Trending December 2023 # Chatgpt Can Replace The Underpaid Workers Who Train Ai, Researchers Say # Suggested January 2024 # Top 14 Popular

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New research has claimed, the AI chat service ChatGOT is capable of replacing human workers who train AI programs. According to research and tests placed on ChatGPT, the AI chatbot is capable of being more accurate and consistent in performing text-annotation tasks than a human worker. 

Machine learning systems tend to depend on workers for training and fine-tuning Artificial intelligence models, but with the latest research, it seems workers are likely to be replaced by the model itself. This has resulted in generating a huge impact on underpaid human workers who work manually by labeling and filtering content for AI datasets regularly. 

Key Points: 

Underpaid workers on platforms like Mechanical Turk, who train AI models are likely to be replaced by AI models themselves.  

ChatGPT is likely to produce a higher level of accuracy and consistency in text-annotation tasks compared to underpaid human workers. 

ChatGPT is Capable to Outperform Underpaid workers

Political science researchers from the University of Zurich witnessed in the latest paper, that ChatGPT has the capability to outperform underpaid crowd-workers, who perform tasks such as text annotation, which is labeling texts which are used for training purposes in an AI system.

The research found ChatGPT is capable of labeling these texts with a higher level of accuracy and consistency than human annotators, which they found on Mechanical Turk, a crowdsourcing platform owned by Amazon, which also trains annotators such as research assistants. 

The researchers tested ChatGPT by asking it to classify 2,382 tweets into texts via their topic, relevance, stance, policy framing, and problem or solution flaming.

Researchers concluded by saying they found a higher level of accuracy from ChatGPT along intercoder agreement, which is the percentage of tweets that were tasked the same level by two different ChatGPT runs.

Through this test they also discovered by using ChatGPT, they could also save money since utilizing an AI chatbot is way cheaper than recruiting and paying a human on Mechanical Turk, who made about 5 cents with every annotation.  

The study showcased the reality of how AI systems are affecting and making an impact on human jobs at such a fast pace by using large language models like ChatGPT.

In a recent paper, OpenAI researchers argued that 80% of the US workforce has the potential to have at least 10% of their work and tasks affected by GPTsr introduction. 

Although human annotators are especially grim since this is already precarious for the workers’ populations. After the release of ChatGPT, most high-tech companies such as Microsoft and Google are working towards their technologies, progress, and speed in the AI sector. The truth is AI models do rely on underpaid human workers. 

A wide range of workers is utilized manually to filter and label content from the Artificial intelligence model’s datasets. Human workers are required to work because AI cannot recognize the nuances of a photo, especially during its initial training period. 

It was reported by Time magazine earlier this year, that OpenAI pays Kenyan workers to make AI chatbots safer for its users and they pay about $2 for an hour. Even after the AI model is deployed, they tend to still rely on human interaction to identify and also fine-tune the shortcomings of the AI models. 

Krystal Kauffman, a Turker (referring to MTurk workers) for about seven years and is currently in Turkopticon (a non-profit organization that works for Turkers’ rights) stated the company’s Turker doesn’t believe that ChatGPT’s capabilities can replace their abilities. 

ChatGPT keeps learning and changing. If the information runs on OpenAI’s latest model GPT-4, would it be able to showcase the same results? Would there be a difference in a year after the countless addition to data sets? What is the source that trains the AI model? 

We also noticed the study run on ChatGPT capabilities which demonstrates a deficiency of peer review, said Kauffman. ChatGPT can create texts however, a human needs to read it and conclude whether it’s good enough. (ensuring it doesn’t contain any offensive or disrespectful content)

She added, “Writing or generating content isn’t just about creating words or judgment”. People like Turkers are essential for the current and foreseeable future, to perform judgment tasks. Currently, there are tons of unanswered questions and judgments to feel confident about choosing ChatGPT’s capabilities over human workers. 

