Artificial intelligence. The prevalence of this term and importance of AI applications in our everyday lives are likely to increase in the coming years and decades. At the bottom of this article are resources to start your learning and engagmenet journey.
Some examples of AI being used in our daily lives include:
Smartphone assistants: Many smartphones come equipped with AI assistants like Siri, Google Assistant, or Alexa, which can help you with tasks like setting reminders, answering questions, and making phone calls.
Personalized recommendations: Many websites and apps use AI algorithms to make personalized recommendations based on your past behavior and preferences. For example, Netflix uses AI to recommend TV shows and movies that you might enjoy.
Voice recognition: AI is used to power voice recognition software, which allows you to interact with your devices using natural language. This is used in applications like virtual assistants, as well as in other areas such as automated customer service systems.
Fraud detection: AI is being used by banks and other financial institutions to detect fraudulent activity in real time.
Self-driving cars: While not yet widely available, self-driving cars are being developed and tested by several companies, and they rely on AI to navigate roads and make decisions.
AI is also being used in many other areas, including healthcare, education, and manufacturing. As technology continues to advance, it is likely that AI will become even more prevalent in our daily lives.
It is important for people to have a basic understanding of artificial intelligence (AI) and how it is being used in our daily lives, especially as technology becomes more prevalent. This is because AI has the potential to impact many aspects of our lives, including how we work, communicate, and make decisions.
Having a basic understanding of AI can help people better understand how it is being used and how it might affect them. It can also help people make informed decisions about the technologies they use and the ways in which they interact with them.
Additionally, as AI continues to advance and become more widely used, it is likely that there will be more and more job opportunities in the field. Having a basic understanding of AI can help people understand what these job opportunities might involve and how to prepare for them.
Overall, while it is not necessary for everyone to become an expert in AI, having a basic understanding of the technology can be beneficial in many ways.
The societal and cultural aspects
AI has the potential to impact people and societies in many different ways, and the way that it is adopted and understood can vary significantly depending on the cultural and social context. Here are a few factors that might influence how different societies and cultures approach AI:
Economic and technological development: Societies with more advanced economies and higher levels of technological development may be more likely to adopt AI and incorporate it into their daily lives. For example, countries like the United States, China, and Japan, which have strong technology sectors and high levels of economic development, have been at the forefront of AI development and adoption. In contrast, countries with weaker economies and less developed technology sectors may be slower to adopt AI and may have less capacity to develop and implement it.
Legal and regulatory frameworks: Different countries have different laws and regulations governing the use of AI, which can impact its adoption and implementation. For example, some countries have enacted laws that specifically address the use of AI in certain sectors, such as healthcare or transportation. Other countries may have more general laws that apply to the use of AI or may have no specific laws at all. The legal and regulatory frameworks in place in a given country can impact the way that AI is used and understood in that society.
Cultural values and beliefs: Different societies may have different values and beliefs about technology and its role in society, which can influence their attitudes toward AI. For example, some cultures may place a high value on tradition and may be more skeptical of new technologies, while other cultures may be more open to innovation and change. These cultural values and beliefs can shape the way that AI is perceived and adopted in different societies.
Social and political context: The social and political climate of a society can also impact its adoption of AI. For example, a society that is experiencing rapid social and economic change may be more open to adopting new technologies like AI, while a society that is more stable and traditional may be more hesitant to adopt it. Similarly, a society with a strong tradition of technological innovation may be more receptive to AI, while a society with a weaker tradition of innovation may be more skeptical of it.
It’s also worth noting that AI has the potential to have different impacts on different groups within a society. For example, it could lead to job displacement for some workers while creating new opportunities for others. It’s important to consider these potential impacts and to have open and honest conversations about how to address any potential negative consequences.
Here are a few high-level differences globally in AI adoption and perception across different societies:
China: China has emerged as one of the predominant adopters of AI and AI-based applications, with a strong focus on developing and implementing AI in a variety of sectors, including healthcare, transportation, and education. The Chinese government has also taken a proactive approach to regulate AI, with a focus on promoting its development and encouraging innovation.
Europe: European societies have generally been more skeptical of AI and have tended to approach it with caution. This has been reflected in the region’s regulatory framework, which has focused on protecting citizens‘ privacy and rights. European countries have also emphasized the importance of ethical considerations in the development and use of AI more so than China for example.
