, and it is certain that continued progress in AGI and other forms of AI will revolutionize the way we interact with the world.
Conclusion
As AI continues to evolve, and potential applications become
more sophisticated, there is no limit to the potential of AI.
Predictive, generative, inventive, and AGI technologies have
the potential to revolutionize the way businesses operate and
revolutionize the lives of people around the world. It will be
interesting to see what advancements emerge in the coming
years and how these technologies become leveraged in the
commercial space.
Tim Davies