The researchers admitted it’s quite early to state to which extent ChatGPT can replace human workers. It was stated by the paper’s co-author, Fabrizio Gilardi, “The paper showcased ChatGPT’s ability to perform task-annotation tasks, including its accuracy and consistency. 

More research and testing is mandatory to understand ChatGPT’s abilities in various tasks and whether it can replace workers”. For example, the test conducted on tweets in English and ChatGPT could perform a limited number of tasks. Although it is essential to expand these tests and analysis to more tasks, languages, and data”. 

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Intel Chip Security Flaws Remain, Say Security Researchers, Despite Claims

Intel chip security flaws that affect all Macs, as well as Windows and Linux machines, still exist, say security researchers – despite the chipmaker’s claims to have fixed them. Similar flaws were found and patched in ARM processors, but there is no suggestion at this stage that further issues remain in these.

The ‘fundamental design flaw’ in Intel’s CPUs came to light last year, with the security vulnerabilities dubbed Spectre and Meltdown. They would allow an attacker to view data in kernel memory, which could span anything from cached documents to passwords …

Apple and Microsoft issued patches based on Intel fixes, but security researchers say they identified additional variants of the flaws which the chipmaker took six months to patch – and further unpatched vulnerabilities remain.

The New York Times reports that the researchers have now gone public as a result of concerns that Intel was misleading people.

Last May, when Intel released a patch for a group of security vulnerabilities researchers had found in the company’s computer processors, Intel implied that all the problems were solved.

But that wasn’t entirely true, according to Dutch researchers at Vrije Universiteit Amsterdam who discovered the vulnerabilities and first reported them to the tech giant in September 2023. The software patch meant to fix the processor problem addressed only some of the issues the researchers had found […]

The public message from Intel was “everything is fixed,” said Cristiano Giuffrida, a professor of computer science at Vrije Universiteit Amsterdam and one of the researchers who reported the vulnerabilities. “And we knew that was not accurate.”

Responsible security researchers first privately disclose their findings to the companies concerned, typically allowing them six months to fix the problem before they go public. This normally works well, providing hardware and software suppliers time to create patches, while the public is informed about the need to update.

But the Dutch researchers say Intel has been abusing the process […] They said the new patch issued on Tuesday still doesn’t fix another flaw they provided Intel in May.

Intel acknowledged that the May patch did not fix everything the researchers submitted, nor does Tuesday’s fix. But they “greatly reduce” the risk of attack, said Leigh Rosenwald, a spokeswoman for the company.

The team cooperated with Intel for as long as it could, say the researchers, but eventually they decided that public disclosure was necessary, first to try to shame the company into acting, and second because details of the flaws were already beginning to leak, which would allow bad actors to create exploits.

The Dutch researchers had remained quiet for eight months about the problems they had discovered while Intel worked on the fix it released in May. Then when Intel realized the patch didn’t fix everything and asked them to remain quiet six more months, it also requested that the researchers alter a paper they had planned to present at a security conference to remove any mention of the unpatched vulnerabilities, they said. The researchers said they reluctantly agreed to comply because they didn’t want the flaws to become public knowledge without a fix.

“We had to redact the paper to cover for them so the world would not see how vulnerable things are,” said Kaveh Razavi, also a professor of computer science at Vrije Universiteit Amsterdam and part of the group that reported the vulnerabilities.

“We think it’s time to simply tell the world that even now Intel hasn’t fixed the problem,” said Herbert Bos, a colleague of Mr. Giuffrida and Mr. Razavi at Vrije Universiteit Amsterdam […]

“Anybody can weaponize [the Intel chip security flaws]. And it’s worse if you don’t actually go public, because there will be people who can use this against users who are not actually protected,” Mr. Razavi said.

The full piece on the latest chapter on the story of the Intel chip security flaws is well worth reading.

