In the wake of our transformative “Generative AI: A New Frontier for VC Investments” virtual event, the buzz of innovation, disruption, and opportunity continues to resonate. The discussions and insights shared by our experts inspired a myriad of queries from our audience. It’s clear that as we navigate this new frontier, there’s still so much to unravel about Generative AI and its implications for Venture Capital investments.
“Generative AI: A New Frontier for VC Investments” Q&A with Beny Rubinstein, Head of BV Israel, TIGER 21 Chair, TLV
Your questions reflected our curiosity, engagement, and the thirst for more knowledge, and we couldn’t leave them unanswered. Therefore, we’ve reached out to one of our esteemed speakers, Beny Rubinstein, to provide clarity and delve deeper into this fascinating subject.
In this Q&A article, we present Beny’s thoughtful and insightful responses to your questions. Covering a broad array of topics—from uses cases of Generative AI and venture capitalism through bias to LLM’s effect on education, this Q&A provides information for anyone keen to understand this dynamic technology and its role in shaping our future.
Whether you attended the event or are just catching up, we invite you to dive into the following article. Consider this a continuation of the conversation started at our event, an opportunity to revisit the frontier of Generative AI and deepen our understanding of this revolutionary technology. Let’s continue to explore, question, and pioneer together.
And this is just the beginning. We’re preparing yet another enlightening Q&A piece featuring insights from our other accomplished speakers. They’ll be addressing even more of your queries, shedding light on further complexities, and painting a broader picture of the Generative AI landscape.
Stay tuned for our next article, where we’ll be delving into more intricate facets of Generative AI, its potential impact on various sectors, and how it’s shaping VC investment strategies. Your curiosity fuels this journey, and together, we’re continuing to explore the breadth and depth of this transformative technology.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
For the conference details, agenda, speaker line-up, and handouts CLICK HERE.
For the conference recording CLICK HERE
Q: Christopher Mott: If we are going to talk about productivity and operational use of AI INSIDE VCs, can we be concrete about what AI services and use cases — maybe using a lifecycle approach from POV of the VC? General benefit statements aren’t as useful as those we can read in the media or find on Google. Likewise, if APIs are important for the interoperability of operations in enterprises or other areas, what use cases and applications are being integrated to create a new and better way than the past?
[Beny Rubinstein]: Certainly! AI can provide valuable benefits to venture capital firms across various stages of the investment lifecycle. Here are some concrete use cases of AI in the context of venture capital:
Deal Scouting: AI-powered algorithms can analyze vast amounts of data from various sources such as news articles, social media, industry reports, and startup databases to identify potential investment opportunities. Natural Language Processing (NLP) techniques can extract relevant information and identify emerging trends, helping VCs discover promising startups more efficiently.
Due Diligence: AI can assist in the due diligence process by automating data analysis and pattern recognition. Machine Learning algorithms can analyze financial statements, market trends, and customer feedback to provide insights into a startup’s financial health, market potential, and competitive positioning. This helps VCs make more informed investment decisions and identify potential risks.
Market Analysis: By analyzing large datasets, AI algorithms can identify market trends, consumer behavior patterns, and competitive landscapes. This information helps VCs assess market opportunities and potential risks associated with a particular investment.
Portfolio Management: AI-powered dashboards and predictive analytics can help VCs track the progress and performance of their portfolio companies, identify areas for improvement, and make data-driven decisions regarding resource allocation and strategy adjustments.
Risk Assessment: By leveraging historical data, AI models can identify patterns and signals that may indicate risks related to financial stability, operational efficiency, or market dynamics. This enables VCs to proactively address risks and take appropriate measures to protect their investments.
Investor Relations: AI-powered chatbots and virtual assistants can enhance communication and engagement with limited partners (LPs).
It’s important to note that while AI can bring significant benefits to venture capital, human expertise and judgment remain essential throughout the investment process. AI should be seen as a tool to augment and assist VC professionals rather than replace them.
I wish I had AI tools to help me and my team do that Back in the days when I started Acelera Partners, a post-accelerator and micro-VC that invested in AI startups! Hopefully for the next fund I will be raising that will be one of the main areas I will focus on so that VCs can also reap the benefits of AI for better outcomes of their own activities – “walk the talk”!
