Here’s our 10 predictions & trends for 2023
1) Generative AI revolutionizes content creation and generation
2) Large language models will change the way of coding
3) AI will drive increased productivity across various professions
4) Future notebooks will move towards low-code data solutions
5) Growing importance of data visualization and storytelling
6) Data literacy as core competency for individuals and organizations
7) Efforts to develop humanoid robots will attract more attention
8) Web search will change due to rise of large language models
9) Availability of data to train large models will become a constraint
10) Data Science education will become more application-focused
Introduction
2022 was a significant year for the field of data, with the emergence of AI-generated content and the growth of low-code data tools and AI assistants. These advancements signal a paradigm shift, where data-powered tools and machine learning systems will radically transform workflows across various professions. In particular, the emergence of AI-generated content has the potential to revolutionize the way we create and consume content, from written articles to videos and audio. With the ability to generate high-quality content in a fraction of the time it would take a human, we will see a proliferation of AI-generated content in the coming years.
2023 is shaping up to be an inflection point for data and machine learning technologies, where the impact will be felt across organizations, individuals, and society as a whole. The skills agenda will be on full display, as skills transformation becomes the common thread that enables organizations and individuals to lead in this paradigm shift. With the rapid pace of technological change, it’s more important than ever for individuals and organizations to continuously develop their skills in order to stay relevant and competitive. This includes not just technical skills related to data and machine learning, but also soft skills such as critical thinking, problem-solving, and communication.
As we move into 2023, the field of data, data skills and machine learning is evolving rapidly and individuals and organizations that are able to keep up with these trends will be well-positioned to succeed in the data-driven economy.
Prediction 1: Generative AI revolutionizes content creation and generation
One of the most exciting areas of AI development in recent years has been the advancement of generative AI. This technology has the ability to create new content, such as images, music, and text, without any human input. This has the potential to revolutionize many industries, and in 2023, we can expect to see generative AI making a significant impact in the field of content creation and generation.
One of the most obvious areas where generative AI will have a big impact is in the world of digital media. With the ability to generate images, videos, and music, this technology will make it possible to create high-quality digital content at a fraction of the cost and time it takes to produce it manually. This will have a major impact on industries such as video game development, animation, and film production.
Generative AI will also have a big impact on the world of marketing and advertising. With the ability to generate high-quality images and videos, companies will be able to create eye-catching and effective advertising campaigns at a fraction of the cost and time it takes to produce them manually. Additionally, generative AI will also be able to assist with copywriting, making it possible to create engaging and persuasive content quickly and easily.
Another area where generative AI will have a big impact is in the field of journalism. With the ability to generate news articles, this technology will make it possible for news organizations to produce high-quality content quickly and easily. Additionally, generative AI will also be able to assist with fact-checking and research, making it possible for journalists to produce accurate and reliable content more efficiently.
Generative AI will also have a major impact on the field of education. With the ability to generate educational content, such as textbooks and lectures, this technology will make it possible to create high-quality educational materials at a fraction of the cost and time it takes to produce them manually. This will have a major impact on the way that students learn and the way that educators teach.
In conclusion, generative AI is set to have a major impact on content creation and generation in 2023. With the ability to create new images, music, and text without any human input, this technology will revolutionize many industries, including digital media, marketing and advertising, journalism, and education. Generative AI will make it possible to create high-quality content quickly and easily, and at a fraction of the cost and time it takes to produce it manually. As a result, we can expect to see a significant shift in the way that content is created and consumed in the years to come.
RECOMMENDED READING LIST
Generative AI: A Creative New World – By Sonya Huang, Pat Grady and GPT-3, September 19, 2022.
A Coming-Out Party for Generative A.I., Silicon Valley’s New Craze – New York Times, October 21, 2022.
AI’s New Creative Streak Sparks a Silicon Valley Gold Rush – Wired, October 27, 2022.
How Large Language Models Will Transform Science, Society, and AI – By Alex Tamkin and Deep Ganguli, February 5, 2021.
Prediction 2: Large language models will change the way of coding
In recent years, the field of artificial intelligence (AI) has made significant strides in natural language processing (NLP) and language generation. One of the most notable advancements in this area has been the release of large language models such as GPT-3 and Codex. These models have the ability to understand and generate human language, making them incredibly useful for a wide range of applications.