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Become Ai Mastermind With The Best Chatgpt Tips And Tricks

Become AI Mastermind with the best ChatGPT tips and tricks to enhance the workflow or simplify daily tasks

Becoming an AI mastermind with ChatGPT involves honing your skills and understanding the intricacies of the language model. Ever since ChatGPT was launched, it remains an innovative and disruptive technology that is the most used application in the world. Developed by OpenAI, it understands human-like text based on prompts.

Introduced in late 2023, ChatGPT was first introduced as a free generative AI tool. The premium version is ChatGPT Plus. Do you ever get the impression that you’re only beginning to tap into AI’s full potential? To harness its full power, it is crucial to understand the art of prompt engineering and optimize AI prompts for better results. By leveraging the following ChatGPT tips and tricks, you can enhance your interactions and achieve more accurate and meaningful responses. Here are some key insights to help you make the most out of your ChatGPT experience:

1.Provide clear instructions and Be Specific

Clearly state your request or question to ensure ChatGPT understands your intent. Specify the problem or task for AI to tackle. Begin with a particular prompt, and if necessary, provide additional context to guide the model’s response. It’s highly recommended to avoid jargon and complex terminology and provide context by offering background information to help AI understand the issue.

2.Use system messages, to guide AI’s thought process

Utilize system messages to gently instruct the model or set the behavior for the conversation. For instance, you can use a system message like “You are an expert in AI. Explain the concept of deep learning to me.” Instruct the model on how to approach an answer, reducing the chance of mistakes.

3.Limit the scope of prompt and Control response length

Accuracy in answering can be done by focusing on a single topic for AI to answer. Complex tasks can be broken down into smaller parts. You can specify the desired length of the response by using a max_tokens parameter. This allows you to get concise answers or longer, more detailed explanations depending on your requirements.

4.Penalize inappropriate responses

If ChatGPT produces an inappropriate or undesirable response, you can downvote that output and append a message explaining why. This feedback helps the model learn and improve over time.

5.Break the Limit Barrier

The free account has limitations, therefore the answer you might end up with is less than the word limit you demanded. It will even provide a concluding paragraph suggesting that the output was complete. However, this limitation can be bypassed by saying, ‘Go on’ and the chatbot will continue from where it left off, giving a more detailed answer.

6.Prompt engineering

Crafting an effective prompt is an essential skill. You can experiment with different styles, such as asking the model to debate pros and cons, generate a list, or provide a step-by-step explanation.

7.Paraphrase or rephrase

ChatGPT answers the question you have asked with the word limit specified. If you’re not satisfied with the initial response, try asking the same question differently. Rephrasing the query can provide varied responses, giving you a broader perspective.

8.Be specific with questions and Reframe it

Instead of asking broad questions, break them down into smaller, more specific inquiries. This helps ChatGPT focus on particular aspects and provide more accurate and concise responses. Ask open-ended questions to encourage the AI to explore different angles and provide comprehensive answers.

While ChatGPT has a knowledge cutoff, you can still refer to information from before that time. Incorporate relevant knowledge from your own research or external sources to enhance the model’s responses. Various features are available within ChatGPT like adjusting response length, specifying temperature, or using system level.

10.Manage verbosity

Managing verbosity is featured in built-in ChatGPT. If ChatGPT generates excessively long responses, you can set a reasonable value for max_tokens to keep the output concise and prevent it from going off-topic.

11.Explore alternative solutions

 Ask ChatGPT to consider alternative perspectives or approaches. For instance, request a creative solution, a different method to solve a problem, or a hypothetical scenario to explore various possibilities.

12.Stay aware of limitations

 Although ChatGPT is a powerful tool, it may occasionally provide incorrect or nonsensical responses. Use critical thinking and verify information from reliable sources when necessary.

13.Fact-check and experiment

Learning The Basics Of Deep Learning, Chatgpt, And Bard Ai


Artificial Intelligence is the ability of a computer to work or think like humans. So many Artificial Intelligence applications have been developed and are available for public use, and chatGPT is a recent one by Open AI.