Regarding APIs (Application Programming Interfaces): they play a crucial role in enabling interoperability and creating new and improved ways of operating across various sectors. Here are some use cases and applications where the integration of APIs is driving innovation and transforming traditional practices:
- E-commerce and Retail: API integrations have revolutionized the e-commerce and retail industry by enabling seamless connections between different systems. For example:
- Payment Gateway APIs allow businesses to securely process online transactions and accept various payment methods.
- Shipping APIs enable real-time tracking and logistics management, improving order fulfillment and customer experience.
- Fintech and Open Banking: APIs are reshaping the financial services landscape by promoting interoperability and enabling new services. Key applications include:
- Open Banking APIs allow secure sharing of financial data between banks and authorized third-party providers, empowering users with better financial insights and enabling innovative services. This is a very hot area for banco BV, for example.
- Payment APIs enable easy integration of payment processing into applications, facilitating smooth and secure transactions.
- Investment APIs provide developers access to stock market data, trading capabilities, and investment tools, fostering the development of investment platforms and robo-advisory services.
- Healthcare and Telemedicine: APIs are transforming healthcare by facilitating data exchange, interoperability, and telemedicine services. Examples include:
- Electronic Health Record (EHR) APIs enable secure and standardized sharing of patient health data across different healthcare systems, improving care coordination and interoperability.
- Telemedicine APIs integrate video consultations, appointment scheduling, and patient management systems, enabling remote healthcare delivery and telehealth applications.
- Health and Fitness APIs connect wearable devices, mobile apps, and health monitoring platforms, providing users with personalized health insights, and encouraging preventive care. I used that back in 2008 when I was the global product manager for Microsoft HealthVault (we were really early in the game and learned a lot!).
These are just a few examples highlighting how API integrations are driving innovation and creating new and improved ways of operating across industries. APIs foster interoperability, collaboration, and the development of novel services and applications, ultimately enhancing user experiences and unlocking new opportunities for businesses.
Q: Eleanor Wright: Will bias in venture capital lead to bias in AI?
[Beny Rubinstein]: I can share insights on the relationship between bias in venture capital and bias in AI from a few different vantage points. First, as someone who was employee number 6 in Microsoft Cloud & AI (Azure) global business development team. Second, as a venture capitalist with extensive experience in early-stage startups and AI investments (I founded and led the first partner of Microsoft Ventures in Latin America and invested in a dozen Israeli AI startups accelerated by Microsoft in Israel). I actively engage with organizations like Women in Tech where I will be on a panel on July 3rd in Tel Aviv addressing “How AI empowers diversity & inclusion by eliminating biases, fostering fair decision-making, and creating equitable opportunities for all”.
While bias can exist within the venture capital industry, it does not necessarily directly translate into bias in AI. However, it’s important to recognize that venture capital plays a crucial role in shaping the development and deployment of AI technologies. The biases present within the venture capital ecosystem, such as unconscious biases in investment decision-making, can indirectly impact the diversity and inclusivity of AI innovations.
When venture capitalists predominantly invest in startups led by individuals from specific demographics or with similar backgrounds, it can result in a lack of diversity in the teams developing AI technologies. This limited representation can inadvertently lead to biases in the data used to train AI models and the design decisions made during their development.
Bias in AI can emerge from various sources, including biased training data, algorithmic design choices, and the social and cultural context in which AI is deployed. Therefore, addressing bias in AI requires a comprehensive approach that encompasses not only venture capital but also diverse representation in AI research and development teams, inclusive data collection and labeling processes, rigorous testing and validation, and ongoing ethical considerations.
In summary, while bias in venture capital does not directly lead to bias in AI, it can indirectly influence the diversity and inclusivity of AI technologies. Addressing bias in AI requires a multifaceted approach, including diverse representation in AI development teams, inclusive data practices, and ongoing ethical considerations, in which venture capitalists can play a significant role by promoting diversity in their investment decisions and supporting initiatives aimed at addressing bias in AI.
Q: Arvind Punj: Can anyone comment on the change in the education system which is needed because of the LLM models impacting learning?