One area where large language models are expected to have a significant impact in 2023 is in the field of coding. As software development becomes increasingly complex, there is a growing need for tools that can assist developers in completing their work more efficiently. Large language models can be trained on existing codebases and used to generate new code, suggest lines of code, complete functions and methods, and even create complex algorithms.
This will have a transformative effect on coding workflows, making it possible for developers to focus on the high-level design of their software while the AI handles the tedious and repetitive aspects of coding. This will result in a more efficient and productive programming process, allowing developers to complete their work more quickly and with fewer errors.
Large language models will also have a big impact on the way code is written and maintained. With the ability to understand and generate human language, these models will be able to communicate with developers in natural language. This will make it possible for developers to explain their code to the AI in plain language, which will then be able to understand and execute it.
Additionally, large language models will also be able to assist with code maintenance and refactoring. By understanding the meaning and intent of the code, these models will be able to make suggestions for improving it and even make the changes themselves. This will help to ensure that code is well-written, maintainable, and free of errors.
In conclusion, large language models are set to have a major impact on coding workflows in 2023. By assisting developers in completing their work more efficiently, these models will help to improve the speed and quality of software development, making it possible for developers to focus on the high-level design of their software while the AI handles the tedious and repetitive aspects of coding. Additionally, large language models will also have a big impact on the way code is written and maintained, making code more maintainable, readable, and error-free.
RECOMMENDED READING LIST
Language modeling at scale: Gopher, ethical considerations and retrieval – Deep Mind, December 8, 2021
Introduction to Large Language Models – Armand Ruiz, October 12, 2022
Thanks to Large Language Models, computers understand language better than ever – Radical Ventures, January 16, 2023
Elad Gil: AI Revolution – Transformers and Large Language Models (LLMs) – Elad Gil, August 30, 2022
Prediction 3: AI will drive increased productivity across various professions
The use of artificial intelligence (AI) is no longer limited to a few specific industries; it is becoming more prevalent across a wide range of professions. As the technology continues to evolve and become more sophisticated, we can expect to see AI fueling productivity increases across a range of professions in 2023.
One of the most obvious areas where AI will have a big impact on productivity is in the field of customer service. With the ability to understand natural language and respond to customer inquiries, AI-powered chatbots and virtual assistants will make it possible for businesses to provide 24/7 customer service with minimal human intervention. This will help to reduce wait times for customers and increase the efficiency of customer service teams.
AI will also have a big impact on the field of finance. With the ability to process large amounts of data and make predictions, AI-powered systems will make it possible for financial institutions to identify fraudulent activity more quickly and accurately. Additionally, AI will also be able to assist with tasks such as portfolio management and risk assessment, making it possible for financial professionals to make more informed decisions more quickly.
Another area where AI will have a big impact on productivity is in the field of healthcare. With the ability to process large amounts of medical data, AI-powered systems will make it possible for doctors and nurses to identify and diagnose illnesses more quickly and accurately. Additionally, AI will also be able to assist with tasks such as patient monitoring and treatment planning, making it possible for healthcare professionals to provide better care more efficiently.
AI will also have a big impact on the field of logistics and transportation. With the ability to process large amounts of data and make predictions, AI-powered systems will make it possible for logistics and transportation companies to optimize routes and schedules, reducing the time and cost of transportation. Additionally, AI will also be able to assist with tasks such as inventory management and demand forecasting, making it possible for logistics and transportation professionals to make more informed decisions more quickly.
In conclusion, AI will fuel productivity increases across a range of professions in 2023. With the ability to process large amounts of data and make predictions, AI-powered systems will make it possible for professionals to make more informed decisions more quickly and more accurately. Additionally, AI will also be able to assist with tasks such as customer service, portfolio management, patient monitoring, and transportation optimization, making it possible for professionals to provide better service and results more efficiently. As a result, we can expect to see a significant increase in productivity across a wide range of professions in the coming years.
EXAMPLE CUSTOMER SERVICE
Prediction 4: Future notebooks will move towards low-code data solutions
Notebooks, such as Jupyter Notebooks and Colab, have become popular tools for data scientists, developers and researchers to write, test and share their code, as well as visualizations and markdown notes. They offer a flexible and interactive environment to create and share code, however they still rely on the user to have a certain level of coding skills. In 2023, the next generation of notebooks will inch closer to low-code data products, allowing users with less coding expertise to create and share data products more easily.