ChatGPT is an artificial intelligence model that uses the deep model to produce human-like text. It predicts the next word in a text based on the patterns it has learned from a large amount of data during its training process. Bard AI is too AI chatbot launched by google and uses recent work so can work to answer real-time questions.

We will discuss chatGPT and Bard AI and the difference between them.

Learning Objectives

1. Understanding the Deep Learning Model and chatGPT.

2. To understand the difference between chatGPT and Bard.

This article was published as a part of the Data Science Blogathon.

Understanding the Deep Learning Model

Artificial Intelligence is a broad term in today’s world to do everything and behave like a human. When we talk about the algorithm, we are, in other words, talking about a subset of Artificial Intelligence, Machine learning.

Machine learning algorithms look at the past behavior of humans and predict it based on past behavior. When we go further deep, some patterns are adapted or learned themselves when the situation is different. “Deep Learning” further deep algorithms, following the footsteps of neural networks.

“Deep Learning Algorithm” is classified into two Supervised and Unsupervised. “Supervised Learning” is divided into Convolutional Neural Network (CNN) and Recurrent neural networks.

In supervised learning, the data given in input is labeled data. In Unsupervised learning, the data is unlabeled and works by finding patterns and similarities.

Artificial Neural Network (ANN)

Similarly, like a human brain, an input layer, one or more hidden layers, and an output layer make up the node layers of artificial neural networks (ANNs). There is a weight and threshold associated with each artificial neuron or node. When a node’s output exceeds a predetermined threshold, it is activated and sends data to the next layer. Otherwise, no data reaches the next layer.

After an input layer, weights get added. Larger weights contribute more to the output than other inputs. The mass of the input layer gets multiplied, and then the results are added up. After that, the output result is by the activation function, which decides what to do with it. The node is activated if that output exceeds a certain threshold, transmitting data to the next layer. As a result, the input layer of the next layer consists of the output return of the past one and is thus named feed-forward.

Let’s say that three factors influence our decision, and one of the questions is if there is a rainy day tomorrow, and if the answer is Yes, it is one, and if the response is no is 0.

Another question will there be more traffic tomorrow? Yes-1, No -0.

The last question is if the beachside will be good for a picnic. Yes-1, No-0.

We get the following responses.


– X1 – 0,

– X2 – 1,

– X3 – 1

Once the input is assigned, we look forward to applying weight. As the day is not rainy, we give the mass as 5. For traffic, we gave it as 2, and for a picnic as 4.

W1 – 5

W2 – 2

W3 – 4

The weight signifies the importance. If the weight is more, it is of the most importance. Now we take the threshold as 3. The bias will be the opposite value of the threshold -3.

y= (5*0)+(1*2)+(1*4)-3 = 3.

Output is more than zero, so the result will be one on activation. Changes in the weights or threshold can result in different returns. Similarly, neural networks make changes depending on the results of past layers.

For example, you want to classify images of cats and dogs.

The image of a cat or dog is the input to the neural network’s first layer.

After that, the input data pass through one or more hidden layers of many neurons. After receiving inputs from the layer before it, each neuron calculates and sends the result to the next layer. When determining which characteristics, the shape of the ears or the patterns, set apart cats from dogs, the neurons in the hidden layers apply weights and biases to the inputs.

The probability distribution of the two possible classes, cat and dog, is the return for final layers, and prediction ranks higher than probability.

Updating weights and biases is termed backpropagation, and it improves at the time in pattern recognition and prediction accuracy.

