Industry 4.0 revolution - Artificial Intelligence, Chatbots and beyond
Kaaustubh K.
Business & Digital Transformation | Open Banking | CBDC | SWIFT MT & MX | SEPA | Buna Payment | Ripple | RegTech | Fintech | PSD2 | ISO 20022 l Kay’s Transformation Principle - ADAPC
We are in the age of Industry 4.0 revolution, which talks about :
“Conceptualizes rapid change to technology, industries, and societal patterns and processes in the 21st century due to increasing interconnectivity and smart?automationâ€.
Industry 4.0 summarizes with the four themes:
- Theme 1 (Interconnection) – the ability of machines, devices, sensors, and people to connect and communicate with each other via the Internet of things, or the internet of people (IoP).
- Theme 2 (Information transparency) – the transparency afforded by Industry 4.0 technology provides operators with comprehensive information to make decisions. Inter-connectivity allows operators to collect immense amounts of data and information from all points in the manufacturing process, identify key areas that can benefit from improvement to increase functionality.
- Theme 3 (Technical assistance) – the technological facility of systems to assist humans in decision-making and problem-solving, and the ability to help humans with difficult or unsafe tasks.
- Theme 4 (Decentralized decisions) – the ability of cyber physical systems to make decisions on their own and to perform their tasks as autonomously as possible. Only in the case of exceptions, interference, or conflicting goals, are tasks delegated to a higher level.
?As part of Industry 4.0 revolution, the rapid progress will be happened in the area of Artificial intelligence.
As per Encyclopedia Britannica, Artificial intelligence (AI), the ability of a digital?computer?or computer-controlled?robot?to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the?intellectual?processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience.
Currently we are having three trendy advanced conceptualization AI representation, i.e. Chat GPT from OpenAI, Lambda from Google and BlenderBot from Meta.
LaMDA: The Language Model for Dialog Applications (LaMDA) is a Transformer-based neural language model consisting of up to 137B parameters and pre-trained on 1.56T words of publicly available dialogue data and web documents. In addition, the model is fine-tuned on three metrics:?Quality,?Safety, and?Groundedness.?LaMDA’s progress is quantified by collecting responses from the pre-trained model, fine-tuned model, and human raters (i.e., human-generated responses) to multi-turn two-author dialogues—the responses are then evaluated by a different set of human raters on a series of questions against the above defined metrics.?It uses a combination of neural networks and machine learning techniques, such as supervised learning and reinforcement learning, to analyze and understand large volumes of text data. The system is trained using a large dataset of human-generated text, and can then generate new text based on this analysis using a process known as text generation. The LaMDA AI platform is presently not accessible to the general public and is only accessible to a select number of AI developers via the AI Test Kitchen (https://aitestkitchen.withgoogle.com/). LaMDA has an advantage on different metrics features which generates answers for different themes. For instance, the groundedness metric validates the solutions based on reliable outside sources. Similarly, the quality meter evaluates replies according to sensibleness, specificity, and interest (SSI) criteria. In other words, it ensures the answers are non-generic, make sense in the context of the question, and are also wise, surprising, or humorous.
LaMDA was trained in dialogue, the same as its predecessor, Meena, another conversational tech that Google presented in 2020. Meena proved that chatbots could talk about virtually anything. It was trained to minimize a training objective they called perplexity, a measure of how confident is a model in predicting the next token. They found perplexity correlates very well with human evaluation metrics such as the SSA — sensibleness and specificity average — which is very useful to evaluate the quality of the chatbots.
However, LaMDA went a step further. It excels at detecting sensibleness - whether a sentence makes sense in the context of a conversation - and is better able to keep its responses specific. As the authors note in their post, a response like “I don’t know†could be always sensible, but very useless nevertheless.
ChatGPT: ?ChatGPT, a text-based chatbot with artificial intelligence was made available by OpenAI in November 22. It is a sibling model to InstructGPT, which is trained to follow an instruction in a prompt and provide a detailed response. Trained this model using Reinforcement Learning from Human Feedback (RLHF), using the same methods as InstructGPT, but with slight differences in the data collection setup.