One of the main features of the next generation of notebooks will be the integration of visual drag-and-drop interfaces, allowing users to create data pipelines and workflows without the need to write code. These interfaces will also make it possible for users to connect different data sources, such as databases and APIs, and perform data cleaning, transformation and analysis.
Additionally, these notebooks will also offer pre-built modules, such as machine learning and natural language processing models, which can be easily integrated into the data pipelines, allowing users to perform advanced data analysis and modeling with minimal coding.
The next generation of notebooks will also offer a more collaborative environment, allowing multiple users to work on the same notebook and share their results. This will make it possible for teams to collaborate on data projects and share their findings more easily.
The next generation of notebooks will also provide a more user-friendly interface, making it easy for users to access and understand their data. These notebooks will offer a variety of visualization options, such as charts, graphs and maps, allowing users to explore and understand their data more easily.
In conclusion, the next generation of notebooks will inch closer to low-code data products, making it easier for users with less coding expertise to create and share data products. By integrating visual drag-and-drop interfaces, pre-built modules, and collaboration tools, these notebooks will enable users to perform data cleaning, transformation, analysis and modeling more easily. Additionally, with more user-friendly interface and visualization options, these notebooks will make it easier for users to access and understand their data, allowing them to make more informed decisions.
“Notebooks will win 20% of Excel users. Of the 1b global Excel users, 20% of them will become prosumers, writing Python/SQL to analyze data. They will do it in notebooks like Jupyter, which are easily shared, reproducible & version controlled. Those notebooks will become data apps used by end users inside of companies, replacing brittle Excel & Google Sheets.”
Tomasz Tunguz, Venture Capitalist, Redpoint Ventures
Prediction 5: Growing importance of data visualization and storytelling
Data visualization and storytelling are becoming increasingly important tools for communicating complex data insights to a wider audience. In 2023, we can expect to see a growing emphasis on these methods as a way to make data more accessible and understandable to a wider range of people.
One of the main reasons for this growing importance is the sheer amount of data that is being generated. With the explosion of data in recent years, it has become increasingly difficult for people to make sense of this data and to understand its implications. Data visualization and storytelling provide a way to make this data more accessible and understandable, by presenting it in a visual format that is easy to understand.
Another reason for this growing importance is the need for data to be communicated in a way that is engaging and memorable. Data visualization and storytelling allow data to be presented in a way that is visually appealing and easy to remember, making it more likely that people will retain the information and be able to use it to make better decisions.
Additionally, data visualization and storytelling are also becoming increasingly important in a business context. Businesses are using data visualization and storytelling to communicate their data insights to a wider range of stakeholders, including employees, customers, and investors. This allows them to make data-driven decisions and to communicate the value of their data in a way that is easy to understand.
In conclusion, in 2023, data visualization and storytelling will become an increasingly important tool for communicating complex data insights to a wider audience. The growing importance of this method is a result of the sheer amount of data being generated, the need for data to be communicated in a way that is engaging and memorable and the increasing importance in business context. Data visualization and storytelling provide a way to make data more accessible and understandable to a wider range of people, allowing them to make better decisions and understand the value of data.
RECOMMENDED BLOGS
Storytelling with Data by Cole Nussbaumer Knaflic – StorytellingWithData.com
Information is Beautiful by David McCandless – InformationIsBeautiful.net
Flowing Data by Nathan Yau – FlowingData.com
Visualizing Data by Andy Kirk – VisualisingData.com
Junk Charts by Kaiser Fung – JunkCharts.typepad.com
Prediction 6: Data literacy as core competency for individuals and organizations
Data literacy and data-driven decision making are becoming increasingly important as core competencies for individuals and organizations. In 2023, we can expect to see a growing emphasis on these skills as a way for individuals and organizations to stay competitive and make better decisions.
One of the main reasons for this growing importance is the sheer amount of data that is being generated. With the explosion of data in recent years, it has become increasingly difficult for people to make sense of this data and understand its implications. Data literacy and data-driven decision making provide a way for individuals and organizations to navigate this data and make better decisions based on it.
Another reason for this growing importance is the increasing use of data and analytics in a wide range of industries. Data is becoming an increasingly important asset for organizations, and the ability to use data effectively is becoming a key differentiator. Data literacy and data-driven decision making provide individuals and organizations with the skills they need to make the most of this data, and to stay competitive in their industry.