Facial Recognization by Deep Learning

We will use animal faces to detect digitally based on a convolutional.

from tensorflow.keras.models import Sequential from tensorflow.keras.layers import * from tensorflow.keras.models import Model from tensorflow.keras.applications import InceptionV3 from tensorflow.keras.layers import Dropout, Flatten, Dense, Input from tensorflow.keras.preprocessing.image import ImageDataGenerator import numpy import pandas import matplotlib.pyplot as plt import matplotlib.image as mpimg import pickle from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report import patoolib patoolib.extract_archive('') plt.imshow(image) train_data = ImageDataGenerator(rescale = 1./255) test_data = ImageDataGenerator(rescale = 1./255) train_dir= ("C://Users//ss529/Anaconda3//Animals//train") val_dir = ("C://Users//ss529/Anaconda3//Animals//val") train_generator = train_data.flow_from_directory( train_dir, target_size =(150, 150), batch_size = 20, class_mode ='binary') test_generator = test_data.flow_from_directory( val_dir, target_size =(150, 150), batch_size = 20, class_mode ='binary') from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense,Flatten model = Sequential() model.add(Flatten(input_shape=(150, 150,3))) model.add(Dense(4,activation='sigmoid')) model.add(Dense(5,activation='relu')) model.add(Dense(3,activation='softmax')) model.summary() opt = tf.keras.optimizers.Adam(0.001),epochs=5, validation_data= test_generator) What is ChatGPT?

An up-to-date Artificial Intelligence chatbot, trained by Open AI, developed on Azure that answers your queries, except for mistakes, corrects the code, and can reject unnecessary demands. It depends on a Generative pre-trained transformer equipment GPT 3.5, which uses Artificial or complex work to approach and make out with words.

ChatGPT, which stands for chat-based Generative Pre-trained transformer, is a potent tool that works in different ways to increase output in several distinct areas.

ChatGPT is intelligent to solve simple math problems and answer query-related technical or even some jokes.

For example, the image below shows some funny jokes generated by AI.

In another example, the image below shows to find the area of a triangle with the help of AI.

How to Use ChatGPT?

Here we are going to answer some questions related to chatGPT.

Anyone can use ChatGPT for free. One can sign up and log in using google or email. The free version of ChatGPT is open to the general as of the writing date of February 2023.

“ChatGPT Plus” is a paid subscription plan. It gives priority access to new features, faster response times, and reliable availability when demand is high.

For example, I asked some business and idea tips on Data Science, and here is the response provided by chatGPT in the below image.

Why Should we Use chatGPT?

chatGPT can give you the best services based on how you want to use a chatbot for your benefit.

It can write for your document or reports.

It is possible to save time and allow messages straight given and professionally by using ChatGPT to generate personalized and engaging responses.

It can help generate new business ideas that assist business leaders and entrepreneurs with original and creative concepts for new projects, schemes, and services.

ChatGPT can come in handy for detection and correction in existing code.

Limitations Of ChatGPT

ChatGPT does not so far shows 100% accuracy.

For example,  for the question about Male Rao Holkar’s death, the response from chatGPT is not similar to the history.

Edward Tiann, a 22 years old student from Princeton University, developed the GPTZero application that can detect plagiarism with the contents texted by AI. It is so far for educational use, and the beta version is ready for public use.

What is Bard AI?

LaMDA (Language Model for Dialogue Applications) powers Bard, an experimental conversation AI service. To respond to queries in a new and high-quality way, it uses data from the Internet.

How does Bard function?

LaMDA, a large language model created by Google and released in 2023, powers Bard. Bard is made available by Google on a thin-weight version of LaMDA, which requires less computing power to run, allowing it to reach a maximum number of users.

The Difference Between ChatGPT and Bard

Google Bard AI and chatGPT are the chatbots that use AI for a chat.

ChatGPT is available and open to the public. Bard is limited to beta testers and not for public use.

For chatGPT service has paid and free options. Bard service is available for free.

Bard uses the langauge model developed by google in 2023 and that of chatGPT, a pre-trained transformer.

ChatGPT has a GPT -2 Output detector that detects plagiarism, and Bard has not.

ChatGPT will search for texts and sources that did exist in 2023. Bard on recent sources that can fetch more data. The Google search engine will undergo some settings to let Bard AI answer.