[Beny Rubinstein]: Absolutely! This is an incredibly fascinating and crucial topic for the future of society! The rise of LLMs has the potential to bring significant changes (and improvements!) to the education system. These models can provide access to vast amounts of information and assist in automating certain tasks traditionally performed by educators, such as grading and content generation. They also have the potential to personalize learning experiences, offering tailored feedback and adaptive resources to individual students.
However, the integration of LLMs into education also requires rethinking the role of human educators and the need for a balanced approach. While LLMs can provide valuable assistance, they cannot fully replace the essential aspects of human interaction, mentorship, and emotional intelligence that educators bring to the learning process. To leverage the benefits of LLMs while mitigating their limitations, the education system must adapt. This adaptation may involve integrating LLMs as tools to support educators, emphasizing critical thinking, problem-solving, and creativity in the curriculum, and focusing on developing skills that are uniquely human and complementary to AI capabilities (after all, those will be the skills required soon; I suggest you refer to World Economic Forum’s “Future o Jobs Report 2023” for more details).
Furthermore, attention should be given to ethical considerations surrounding the use of LLMs in education, such as data privacy, algorithmic biases, and the potential for widening educational inequalities. Safeguards and guidelines should be put in place to ensure responsible and equitable deployment of LLMs in educational settings. In summary, the integration of LLMs into the education system has the potential to revolutionize learning experiences, but careful thought and planning are required to strike the right balance between technology and human involvement, address ethical concerns, and ensure equitable access to education in the age of AI. My former Professor at the University of Pennsylvania’s prestigious Wharton School of Business, Ethan Mollick, has been doing fascinating work on that space and is not only allowing his students to use ChatGPT, they are required to (see his interview to NPR here: ‘Everybody is cheating’: Why this teacher has adopted an open ChatGPT policy : NPR)
Q: Nanjun Li: Do you think the world will be more divided as generative AI deepening the division of labour?
The impact of generative AI on the division of labour and potential division within societies is a topic of debate among experts. While it is challenging to predict the exact outcome, here are two perspectives on the subject:
- Potential for Division: Some experts argue that the advancements in generative AI could deepen the division of labour in society. As AI technologies become more capable of performing complex tasks, there is a possibility of job displacement in certain industries. This could lead to a division between those who have the skills and capabilities to adapt to the evolving job market and those who do not, potentially exacerbating existing inequalities.
- Potential for Convergence: On the other hand, some experts believe that generative AI has the potential to converge rather than divide societies. They argue that while AI may automate certain tasks, it can also augment human capabilities and create new opportunities. AI technologies can assist humans in performing complex tasks, enabling them to focus on higher-value work that requires creativity, critical thinking, and interpersonal skills. This could lead to a more inclusive and collaborative labour market, where individuals can contribute their unique strengths and expertise.
It is important to note that the impact of generative AI on the division of labour will depend on various factors, including the pace of AI adoption, the availability of reskilling and upskilling opportunities, government policies, and societal responses. To mitigate potential divisions, efforts such as investing in education and skills development, promoting inclusive AI adoption, and implementing supportive policies can play a crucial role in ensuring that the benefits of AI are accessible to a broader segment of the population.
Ultimately, whether the world becomes more divided or more convergent because of generative AI will depend on the choices made by individuals, organizations, and societies as they navigate the opportunities and challenges presented by AI technologies. I recommend the Ted Talk “How we can face the future without fear, together” from Rabbi Lord Jonathan Sacks (Z”L) from 2017 on that topic which has more than 2M views as it’s a societal issue more than a technological issue (he is also author of best seller “Morality: Restoring the Common Good in Divided Times” which is extremely helpful to understand the background and context of some the dilemmas we are facing now).
Q: Boris Bend: Thinking beyond the atypical: How do you see the world changing once true AGI will be achieved, and when do you personally expect that this may be achieved? (There are quite a few experts that expect that this could happen much faster than most people believe due to the current exponential progress of AI research.)
[Beny Rubinstein]: Artificial intelligence can be broadly categorized into three main types: artificial narrow intelligence (ANI), artificial general intelligence (AGI) and artificial superintelligence (ASI). Amongst these, AGI positions artificial intelligence at par with human capabilities. As a result, AGI systems can think, comprehend, learn, and apply their intelligence to solve problems much like humans would for a given situation. In simpler words, if AGI is achieved, machines would be capable of understanding the world at the same capacity as any human being.