United States: The United States has been a strong promoter of the development and innovation of AI and AI-based systems via both academia and industry, with many of the world’s leading tech companies located in the country. However, there has also been a significant public debate about the potential risks and ethical concerns associated with AI, and some groups have called for more regulation of the technology.
Japan: Japan has a long history of technological innovation and has been at the forefront of the development of AI. The country has also taken a proactive approach to regulate AI, with a focus on ensuring that it is used ethically and responsibly.
Data acting as a fuel for the growth of AI-based systems and what people should be aware of
Data is a critical component of artificial intelligence (AI) systems, and it plays a key role in enabling these systems to learn, improve, and innovate. Here are a few ways in which data fuels the growth and innovation of AI-based systems:
Training and learning: AI systems rely on data to learn and improve over time. When an AI system is „trained“ on a large dataset, it uses that data to learn about the characteristics and patterns that are present in the data. This enables the system to make predictions or decisions based on new data that it encounters.
Modeling and simulation: Data can also be used to create models or simulations that can help AI systems understand how different systems or processes work. These models can be used to test different scenarios and make predictions about how the system will behave in different situations.
Optimization and improvement: AI systems can use data to optimize their performance and improve over time. For example, an AI system that is used to predict stock prices might use data about historical stock prices and other market conditions to optimize its predictions.
It’s important for people to be aware that the quality and quantity of data that is used to train and improve AI systems can have a significant impact on their performance. If an AI system is trained on a large, diverse, and high-quality dataset, it is likely to perform better than a system that is trained on a smaller or less diverse dataset. It’s also important to consider issues of bias and fairness when collecting and using data to train AI systems, as these can have significant impacts on the performance and reliability of the system.
How data influences AI-based systems
Natural language processing: Natural language processing (NLP) is a field of AI that involves using computer programs to process and understand human language. NLP systems can be trained on large datasets of text, such as books, articles, and social media posts, to learn about the structure and meaning of language. This enables them to perform tasks like translation, summarization, and sentiment analysis.
Computer vision: Computer vision systems use AI and machine learning techniques to analyze and understand images and videos. These systems can be trained on large datasets of labeled images to learn about different objects, scenes, and patterns. This enables them to perform tasks like image classification, object detection, and facial recognition.
Healthcare: AI systems are being used in the healthcare industry to analyze large amounts of data, such as electronic health records, to identify patterns and predict outcomes. For example, an AI system might be trained on data about patient diagnoses and treatments to predict the likelihood of a patient developing a particular disease or responding to a particular treatment.
Finance: AI systems are being used in the finance industry to analyze large datasets of financial data, such as stock prices, to make predictions about market trends and risks. For example, an AI system might be trained on data about historical stock prices and other market conditions to predict the likelihood of a particular stock going up or down in value.
Transportation: AI systems are being used in the transportation industry to analyze data from sensors and other sources to optimize routes, predict maintenance needs, and improve safety. For example, an AI system might be trained on data about vehicle performance, traffic patterns, and weather conditions to predict the best routes for a particular trip or to identify potential maintenance issues.
Engaging in dialogue and discussion around the topic of AI
Given the prevalence of AI and its implications, it is very much important to engage in dialogue and discussion around this topic. There are numerous opportunities to do so:
Host a workshop or seminar: Organizing a workshop or seminar on AI and related topics can provide a platform for people to learn about the latest developments in the field and engage in discussions. You could invite experts in the field to give presentations or hold panel discussions to spark conversation. For example, you could host a workshop on the ethics of AI or a seminar on the latest advancements in natural language processing.
Start a club or group: Consider starting a club or group focused on AI and related topics. This can provide a regular forum for people to come together and discuss the latest developments in the field, ask questions, and share ideas. You could meet in person or use online platforms like Zoom or Google Meet to hold virtual meetings. You could also invite guest speakers to give presentations or hold discussions on specific topics.
Use social media: Social media platforms like Twitter and LinkedIn can be great places to find and engage with others interested in AI. You can join relevant groups or follow thought leaders in the field to stay up-to-date on the latest developments and participate in discussions. For example, you could join the AI Ethics group on LinkedIn or follow the #AI hashtag on Twitter to find and engage with others interested in the topic.