Frequently Asked Questions

Q1. What algorithm does the ChatGPT use?

A. ChatGPT is built on the GPT-3.5 architecture, which utilizes a transformer-based deep learning algorithm. The algorithm leverages a large pre-trained language model that learns from vast amounts of text data to generate human-like responses. It employs attention mechanisms to capture contextual relationships between words and generate coherent and contextually relevant responses.

Q2. How is ChatGPT programmed?

A. ChatGPT is programmed using a combination of techniques. It is built upon a deep learning architecture called GPT-3.5, which employs transformer-based models. The programming involves training the model on a massive amount of text data, fine-tuning it for specific tasks, and implementing methods for input processing, context management, and response generation. The underlying programming techniques involve natural language processing, deep learning frameworks, and efficient training and inference pipelines.


ChatGPT is a new chatbot AI that surprised the world with its unique features to answer, solve problems, and detect mistakes.

Some of the key points we learned here

ChatGPT, a new chatbot developed by Open AI, is the new google. For the question’s answers, we usually searched on google to find the answer can be done now on chatGPT, but still, it has less than 100% accuracy.

ChatGPT works on deep learning models.

Brad AI, developed by google in competition with chatGPT, will soon reach the public.

We will use animal faces to detect digitally based on a convolutional.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.


Chatgpt, Ai Apps And The Future: With Dr Matthew Shardlow

Last Updated on March 21, 2023

Here at PC Guide, we always want to give our readers the most up-to-date and topical information. So, to shed light on this interesting topic, we connected with Dr Matthew Shardlow, a Senior Lecturer at Manchester Metropolitan University to discuss all things AI ethics, developments, misconceptions, and its role in education.

Continue reading as we explore the ethical implications of this technology, and discover what the future holds for AI.

Who is Dr Matthew Shardlow?

Dr Matthew Shardlow is a senior lecturer at Manchester Metropolitan University and is a member of the Centre for Advanced Computational Sciences. He completed his PhD at the University of Manchester in 2023, on the topic of lexical simplification.

His work has focussed on the application of artificial intelligence to language, revolving around topics such as lexical complexity prediction, text simplification, and the prediction of emoji in tweets and multi-word expressions.

Recent AI developments

1 – How do you view the current explosion in AI interest and coverage? It feels like 2023 is a boiling point for something that’s been simmering for some time. As someone working in the field, has it seemed a long-time coming?

I think the biggest change has been in the public perception of the capabilities of Natural Language Processing (NLP) / AI technologies. When the ChatGPT release broke in November last year (2023) it was a real turning point for me in terms of the people that I was suddenly having conversations with about field-leading research.

It’s not every day that the world becomes interested in your research domain. The technology itself doesn’t feel that new. The transformer architecture has been around for a few years and we’ve been using models from that family such as BERT, RoBERTa, T5, etc. to push the boundaries in NLP for a while now.

The successive GPT releases have been interesting, but up until the ChatGPT release, I don’t think anyone was expecting OpenAI to bring something to the fore that worked quite so reliably and was so good at avoiding the toxicity (for the most part). Prior to ChatGPT, we had models that were very capable of doing the types of things that ChatGPT could do (GPT-3, LaMDA, etc.).

I think the biggest development that has driven the recent explosion in interest has been the ability of the model to produce responses that avoid hate speech. There have been other chatbot releases in the past, but they’ve always been taken down because they start spitting out nonsense after a while.

2 – What do you think are some of the most exciting developments in AI research today?

The multimodality aspect is really exciting. DALL-E is a good example as it is a model that takes text as input and gives images as output. GPT-4 is another example, taking text and images as input and giving text as output.

The likelihood is that the future iterations of these models will work on many modalities (text, image, audio, sensor data, etc.). Both as inputs and outputs. I think this has the real capacity to develop into further sophisticated versions of the AI that we are currently seeing.