Additionally, data literacy and data-driven decision making are also becoming increasingly important in a business context. Businesses are using data to drive their decisions, and they are increasingly looking for employees who have the skills and knowledge to use data effectively. This is particularly true in industries such as finance, healthcare, and technology, where data is becoming an increasingly important asset.
Moreover, data literacy and data-driven decision making are essential for organizations and individuals to navigate the ethical and societal challenges that come with data usage. For instance, data literacy ensures that individuals and organizations can understand the ethical implications of their data usage, and make decisions that are in line with their values and the values of society. Similarly, data-driven decision making can help avoid biases and discrimination in decision making.
In conclusion, in 2023, data literacy and data-driven decision making will become increasingly important as core competencies for individuals and organizations. The growing importance of these skills is a result of the sheer amount of data being generated, the increasing use of data and analytics in a wide range of industries, the importance of data in the business context and the need to navigate the ethical and societal challenges that come with data usage. Data literacy and data-driven decision making provide individuals and organizations with the skills they need to make the most of data and stay competitive in their industry, and to make data-driven decisions that are transparent, ethical and fair.
WHAT IS DATA LITERACY
Prediction 7: Efforts to develop humanoid robots will attract more attention
Humanoid robots, robots that are designed to resemble humans in terms of appearance and movement, have been a topic of interest in the field of robotics for many years. In 2023, we can expect to see considerable attention being paid to efforts to develop these types of robots for a variety of different applications.
One of the main reasons for this attention is the increasing demand for robots that can work alongside humans in a variety of settings. Humanoid robots are well suited for this task as they can move and interact with their environment in ways that are similar to humans. This makes them ideal for tasks such as manufacturing, construction, and even personal assistance. In manufacturing, for example, humanoid robots can be used to perform tasks such as assembly, welding and painting, which are typically done by human operators. In construction, humanoid robots can be used for tasks such as bricklaying and welding, allowing for faster and more efficient building construction. Furthermore, in personal assistance, humanoid robots can be used for tasks such as helping with daily activities and providing companionship, which will be especially beneficial for elderly or disabled individuals.
Another reason for the attention on humanoid robots is the advancements in technology that have been made in recent years. With the rapid development of fields such as artificial intelligence, machine learning, and computer vision, it is now possible to create robots that are more human-like than ever before. These advancements have made it possible to create robots that can understand and respond to human speech, as well as recognize and interact with people and objects in their environment. This technology is making it possible to create robots that can understand human emotions and interact with people in a more natural way, which will be important for tasks such as personal assistance and customer service.
Another area that will attract attention is the development of human-like robots that can be used in entertainment and media, such as movies, TV, and video games. These robots will be designed to be able to mimic human emotions and expressions, making them more lifelike and engaging to audiences. Additionally, the technology may be used in the field of education, allowing students to interact with virtual teachers and assistants that can help them learn and understand complex subjects. This will be especially beneficial for children and students who have difficulty learning with traditional teaching methods.
However, developing humanoid robots also brings some ethical and societal challenges. One of the main challenges is to ensure that the robots are designed and programmed to behave in ways that are consistent with human ethical standards and values. This will require the development of robust ethical guidelines, as well as the integration of ethical decision-making mechanisms into the robots’ programming. Additionally, there will be the need to ensure that these robots do not displace human workers, or create other negative societal impacts.
In conclusion, efforts to develop humanoid robots will attract considerable attention in 2023. With the increasing demand for robots that can work alongside humans and advancements in technology, we can expect to see more humanoid robots being developed for a variety of applications such as manufacturing, construction, personal assistance, entertainment and education. However, it is important to be aware of the ethical and societal challenges that come with this technology and to develop robust guidelines and mechanisms to mitigate them.
WILL ROBOTS BE THE FUTURE?
Prediction 8: Web search will change due to rise of large language models
Web search has been an integral part of the internet since its inception, but in recent years, the way we search the web has changed dramatically. With the rise and performance of large language models, such as GPT-3, we can expect to see a significant change in web search in 2023.
One of the main changes that we can expect to see is an increase in the use of natural language processing (NLP) in web search. Large language models, such as GPT-3, are able to understand and respond to natural language queries, making it possible for users to search the web using more conversational and natural language. This will make it easier for users to find the information they are looking for and will also make it possible for them to find information that they may not have known to search for.