Write articles or blog posts: Sharing your thoughts and ideas about AI through writing can be a great way to spark conversation and encourage others to engage in discussions. You could consider writing articles or blog posts about AI and related topics and sharing them on social media or other online platforms. You could write about the latest advancements in AI, the ethical implications of the technology, or your own thoughts and ideas about its future.
Engage with others: Finally, one of the best ways to encourage discussions about AI is to simply engage with others who are interested in the topic. Ask questions, share your own ideas and thoughts, and be open to hearing the perspectives of others. You could start or join online discussions on forums or social media platforms, or participate in in-person events like meetups or conferences. You could also reach out to experts in the field and ask them questions or seek their advice.
Building a foundation to understand AI is less daunting than it seems
Developing a basic understanding of artificial intelligence (AI) is generally less daunting than one might imagine. While AI can be a complex and technical field, there are many resources available that can help people learn about the basics of the technology in an accessible way.
There are many books, articles, and online courses that can provide a broad overview of AI and its applications. These resources can help people understand the basic concepts and techniques used in the field, as well as the ways in which AI is being used in various industries.
Additionally, there are many AI-focused organizations and communities that can provide support and resources for those interested in learning about the technology. For example, there are AI meetup groups, online forums, and conferences that can provide opportunities for people to ask questions and engage with others interested in the field.
Overall, while AI can be a complex and rapidly evolving field, it is generally not too difficult for people to get a basic understanding of the technology and its applications.
There are numerous resources and channels for people looking to get started even though one might not necessarily pursue a career in AI. Some popular examples and resources are:
„AI For Dummies“ by John Paul Mueller: This book provides a comprehensive overview of AI, including the history of the field, the different types of AI, and the ways in which it is being used in various industries.
„The Singularity Trap“ by Federico Pistono: This book explores the potential risks and benefits of AI and provides a balanced perspective on the technology.
„Introduction to Artificial Intelligence“ on Coursera: This course, offered by Stanford University, provides a broad overview of AI, including the history of the field, the different types of AI, and the ways in which it is being used in various industries.
„Artificial Intelligence Basics“ on edX: This course, offered by the Massachusetts Institute of Technology (MIT), covers the basics of AI, including machine learning, natural language processing, and robotics.
AI-focused organizations and communities:
Association for Computing Machinery (ACM): The ACM is a professional association for computer scientists and has several special interest groups focused on AI, including the Special Interest Group on Artificial Intelligence (SIGAI). The ACM also hosts conferences and events on AI topics.
Machine Learning Mastery: This website provides resources and tutorials on machine learning, which is a subfield of AI that involves training algorithms to make predictions or decisions based on data.
AI Ethics: This website provides resources and information on the ethical implications of AI, including articles, events, and online courses.
Overall, there are many other resources available that can help people get a basic understanding of AI and its applications. These resources can provide a foundation for those who are interested in learning more about the technology and its potential impacts on society.
Who is Varsh Anilkumar?
Varsh Anilkumar is an engineer and entrepreneur passionate about solving problems using technology.
He has worked in leading AI and data engineering projects for a wide range of use cases ranging from e-commerce to health tech.
Varsh Anilkumar is also the Founder and director of a health tech startup BlockMMP. BlockMMP is based in the US and offers technological solutions to help out in the ongoing Opioid crisis rampant in North America and a few other parts of the world. BlockMMP offers a secure prescription dosage tracking software platform that enables streamlined dosage tracking and administration of medications used to treat patients with Opioid use disorders. Initially funded by an NIH grant for research and development, the startup has scaled out the platform to function end to end to provide better care and trust for patients suffering from Opioid use disorders. Varsh is also working on a computational platform that utilizes AI for cancer research in collaboration with a few researchers in the field.
He is passionate about democratizing technical education specifically in the field of AI and Data Science engineering and is actively involved in mentoring engineering students at Boston University and professionals trying to make a transition into tech through other organizations such as the Global AI hub.
Varsh actively advocates for a rational collaboration between technology developers and policymakers to enable fair, governed, and unbiased technology service offerings. He is currently writing a book that emphasizes the importance of a clear understanding of AI to its end users which empowers them to be more actively involved in the design and policy aspects of AI by having the ability to ask informed questions to the tech and policy architects of AI.