Evolution of AI Chatbots and Large Language Models Based AI Assistants

Tags

AI, LLaMA

Artificial Intelligence (AI) has been a topic of discussion for several decades, but it wasn’t until recently that AI chatbots became popular. AI chatbots have evolved over time, and today they are sophisticated tools that have become an essential part of many businesses. In this article, we will explore the evolution of AI chatbots and how they have changed over the years.

The first AI chatbot was created by Joseph Weizenbaum in the 1960s. The chatbot, named ELIZA, was designed to simulate a psychotherapist and was capable of holding a conversation with humans. ELIZA was a groundbreaking development, and it paved the way for further advancements in the field of AI chatbots.

In the 1990s, a new type of chatbot emerged, known as “virtual assistants.” These chatbots were designed to help users perform tasks, such as booking a flight or making a reservation at a restaurant. One of the most popular virtual assistants was Microsoft’s Clippy, which was introduced in 1997. Clippy was a cartoon paperclip that would pop up on the screen to assist users with their tasks.

In the early 2000s, AI chatbots became more sophisticated, and they were used in a variety of industries. For example, some companies used chatbots to provide customer service, while others used them for marketing purposes. One of the most notable chatbots of this era was SmarterChild, which was introduced in 2001. SmarterChild was a virtual assistant that was available on AOL Instant Messenger and could answer a wide range of questions.

In the mid-2000s, chatbots began to incorporate machine learning algorithms, which allowed them to learn from interactions with humans. This made them more intelligent and allowed them to provide more accurate responses to user queries. One of the most notable chatbots of this era was A.L.I.C.E., which was introduced in 2002. A.L.I.C.E. was designed to hold conversations with humans and was capable of learning from those interactions.

In the late 2000s, AI chatbots became even more sophisticated, and they were used in a variety of applications. For example, some chatbots were designed to provide financial advice, while others were used for educational purposes. One of the most notable chatbots of this era was Siri, which was introduced in 2011. Siri was a virtual assistant that was available on Apple devices and was capable of answering a wide range of questions.

In recent years, AI chatbots have become even more advanced, and they are now capable of performing a wide range of tasks. For example, some chatbots are designed to help users shop online, while others are used for healthcare purposes. One of the most notable chatbots of this era is Google Assistant, which was introduced in 2016. Google Assistant is a virtual assistant that is available on a wide range of devices and is capable of answering a wide range of questions.

Since 2018, there has been a significant advancement in the evolution of AI chatbots, particularly with the development of large language models (LLMs). LLMs are neural network-based models that use deep learning algorithms to process natural language inputs and generate relevant outputs. These models can learn from large amounts of data and can generate human-like responses with a high degree of accuracy. Some of the notable LLM-based chatbots that have been developed in recent years include BERT, OpenAI, Meta’s LLaMA, and Google’s BARD.

BERT, or Bidirectional Encoder Representations from Transformers, is a language model developed by Google. BERT uses a transformer-based neural network architecture to process natural language inputs and generate relevant outputs. It has been trained on large amounts of data and can perform a wide range of natural language processing tasks, including sentiment analysis, question answering, and text classification. BERT has been used to develop several AI chatbots, including conversational agents for customer service and virtual assistants for smart homes.

OpenAI, a research organization focused on advancing AI technology, has developed several LLM-based chatbots, including GPT-2 and GPT-3. GPT-2 is a language model that can generate human-like responses to natural language inputs. It has been trained on a massive corpus of data and can perform a wide range of natural language processing tasks, including language translation, summarization, and question answering. GPT-3 is an even more advanced language model that has been trained on a massive corpus of data and can generate highly accurate and human-like responses to natural language inputs. It has been used to develop several AI chatbots, including conversational agents for customer service and virtual assistants for smart homes.

Meta’s LLaMA, or Language Learning Multi-Agent, is an AI chatbot developed by Meta, a subsidiary of Facebook. LLaMA is a multi-agent chatbot that uses LLMs to generate responses to natural language inputs. It can learn from interactions with humans and can generate highly accurate and human-like responses to natural language inputs. LLaMA has been used to develop several conversational agents for customer service and virtual assistants for smart homes.

Google’s BARD, or Biologically inspired Neural Chatbot, is an AI chatbot that uses a neural network architecture inspired by the human brain to generate responses to natural language inputs. BARD can learn from interactions with humans and can generate highly accurate and human-like responses to natural language inputs. It has been used to develop several conversational agents for customer service and virtual assistants for smart homes.

In conclusion, AI chatbots have come a long way since the creation of ELIZA in the 1960s. But the real growth of AI chatbots and assistants is being observed significantly in recent years, particularly, with the development of large language models. These models can learn from large amounts of data and can generate human-like responses with a high degree of accuracy. With continued advancements in AI technology, it is likely that chatbots will become even more intelligent and capable in the years to come and we can expect to see even more sophisticated chatbots in the future.

