LaMDA

2.Is AI technology here?

• AI technology appears futuristic. Facebook's facial recognition software identifies faces in the photos we post.
• The voice recognition software that translates commands we bark at Alexa.
• The Google translate apps are all examples of AI Tech already around us.
• AI tech today aims to satisfy the Turing test to qualify as intelligent.
• Turing was the designer and builder of the world's first computer, ENIGMA was used to break the German codes during the Second World War. 
• To test if a machine thinks, Turing devised a practical solution.
• Place a computer in a closed room and a human in another. If an interrogator interacting with the machine and the human cannot discriminate between them, then Turing said that the computer should be construed as intelligent.
• The reverse Turing test, CAPTCHA, limits technology access to humans and keeps the bots at bay.

3.Which were the first chatbots to be devised?

• As electronics improved and first-generation computers came about Joseph Weizenbaum of the MIT Artificial Intelligence Laboratory built ELIZA, a computer programme with which users could chat.
• ALICE (Artificial Linguistic Internet Computer Entity), another early chatbot developed by Richard Wallace was capable of simulating human interaction.
• In the 1930s, linguist George Kingsley Zipf analysed the typical human speech and found that most utterances began with 2,000 words.
• Using this information, Wallace theorised that the bulk of commonplace chitchat in everyday interaction was limited.
• He found that just about 40, 000 responses were enough to respond to 95 per cent of what people chatted about.
• With assistance from about 500 volunteers, Wallace continuously improved ALICE's responses repertoire by analysing user chats, making the fake conversions look real.
• The software won the Loebner Prize as "the most human-computer" at the Turing Test contests in 2000, 2001 and 2004.

4.What is a neural network?

• It is an AI tech that attempts to mimic the web of neurons in the brain to learn and behave like humans.
• Early efforts in building neural networks targeted image recognition.
• The artificial neural network (ANN) needs to be trained like a dog before being commanded.
• For example, during the image recognition training, thousands of specific cat images are broken down to pixels and fed into the ANN.
• Using complex algorithms, the ANN's mathematical system extracts particular characteristics like the line that curves from right to left at a certain angle, edges or several lines that merge to form a larger shape from each cat image.
• The software learns to recognise the key patterns that delineate what a general cat looks like from these parameters.
• Early machine learning software needed human assistance.
• The training images had to be labelled as 'cats', 'dogs' and so on by humans before being fed into the system.
• In contrast, access to big data and a powerful processor is enough for the emerging deep learning software.
• The App learns by itself, unsupervised by humans, by sorting and sifting through the massive data and finding the hidden patterns.

5.What is LaMDA?

• It is short for 'Language Model for Dialogue Applications', Google's modern conversational agent enabled with a neural network capable of deep learning. 
• Instead of images of cats and dogs, the algorithm is trained using 1.56 trillion words of public dialogue data and web text on diverse topics.
• The neural network built on Google's open-source neural network, Transformer, extracted more than 137 billion parameters from this massive database of language data.
• The chatbot is not yet public, but users are permitted to interact with it.
• Google claims that LaMDA can make sense of nuanced conversation and engage in a fluid and natural conversation.
• The LaMAD 0.1 was unveiled at Google's annual developer conference in May 2021 and LaMDA 0.2 in 2022.

6.How is LaMDA different from other chatbots?

• Chatbots like "Ask Disha" of the Indian Railway Catering and Tourism Corporation Limited (IRCTC) are routinely used for customer engagement.
• The repertoire of topics and chat responses is narrow.
• The dialogue is predefined and often goal-directed. For instance, try chatting about the weather with Ask Disha or about the Ukrainian crisis with the Amazon chat app.
• LaMDA is Google's answer to the quest for developing a non-goal-directed chatbot that dialogues on various subjects.
• The chatbot would respond the way a family might when they chat over the dinner table.
• Topics meandering from the taste of the food to price rise to bemoaning war in Ukraine.
• Such advanced conversational agents could revolutionise customer interaction and help AI-enabled internet search, Google hopes.

7.AI Intelligent

• The Turing test is a powerful motivator for developing practical AI tools.
• Philosopher John Searle uses the ' Chinese Room Argument to demonstrate that passing the Turing test is inadequate to qualify as intelligent.
• Once I used Google Translate to read Whatsapp messages in French from a conference organiser in France and turn replied to her in French. 
• For some time, she was fooled into thinking that I could speak French.
• I would have passed the ' Turing test', but no same person would claim that I know French.
• This is an example of the Chinese room experiment.
• The imitation game goes only so far. Further scholars point out that AI tech uses a false analogy of learning.
• A baby learns a language from close interaction with caregivers and not by ploughing through a massive amount of language data.
• Moreover, whether intelligence is the same as sentience is a moot question.
• The seemingly human-like conversational agents rely on pattern recognition, not empathy, wit, candour or intent.

8.Is the technology dangerous?

• The challenges of AI metamorphosing into sentient are far in the future.
• The unethical AI perpetuating historical bias and echoing hate speech is the real danger to watch for.
• Imagine an AI software trained with past data to select the most suitable candidates from applicants for a supervisory role.
• Women and marginalised communities hardly would have held such positions in the past, not because they were unqualified, but because they were discriminated against.
• The machine has no bias, AI software learning from historical data could inadvertently perpetuate discrimination.