Similar to LaMDA, ChatGPT uses a supervised-learning model, where human AI trainers are given access to model suggestions to craft responses and train the model playing both sides—the user and AI assistant. Following which, the trainers ranked the responses from the chatbot’s conversation with them as well as the sampled alternative completions based on quality.?ChatGPT is based on the?GPT - 3.5 architecture, having 175B parameters. The GPT-3.5 series consists of three models:?code-davinci-002, the base model for code completion tasks,?text-davinci-002, which is trained by supervised fine-tuning on human-written demonstration and samples rated 7/7 by human labelers on overall quality scores, and the most recently released?text-davinci-003, the new and improved version that includes reinforcement learning with human feedback (RLHF), a reward-based model trained on comparisons by humans. The training data is a mixture of text and code from before Q4 2021.?
One of the more interesting aspects about OpenAI’s model is that the GPT-3.5 architecture uses a reinforcement learning model (RLHF), a reward-based mechanism based on human feedback, thereby making it better and better. LaMDA, on the other hand, doesn’t use RLHF.
"To create a reward model for reinforcement learning, we needed to collect comparison data, which consisted of two or more model responses ranked by quality. To collect this data, we took conversations that AI trainers had with the chatbot. We randomly selected a model-written message, sampled several alternative completions, and had AI trainers rank them. Using these reward models, we can fine-tune the model using Proximal Policy Optimization and performed several iterations of this process." ?ChatGPT and GPT 3.5 were trained on an Azure AI supercomputing infrastructure
BlenderBot: Meta also has its own chatbot called?Blenderbot, the third iteration of which was released a few months ago. The conversational AI prototype is based on 175B parameters, and has its own long-term memory. The model uses dialogue history, the internet, and memory to produce output. Meta and Google have been keeping information about their chatbots increasingly under the wraps, but we can expect them to make an announcement when they are completely ready—especially, considering what Google had to go through last time they released prematurely.?Unfortunately, BlenderBot is US-only at the moment (https://geo-not-available.blenderbot.ai/). As per their FAQ section, we can translate the below pointers for the concise understanding.
Currently the bot is designed for English-only conversations. However, multilingual AI is a large and active research area for Meta, and have made some exciting progress in the space.?The bot is based on a large language model trained on publicly available text data. The bot supplements its knowledge by accessing the internet live (via an API with our partners,?Mojeek) and by accessing its long-term memory that summarizes historical conversations you’ve had with the bot. You can see and reset these memories when the “look inside†link appears in your chat. You can also click on a message to see more information about how it was constructed, including search queries that were made by the bot.
Teaching the bot to Blend skills needed to perform multiple conversational tasks made for better performance, instead of training the bot to learn one skill at a time. Hence it’s called as Blender Bot. Some features which are with this initiative are as below:
1: If the bot says something offensive, the user should report the message by clicking the “thumbs down†beside the message and selecting “Rude or Inappropriate†as the reason for the dislike. We will use this feedback to improve future iterations of the bot.
2: The bot can make false or contradictory statements. Users should not rely on this bot for factual information, including but not limited to medical, legal, or financial advice.
3: Reduce or minimize the offensive content - as per them, “We have conducted extensive research on dialogue safety and made attempts to reduce the possibility that our bot engages in conversations that reflect demographic bias or stereotypes. We have also worked to minimize the bots’ use of vulgar language, slurs, and culturally insensitive commentsâ€
We can further get some insights from their blogs for the same – https://ai.facebook.com/blog/blenderbot-3-a-175b-parameter-publicly-available-chatbot-that-improves-its-skills-and-safety-over-time
Comparison:
Scale AI’s?Riley Goodside?compares the responses generated from ChatGPT and LaMDA—calling the former as an “unlovable C-3POâ€â€”with the responses reading almost like a Q&A platform, compared to the latter which is friendly and, in real terms, “conversationalâ€. This can be directly correlated to the fact that LaMDA is trained on dialogues, whereas ChatGPT is said to be highly trained on web texts.?Further, OpenAI’s conversational AI is also described by many to be producing shallow content,?almost as if regurgitated from Wikipedia. However, the AI has also been receiving much flak for various things including producing incorrect information, fake quotes, and non-existing references.?
But, LaMDA, on the other hand, carries an edge in this context because of the various metrics it produces in its responses. For example, the groundedness metric verifies the responses based on authoritative external sources. Similarly, the quality metric measures responses based on dimensions like?Sensibleness,?Specificity, and?Interestingness?(SSI). That is, it ensures that the responses make sense in the context they are asked, are non-generic, and are also insightful, unexpected or witty.?Good side also adds that while one can prompt-inject ChatGPT into behaving the way however one likes, this would mean that users put their own disclaimer accepting that they understand that it’s not real. If not, users will be talking to the protocol droid.?