References:

• Weizenbaum, Joseph. “ELIZA—a computer program for the study of natural language communication between man and machine.” Communications of the ACM 9.1 (1966): 36-45.
• Wallace, Richard, and Noah Wardrip-Fruin. “The enduring legacy of ELIZA.” Communications of the ACM 59.9 (2016): 16-19.
• Damer, Bruce, and John Nolan. “Virtual assistants for the home: A survey of current and emerging technologies.” IEEE Consumer Electronics Magazine 4.2 (2015): 91-96.
• Collier, Nigel, et al. “Language technologies for the challenges of the digital age.” Communications of the ACM 61.4 (2018): 67-79.
• Pandey, Siddharth, et al. “Evolution of chatbots in customer service: A review of literature.” Journal of Business Research 108 (2020): 1-15.
• O’Keeffe, Emer, et al. “A review of chatbots in education: What can we learn from them?” Journal of Educational Technology & Society 23.1 (2020): 1-14.
• Google. “The Google Assistant: Your own personal Google.” https://assistant.google.com/
• Apple. “Siri.” https://www.apple.com/siri/
• SmarterChild. “SmarterChild: The virtual friend who could talk to you on AIM.” https://www.theverge.com/2017/3/14/14924730/smarterchild-aol-instant-messenger-aim-retrospective
• “The History and Evolution of Chatbots” by Hootsuite: https://blog.hootsuite.com/the-history-and-evolution-of-chatbots/
• “A Brief History of Chatbots” by HubSpot: https://blog.hubspot.com/service/history-of-chatbots
• “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding” by Jacob Devlin et al. (2018): https://arxiv.org/abs/1810.04805
• “GPT-2: Language Models are Unsupervised Multitask Learners” by Alec Radford et al. (2019): https://openai.com/blog/better-language-models/
• “GPT-3: Language Models are Few-Shot Learners” by Tom Brown et al. (2020): https://arxiv.org/abs/2005.14165
• “LLaMA: A Multi-Agent Reinforcement Learning Framework for Multi-Turn Multi-Agent Natural Language Processing” by Lianhui Qin et al. (2019): https://www.aclweb.org/anthology/D19-1008.pdf
• “BARD: Biologically inspired neural chatbot” by Zhou Yu et al. (2017): https://arxiv.org/abs/1709.09808
• “AI Chatbots: What They Are and How They’re Being Used” by Forbes: https://www.forbes.com/sites/forbestechcouncil/2021/05/20/ai-chatbots-what-they-are-and-how-theyre-being-used/?sh=5d5f5e663d2c
• “AI Chatbots: What They Are and How They’re Being Used” by Forbes: https://www.forbes.com/sites/forbestechcouncil/2021/05/20/ai-chatbots-what-they-are-and-how-theyre-being-used/?sh=5d5f5e663d2c
• “AI Chatbots: What They Are and How They’re Being Used” by Forbes: https://www.forbes.com/sites/forbestechcouncil/2021/05/20/ai-chatbots-what-they-are-and-how-theyre-being-used/?sh=5d5f5e663d2c
• “Responsible AI: The Advantages of Ethical Chatbots” by Entrepreneur: https://www.entrepreneur.com/article/365191
• “The Benefits and Risks of AI Chatbots” by Forbes: https://www.forbes.com/sites/forbestechcouncil/2021/01/21/the-benefits-and-risks-of-ai-chatbots/?sh=2dbd8a0310cd
• “Ethical Considerations for AI Chatbots” by Chatbots Magazine: https://chatbotsmagazine.com/ethical-considerations-for-ai-chatbots-9bb540219891

Financial Calculations in R

In this tutorial, we will, first, begin with learning basic concepts if interests and compounding in R.

Try this –  “Calculate the future value of $100 one year, two years and five years from now assuming an interest rate (or opportunity cost) of 10%.”

Instructions (to do it in R):

• We first create a variable pv (present value, in dollars) and assign it with a value of 100.
• Then we create another variable r (interest rate or opportunity cost) of 0.10 (i.e. 10%).
• Using simple interest, we then calculate the fv (future value) of $100 one year from now using pv and r. Let’s store the result in to another variable, say,fv1 (representing future value in 1 year).
• Using the above method, let’s calculate the fv (future value) of $100 in two years from now  and store the result into fv2.
• Finally, let’s calculate for the five years and store it into fv5.

R Code:

# create present value vector
pv <- 100

# create rate of interest (or opportunity lost) vector
r <- 0.10  # 10%

# calculate future value in one year, fv1
fv1 <- pv * (1 + r)

# calculate future value in two years, fv2
fv2 <- pv * (1 + r)^2

# calculate future value in five years, fv5
fv2 <- pv * (1 + r)^5

Next,

we will calculate the PV (present value) of a given future value (i.e. fv1, fv2 and fv5 above) for a given r (interest rate or opportunity cost) of 10%, where fv1 is a future value of dollar 100 in one year from now and so forth.

The objects fv1, fv2, fv5, and r, that generated in the above exercise will be used.

# Calculate Present Value pv1
pv1 <- fv1 / (1 + r)

# Calculate Present Value pv2
pv2 <- fv2 / ((1 + r)^2)

# Calculate Present Value pv5
pv2 <- fv2 / ((1 + r)^5)

# Print pv1, pv2 and pv5
pv1
pv2
pv5

In the next set of exercises, we’ll build a cash flow data frame for further analysis.

Suppose Emmy is considering investing in a bond that has a $100 par (or face) value. Bond pays 5% coupon rate annually and it has 5 years to maturity (i.e. the time when bond expires/matures). The coupon rate is the interest that you earn from holding the bond, so prior to the bond’s maturity you would receive $5 of coupon payments each year. At maturity, you will also receive the $100 par (face) value back.

Now, we will create a vector cf that lays out this bond’s cash flows. We will then convert this vector into a data frame, so you can add additional columns of data required in subsequent analyses.