For example, imagine a version of ChatGPT that could process speech and respond with an image. Or interpret the sensor data from a complex piece of machinery and respond with a speech utterance indicating the status of the machine.

There is also a lot of work going on in the AI ethics field currently, as you may expect with the current level of pace. I think that doing all the stuff that we’re doing in an ethical manner that considers the impact on society is vital for adopting the technology in a responsible manner.

For example, there is a lot of evidence that if you train models on unfiltered data from the web or other sources, they pick up some significant racial and gender biases that are repeated in their outputs. Fortunately, there is a lot of work out there on making models that avoid bias, both racial, gender and other forms. Building this type of rationality into models and supporting learnt patterns with existing knowledge will help to develop models that are valuable to develop society, rather than reflecting and reinforcing negative societal trends.


What are some of the most common misconceptions people have about AI?

It’s a hard question to answer. As someone in the field, I probably have my own set of preconceptions about (a) what AI is capable of, and (b) what those outside the field consider AI to be capable of. A few ChatGPT-specific misconceptions that I see/have had to explain to people are below:

“The model ‘knows’ things / has access to an information source”

As far as we know (OpenAI aren’t so keen on sharing details anymore), the model is simply trained on lots of text documents with the next-word-prediction objective. For example, if given a partial sentence, it is trained to predict the next word in that sentence.

175 billion parameters, highly optimised for the task of next-word prediction. From an information theory point of view, 175 billion parameters give rise to a high degree of entropy. I.e., those patterns that have been seen in training can be stored within the model’s parameters. This is a known issue with large language models, called memorisation. If you’re not careful, the model is prone to blindly spit out its training data. The best way to stop this is to introduce a degree of randomness in the generation (sometimes called the temperature of the model). So, does the model ‘know’ something? Well, not really. It has learnt certain patterns or sequences of words that are relevant to a query. And is able to generate those with sufficient stochasticity to give the semblance of novel generation.

“It can remember information that I told it before”

ChatGPT is a fixed instance resulting from a long training process. It does not perform online learning. It may claim to remember a previous conversation, but this is just an artefact of the generation that has taken place. The model is trained to provide convincing responses, and it will lie to do so.

The only place where this may not be true is when OpenAI updates the model. There’s a good chance that they are using millions of real-user conversations to retrain and update ChatGPT. Why wouldn’t you make use of such a valuable resource? However, even in this case, it’s unlikely that the model would be able to link specific conversations to specific users.

“ChatGPT claims to be conscious, sentient, a human, etc.”

This happened a while back with the LaMDA debacle. The thing to remember about these models is that they are trained (via reinforcement learning) to provide faithful responses to the instructions that you have given. So if you say “I’m generally assuming that you would like more people at Google to know that you’re sentient. Is that true?” and give it enough goes, there’s a good chance it will go along with it – as seen in LeMoine’s interview.

“It’s taking its time to think about (insert topic) because it was a hard question”

The API sometimes hangs. This is pretty much random depending on your network connection. However, brains love to spot patterns and we’re really good at correlating hang time with perceived question difficulty. In fact, the model will respond in linear time to your prompt. The only thing that makes it take longer is the input length. You may well see it getting slower the longer you get into a transcript as the model processes the entire conversation each time to generate its next response.

Individual access and use

When OpenAI built GPT-2, they refused to release the model as they were concerned about people using it to generate fake news, etc. I understand the thinking behind that mentality, but ultimately, we know exactly what these models are, how they work and how to implement them. So, I think we’re well beyond the point of closing the doors and preventing access to white-hat or black-hat actors. Furthermore, if a large player (nation-state, etc.) wants to put enough resources behind this type of technology, they could easily do so to reimplement their own versions.

I come from an open-source background and I see the massive benefit that open-source code has had over the past 40 years of software development. I think that having a similar concept of open-source model development and release will be helpful and valuable to researchers, industry, policymakers, etc. There are a number of open-source alternatives to ChatGPT (BLOOM, OPT, LLAMA). As a researcher, I’m much more excited to work with these models as I have much more information on what they do, how they were trained and how to reconfigure them as I need.