Another change that we can expect to see is an increase in the use of AI-powered assistants and chatbots for web search. With the ability to understand natural language, these AI-powered assistants and chatbots will be able to provide more accurate and relevant search results, as well as provide users with personalized recommendations based on their search history and preferences. This will make it easier for users to find the information they are looking for and will also make it possible for them to discover new information that they may not have known to search for.
We can also expect to see a rise in the use of visual search. Large language models can be used to generate and caption images, making it possible for users to find information using images as well as text. This can be especially useful for identifying objects, locations, and even emotions in images.
Additionally, we can also expect to see an increase in the use of voice search. Large language models can be used to understand and respond to voice queries, making it possible for users to search the web using only their voice. This can be especially useful for users who may have difficulty typing or using a keyboard.
In conclusion, web search will change significantly in 2023 due to the rise and performance of large language models. With the ability to understand and respond to natural language, these models will make it possible for users to search the web using more conversational and natural language. Additionally, the use of AI-powered assistants and chatbots, visual search, and voice search will also be on the rise. This will make it easier for users to find the information they are looking for and will also make it possible for them to discover new information that they may not have known to search for.
GOOGLE EVOLUTION
Prediction 9: Availability of data to train large models will become a constraint
Large language models, such as GPT-3, have become increasingly popular in recent years due to their ability to understand and generate natural language. These models are trained on vast amounts of data in order to learn the patterns and relationships that are present in natural language. In 2023, we can expect to see data availability becoming a constraint on the training of large language models.
One of the main reasons for this constraint is the sheer amount of data that is needed to train large language models. These models require vast amounts of data in order to learn the patterns and relationships that are present in natural language. As the size of the models increases, so does the amount of data required to train them. With the growing popularity of large language models, the availability of data to train them will become a constraint.
Another reason for this constraint is the increasing focus on data privacy and security. With the growing concern about data breaches and the misuse of personal data, organizations and individuals are becoming more cautious about sharing their data. This is making it more difficult to obtain the large amounts of data that are needed to train large language models.
Additionally, the cost of obtaining and storing the large amounts of data required to train these models can also become a constraint, especially for smaller companies and organizations. The cost of data storage, data privacy, and data security can become a significant obstacle to the training of large language models.
In conclusion, in 2023, data availability will become a constraint on the training of large language models. With the sheer amount of data required to train these models, the increasing focus on data privacy and security, and the cost of data storage, obtaining the necessary data to train these models will become increasingly challenging. Organizations and researchers will need to find ways to overcome this constraint in order to continue to develop and improve large language models. This could include developing techniques for training models on smaller amounts of data, finding new sources of data, or developing new data privacy and security measures.
GPT-4: 100 TRILLION PARAMETERS
Prediction 10: Data Science education will become more application-focused
Data science is a rapidly growing field that is becoming increasingly important in a wide range of industries. With the vast amounts of data being generated, it is essential that individuals and organizations have the ability to analyze and make sense of this data in order to make informed decisions. In 2023, we can expect to see a shift in data science education towards a more application-focused and outcome-oriented approach.
One of the main reasons for this shift is the increasing demand for data scientists who are able to apply their skills to real-world problems. Traditional data science education has often focused on teaching the technical aspects of the field, such as programming languages and statistical methods, rather than on how to apply these skills to solve real-world problems. This has led to a shortage of data scientists who are able to apply their skills to real-world problems.
In 2023, data science education will become more application-focused and outcome-oriented, with a greater emphasis on teaching students how to apply their skills to solve real-world problems. This will involve teaching students how to work with real-world data, how to use data to make predictions and how to communicate their findings to non-technical stakeholders. Additionally, this will also involve teaching students about the ethical implications of working with data and how to ensure that their work is transparent and trustworthy.
Another reason for this shift is the increasing focus on data science for decision-making. With the ability to make better decisions based on data, organizations are investing in data science education to improve their operations and gain a competitive advantage. Therefore, education will be geared towards teaching students how to use data to make predictions, how to communicate their findings to non-technical stakeholders and how to use data science in decision-making.
In conclusion, data science education will become more application-focused and outcome-oriented in 2023. With the increasing demand for data scientists who are able to apply their skills to real-world problems and the increasing focus on data science for decision-making, education will focus on teaching students how to apply their skills to solve real-world problems, use data to make predictions and communicate their findings to non-technical stakeholders. Additionally, ethics and transparency will be integrated throughout the curriculum to ensure that the future data scientists are responsible and trustworthy.