Regarding the timeline for achieving true AGI, opinions vary among experts. Some experts believe that AGI could be achieved sooner than anticipated due to the rapid progress in AI research, while others believe it may still be several decades away. Accurately predicting the timing of AGI is challenging due to the complexity of the problem and the many uncertainties involved. The next decade will play a crucial role in accelerating the development of AGI. In fact, experts believe that there is a 25% chance of achieving human-like AI by 2030. However, while there have been significant advancements in narrow AI domains, achieving AGI requires overcoming several technical hurdles, such as building robust generalization capabilities, addressing ethical considerations, and ensuring safety measures are in place. Personally, I think it will take at least a decade to get there, if not more, due to those several challenges In the way of Artificial General Intelligence, but it could happen sooner with humans organize themselves better and collaborate more efficiently!
The development of AGI has the potential to bring about profound changes in various aspects of society. Here are some potential areas of impact:
- Automation and Labour: AGI could replace certain manual and cognitive tasks, leading to shifts in employment patterns and the need for upskilling and retraining.
- Scientific Advancements: AGI could accelerate scientific research and discovery by analyzing vast amounts of data, identifying patterns, and generating hypotheses. It may facilitate breakthroughs in areas such as medicine, climate change, and fundamental sciences.
- Socioeconomic Considerations: Achieving AGI raises important socioeconomic questions, including distribution of wealth, access to technology, and ethical considerations surrounding AI decision-making and control.
- Human-Machine Collaboration: AGI could enable more effective collaboration between humans and machines, augmenting human capabilities in decision-making, creativity, and problem-solving.
Q: Marufa Bhuiyan: Based on the data and investment, Which country is the AI capital of the world?
[Beny Rubinstein]: The landscape of AI dominance is evolving, with several countries making significant contributions. The United States has long been considered the AI capital of the world, with a robust ecosystem, substantial investments, and leading tech companies. However, recent developments highlight China’s emergence as a strong contender, with significant investments, a focus on AI research, and a national strategy to become a global leader. Israel, on the other hand, has gained prominence in AI startups and innovation, benefiting from a thriving tech ecosystem and strong research and development efforts. Sam Altman, CEO of Microsoft-backed OpenAI and ChatGPT creator took part in a talk at Tel Aviv University in Tel Aviv, Israel on June 5, 2023, and spurred some ideas on how to establish a national policy and strategy for the use and development of AI during his meeting with the country’s President Isaac Herzog (bear in mind that over 400 multinational organizations have R&D Centers in Israel and a big chunk of AI innovation/development for the “Big Tech” – Microsoft, Google, Amazon, META – is already done in Israel). Other countries like Canada and the United Kingdom are also making noteworthy contributions to the AI landscape. While the United States, China, and Israel currently hold key positions, the competition in AI remains dynamic, with various countries vying for leadership in this rapidly advancing field.
According to the 2019 AI Index Report, published by the Stanford Institute for Human-Centered Artificial Intelligence in California, it is estimated that global private investment in AI in 2019 was more than US$70 billion. The US, China and Europe took the largest share; Israel, Singapore and Iceland were found to invest heavily in per capita terms. Start-ups founded on AI technologies are a major part of the ecosystem, garnering more than $37 billion globally in investments in 2019, up from $1.3 billion raised in 2010, according to the report. (Source: The race to the top among the world’s leaders in artificial intelligence (nature.com))
About Beny Rubinstein:
Beny serves as the Head of Banco BV in Israel and is a Strategic Advisor for Evolution.inc, an AI-for-AI Generator of AI systems. With an MBA from The Wharton School, and as a founding member of Microsoft Cloud & AI (Azure), Beny’s wealth of experience in the field is unparalleled. He’s committed to amplifying the legacy and impact of wealth creators around the world and helping them live a meaningful life.
In the wake of our transformative “Generative AI: A New Frontier for VC Investments” virtual event, the buzz of innovation, disruption, and opportunity continues to resonate. The discussions and insights shared by our experts inspired a myriad of queries from our audience. It’s clear that as we navigate this new frontier, there’s still so much to unravel about Generative AI and its implications for Venture Capital investments.