Top 10 points for Google Lambda and OpenAI ChatGPT
1.??????Training methods: Google Lambda is trained using a combination of supervised learning and reinforcement learning, while OpenAI ChatGPT is trained using a large, pre-trained language model called GPT-3.
2.??????Training data: Google Lambda is trained on a large dataset of human-generated text, while OpenAI ChatGPT is trained on a dataset of billions of words.
3.??????Performance: Google Lambda tends to generate higher-quality, more natural-sounding text, while OpenAI ChatGPT is better suited for real-time language processing tasks such as chatbot development.
4.??????Customization: Google Lambda can be customized for specific language processing tasks, but it requires a large dataset and a significant amount of training time. OpenAI ChatGPT can be easily fine-tuned for specific tasks or applications by adjusting the training data and model architecture.
5.??????Use cases: Google Lambda is suitable for a wide range of language processing tasks, including language translation and language generation. OpenAI ChatGPT is specifically designed for chatbot development and other real-time language processing tasks.
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6.??????Language support: Google Lambda is trained on a variety of languages and can be used for language translation and generation in multiple languages. OpenAI ChatGPT is primarily designed for English language processing and may not perform as well on other languages.
7.??????Integration: Google Lambda can be integrated with other Google products and services, such as Google Translate and Google Assistant. OpenAI ChatGPT can be easily integrated with chat platforms and other real-time communication systems.
8.??????Cost: Google Lambda is a proprietary system and the cost of using it will depend on the specific terms of your contract with Google. OpenAI ChatGPT is available as a cloud-based service and pricing is based on usage.
9.??????Data privacy: Google Lambda processes a large amount of user data and may have access to personal information. OpenAI ChatGPT processes data on a smaller scale and has privacy measures in place to protect user data.
10.???Ethical considerations: Both Google Lambda and OpenAI ChatGPT raise ethical concerns around the potential for misuse or abuse of language processing technology. However, Google has faced criticism in the past for its handling of user data and privacy, while OpenAI has made efforts to address ethical concerns in the development and use of its technology.
So, which system is the better choice? Ultimately, it depends on the specific needs of your application. If you're looking to generate high-quality, natural-sounding text, Google Lambda is probably the better option. However, if you're looking to develop a chatbot or other real-time language processing system, OpenAI ChatGPT is the way to go.
Conclusion:
LaMDA is the next big thing in conversational AIs. We’ll have to test it ourselves to see the degree to which it appears to be human.
The potential for something like OpenAI’s ChatGPT to eventually supplant a search engine like Google isn’t a new idea, but this delivery of OpenAI’s underlying technology is the closest approximation yet to how that would actually work in a fully fleshed out system, and it should have Google scared.
But, the gold rush in generative AI will be driven by developing novel, defensible businesses built around how it shows up, less so than what’s under the hood.
Finally, the below statistics will depict some of the long-term prospect and its impact on overall revenues across all the business areas in the globe.
1)??????Juniper Research says that?using chatbots in businesses saves 2.5 billion hours?which means along with the profit in monetary value, time is also saved.
2)??????$8 billion is estimated to be saved in 2023?by businesses and retail stores by reducing chat support costs.
3)??????55% of businesses gather more sales?when chatbots are used, a study by?Drift.
4)??????MIT Technology?strongly believes that?90% of complaints are tackled using chatbots instantly?if the query is explained properly.
5)??????80% of organizations are searching for ways to implement chatbots?into their service somehow, says?CCW Digital.
6)??????Gartner?has an estimation that?70% of white-collar employees use chatbots to get done with daily tasks?and projects efficiently without any lagging.
7)??????Mobile Marketer?shares that?40% of millennials talk to chatbots for deals, offers, and upcoming discounts?on the desired store or a specific product.
8)??????The healthcare sector provided by 10%?instantly after incorporating chatbots, says?Chatbot Life.
9)??????As a replacement for potential chat support workers,?chatbots save up to 30% in businesses in total costs?by diminishing chat support according to?IBM.
10)??Juniper Research?calculated that the?eCommerce market will hit $112 sales by using chatbots?for multiple leveraging factors and notifications.
Reference:
https://en.wikipedia.org/wiki/Fourth_Industrial_Revolution