“Generative AI: A New Frontier for VC Investments” Q&A with Beny Rubinstein, Head of BV Israel, TIGER 21 Chair, TLV
In this Q&A article, we present Beny’s thoughtful and insightful responses to your questions. Covering a broad array of topics—from uses cases of Generative AI and venture capitalism through bias to LLM’s effect on education, this Q&A provides information for anyone keen to understand this dynamic technology and its role in shaping our future.
Whether you attended the event or are just catching up, we invite you to dive into the following article. Consider this a continuation of the conversation started at our event, an opportunity to revisit the frontier of Generative AI and deepen our understanding of this revolutionary technology. Let’s continue to explore, question, and pioneer together.
And this is just the beginning. We’re preparing yet another enlightening Q&A piece featuring insights from our other accomplished speakers. They’ll be addressing even more of your queries, shedding light on further complexities, and painting a broader picture of the Generative AI landscape.
Stay tuned for our next article, where we’ll be delving into more intricate facets of Generative AI, its potential impact on various sectors, and how it’s shaping VC investment strategies. Your curiosity fuels this journey, and together, we’re continuing to explore the breadth and depth of this transformative technology.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
For the conference details, agenda, speaker line-up, and handouts CLICK HERE.
For the conference recording CLICK HERE
Q: Christopher Mott: If we are going to talk about productivity and operational use of AI INSIDE VCs, can we be concrete about what AI services and use cases — maybe using a lifecycle approach from POV of the VC? General benefit statements aren’t as useful as those we can read in the media or find on Google. Likewise, if APIs are important for the interoperability of operations in enterprises or other areas, what use cases and applications are being integrated to create a new and better way than the past?
[Beny Rubinstein]: Certainly! AI can provide valuable benefits to venture capital firms across various stages of the investment lifecycle. Here are some concrete use cases of AI in the context of venture capital:
Deal Scouting: AI-powered algorithms can analyze vast amounts of data from various sources such as news articles, social media, industry reports, and startup databases to identify potential investment opportunities. Natural Language Processing (NLP) techniques can extract relevant information and identify emerging trends, helping VCs discover promising startups more efficiently.
Due Diligence: AI can assist in the due diligence process by automating data analysis and pattern recognition. Machine Learning algorithms can analyze financial statements, market trends, and customer feedback to provide insights into a startup’s financial health, market potential, and competitive positioning. This helps VCs make more informed investment decisions and identify potential risks.
Market Analysis: By analyzing large datasets, AI algorithms can identify market trends, consumer behavior patterns, and competitive landscapes. This information helps VCs assess market opportunities and potential risks associated with a particular investment.
Portfolio Management: AI-powered dashboards and predictive analytics can help VCs track the progress and performance of their portfolio companies, identify areas for improvement, and make data-driven decisions regarding resource allocation and strategy adjustments.
Risk Assessment: By leveraging historical data, AI models can identify patterns and signals that may indicate risks related to financial stability, operational efficiency, or market dynamics. This enables VCs to proactively address risks and take appropriate measures to protect their investments.
Investor Relations: AI-powered chatbots and virtual assistants can enhance communication and engagement with limited partners (LPs).
It’s important to note that while AI can bring significant benefits to venture capital, human expertise and judgment remain essential throughout the investment process. AI should be seen as a tool to augment and assist VC professionals rather than replace them.
I wish I had AI tools to help me and my team do that Back in the days when I started Acelera Partners, a post-accelerator and micro-VC that invested in AI startups! Hopefully for the next fund I will be raising that will be one of the main areas I will focus on so that VCs can also reap the benefits of AI for better outcomes of their own activities – “walk the talk”!
Regarding APIs (Application Programming Interfaces): they play a crucial role in enabling interoperability and creating new and improved ways of operating across various sectors. Here are some use cases and applications where the integration of APIs is driving innovation and transforming traditional practices:
These are just a few examples highlighting how API integrations are driving innovation and creating new and improved ways of operating across industries. APIs foster interoperability, collaboration, and the development of novel services and applications, ultimately enhancing user experiences and unlocking new opportunities for businesses.
Q: Eleanor Wright: Will bias in venture capital lead to bias in AI?
[Beny Rubinstein]: I can share insights on the relationship between bias in venture capital and bias in AI from a few different vantage points. First, as someone who was employee number 6 in Microsoft Cloud & AI (Azure) global business development team. Second, as a venture capitalist with extensive experience in early-stage startups and AI investments (I founded and led the first partner of Microsoft Ventures in Latin America and invested in a dozen Israeli AI startups accelerated by Microsoft in Israel). I actively engage with organizations like Women in Tech where I will be on a panel on July 3rd in Tel Aviv addressing “How AI empowers diversity & inclusion by eliminating biases, fostering fair decision-making, and creating equitable opportunities for all”.
While bias can exist within the venture capital industry, it does not necessarily directly translate into bias in AI. However, it’s important to recognize that venture capital plays a crucial role in shaping the development and deployment of AI technologies. The biases present within the venture capital ecosystem, such as unconscious biases in investment decision-making, can indirectly impact the diversity and inclusivity of AI innovations.
When venture capitalists predominantly invest in startups led by individuals from specific demographics or with similar backgrounds, it can result in a lack of diversity in the teams developing AI technologies. This limited representation can inadvertently lead to biases in the data used to train AI models and the design decisions made during their development.
Bias in AI can emerge from various sources, including biased training data, algorithmic design choices, and the social and cultural context in which AI is deployed. Therefore, addressing bias in AI requires a comprehensive approach that encompasses not only venture capital but also diverse representation in AI research and development teams, inclusive data collection and labeling processes, rigorous testing and validation, and ongoing ethical considerations.
In summary, while bias in venture capital does not directly lead to bias in AI, it can indirectly influence the diversity and inclusivity of AI technologies. Addressing bias in AI requires a multifaceted approach, including diverse representation in AI development teams, inclusive data practices, and ongoing ethical considerations, in which venture capitalists can play a significant role by promoting diversity in their investment decisions and supporting initiatives aimed at addressing bias in AI.
Q: Arvind Punj: Can anyone comment on the change in the education system which is needed because of the LLM models impacting learning?
[Beny Rubinstein]: Absolutely! This is an incredibly fascinating and crucial topic for the future of society! The rise of LLMs has the potential to bring significant changes (and improvements!) to the education system. These models can provide access to vast amounts of information and assist in automating certain tasks traditionally performed by educators, such as grading and content generation. They also have the potential to personalize learning experiences, offering tailored feedback and adaptive resources to individual students.
However, the integration of LLMs into education also requires rethinking the role of human educators and the need for a balanced approach. While LLMs can provide valuable assistance, they cannot fully replace the essential aspects of human interaction, mentorship, and emotional intelligence that educators bring to the learning process. To leverage the benefits of LLMs while mitigating their limitations, the education system must adapt. This adaptation may involve integrating LLMs as tools to support educators, emphasizing critical thinking, problem-solving, and creativity in the curriculum, and focusing on developing skills that are uniquely human and complementary to AI capabilities (after all, those will be the skills required soon; I suggest you refer to World Economic Forum’s “Future o Jobs Report 2023” for more details).
Furthermore, attention should be given to ethical considerations surrounding the use of LLMs in education, such as data privacy, algorithmic biases, and the potential for widening educational inequalities. Safeguards and guidelines should be put in place to ensure responsible and equitable deployment of LLMs in educational settings. In summary, the integration of LLMs into the education system has the potential to revolutionize learning experiences, but careful thought and planning are required to strike the right balance between technology and human involvement, address ethical concerns, and ensure equitable access to education in the age of AI. My former Professor at the University of Pennsylvania’s prestigious Wharton School of Business, Ethan Mollick, has been doing fascinating work on that space and is not only allowing his students to use ChatGPT, they are required to (see his interview to NPR here: ‘Everybody is cheating’: Why this teacher has adopted an open ChatGPT policy : NPR)
Q: Nanjun Li: Do you think the world will be more divided as generative AI deepening the division of labour?
The impact of generative AI on the division of labour and potential division within societies is a topic of debate among experts. While it is challenging to predict the exact outcome, here are two perspectives on the subject:
It is important to note that the impact of generative AI on the division of labour will depend on various factors, including the pace of AI adoption, the availability of reskilling and upskilling opportunities, government policies, and societal responses. To mitigate potential divisions, efforts such as investing in education and skills development, promoting inclusive AI adoption, and implementing supportive policies can play a crucial role in ensuring that the benefits of AI are accessible to a broader segment of the population.
Ultimately, whether the world becomes more divided or more convergent because of generative AI will depend on the choices made by individuals, organizations, and societies as they navigate the opportunities and challenges presented by AI technologies. I recommend the Ted Talk “How we can face the future without fear, together” from Rabbi Lord Jonathan Sacks (Z”L) from 2017 on that topic which has more than 2M views as it’s a societal issue more than a technological issue (he is also author of best seller “Morality: Restoring the Common Good in Divided Times” which is extremely helpful to understand the background and context of some the dilemmas we are facing now).
Q: Boris Bend: Thinking beyond the atypical: How do you see the world changing once true AGI will be achieved, and when do you personally expect that this may be achieved? (There are quite a few experts that expect that this could happen much faster than most people believe due to the current exponential progress of AI research.)
[Beny Rubinstein]: Artificial intelligence can be broadly categorized into three main types: artificial narrow intelligence (ANI), artificial general intelligence (AGI) and artificial superintelligence (ASI). Amongst these, AGI positions artificial intelligence at par with human capabilities. As a result, AGI systems can think, comprehend, learn, and apply their intelligence to solve problems much like humans would for a given situation. In simpler words, if AGI is achieved, machines would be capable of understanding the world at the same capacity as any human being.
Regarding the timeline for achieving true AGI, opinions vary among experts. Some experts believe that AGI could be achieved sooner than anticipated due to the rapid progress in AI research, while others believe it may still be several decades away. Accurately predicting the timing of AGI is challenging due to the complexity of the problem and the many uncertainties involved. The next decade will play a crucial role in accelerating the development of AGI. In fact, experts believe that there is a 25% chance of achieving human-like AI by 2030. However, while there have been significant advancements in narrow AI domains, achieving AGI requires overcoming several technical hurdles, such as building robust generalization capabilities, addressing ethical considerations, and ensuring safety measures are in place. Personally, I think it will take at least a decade to get there, if not more, due to those several challenges In the way of Artificial General Intelligence, but it could happen sooner with humans organize themselves better and collaborate more efficiently!
The development of AGI has the potential to bring about profound changes in various aspects of society. Here are some potential areas of impact:
Q: Marufa Bhuiyan: Based on the data and investment, Which country is the AI capital of the world?
[Beny Rubinstein]: The landscape of AI dominance is evolving, with several countries making significant contributions. The United States has long been considered the AI capital of the world, with a robust ecosystem, substantial investments, and leading tech companies. However, recent developments highlight China’s emergence as a strong contender, with significant investments, a focus on AI research, and a national strategy to become a global leader. Israel, on the other hand, has gained prominence in AI startups and innovation, benefiting from a thriving tech ecosystem and strong research and development efforts. Sam Altman, CEO of Microsoft-backed OpenAI and ChatGPT creator took part in a talk at Tel Aviv University in Tel Aviv, Israel on June 5, 2023, and spurred some ideas on how to establish a national policy and strategy for the use and development of AI during his meeting with the country’s President Isaac Herzog (bear in mind that over 400 multinational organizations have R&D Centers in Israel and a big chunk of AI innovation/development for the “Big Tech” – Microsoft, Google, Amazon, META – is already done in Israel). Other countries like Canada and the United Kingdom are also making noteworthy contributions to the AI landscape. While the United States, China, and Israel currently hold key positions, the competition in AI remains dynamic, with various countries vying for leadership in this rapidly advancing field.
According to the 2019 AI Index Report, published by the Stanford Institute for Human-Centered Artificial Intelligence in California, it is estimated that global private investment in AI in 2019 was more than US$70 billion. The US, China and Europe took the largest share; Israel, Singapore and Iceland were found to invest heavily in per capita terms. Start-ups founded on AI technologies are a major part of the ecosystem, garnering more than $37 billion globally in investments in 2019, up from $1.3 billion raised in 2010, according to the report. (Source: The race to the top among the world’s leaders in artificial intelligence (nature.com))
About Beny Rubinstein:
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