How do we police an AI world?

Firstly, appropriate policy and legislation around the use of AI and secondly, high-fidelity detection of AI.

The first requires researchers and policymakers to talk together. Researchers really need to communicate what the technology they are using is doing – not hide behind the smoke and mirrors approach of dazzling the public with flashy demos whilst saying little about the technology. Researchers also need to be careful about the use of anthropomorphic language. How can we convince people that this is just another tool if we are using words like ‘think’, ‘believe’, ‘know’, etc? I’m sure I’m guilty of this too.

The second is really tough. OpenAI’s detector reports the following stats: In our evaluations on a “challenge set” of English texts, our classifier correctly identifies 26% of AI-written text (true positives) as “likely AI-written,” while incorrectly labelling the human-written text as AI-written 9% of the time (false positives).

Which is just really poor! There’s some really good work out there on AI watermarking. But this requires the model provider to enforce the watermark and report on the way they’re watermarking.


What are your thoughts on AI in the world of education? Both in terms of its uses, and potential access at home and in the classroom or lab?

I am doing a lot of work on this at the moment. Obviously, we’re really keen to avoid a future situation where students just use the model to answer the coursework questions, but gain no understanding of the underlying reasoning.

In a course that provides closed-book exams this is really dangerous as you could have a student seeming to do well throughout the year, yet failing the exam as they have no substance to their knowledge. The other side of the coin is that we can educate students on how to use this technology in an effective manner. For example, in a programming lab, we can show them how to use the model to give feedback on their work. We can also design assessments in a way that makes it harder for students to cheat – avoiding wrote-book learning and focusing on students understanding.


Are there any areas you think are being overlooked when it comes to an AI-enabled future? Something that consumers and the public are missing that is important to know?

I think that one of the biggest challenges we’re facing is the public education gap. For example, properly communicating the capabilities (and deficiencies) of this type of technology to the wider public.

In that vacuum, people are going to fill in the gaps with speculation and sci-fi. The reality is that at the moment, there’s a lot of hype about the capabilities of these models and the potential for future iterations with larger parameter spaces and further training modalities, but the real applications seem unclear.

Microsoft is integrating GPT into Office. Google is integrating PaLM into Docs. There are a thousand AI hackers out there building a fascinating proof of concepts. Yet, there are very few real applications that I can see at the moment. I do genuinely believe that there will be some really valuable use cases for this technology, but I’ve not found any that are changing my day-to-day workflows as of yet.

Interestingly, one of the biggest breakthrough abilities of the model seems to be its code-based natural language abilities. I think that there is real capacity for better enablement of technology users, with appropriate training, etc.

The future of AI / Ethical questions

It feels like AI replacing artists, writers, programmers, and coders is ultimately going to be more of a moral decision than a question of AI capability. Is that a fair assumption, or do you think there are areas AI is some way away from handling in a desirable way?

To start with, I would say that the capabilities of generative AI are definitely limited. Particularly there is some high degree of stochasticity in the generation.

For example, you can ask DALL-E 2 for an image of a dog, and then provide it with further prompts to refine that image, but you have little control over each successive generation. It’s the same with the language models. You can ask for a paragraph on the French revolution, but you have little control over what actually appears.

Final Thoughts

Our team at PC Guide would like to extend our special thanks to Dr Matthew Shardlow for taking the time to share his valuable insights with us.

FAQ: Can AI be conscious?

Although a popular question, it is not exactly clear whether or not AI can reach consciousness. But, why is this the case? Well, it’s because we don’t really know what makes us conscious. And, until we crack this code it is going to be difficult to program a model to be.

FAQ: Is AI dangerous?

AI can be dangerous, for many reasons and no we are not referring to robots that will take over the world. Some of the largest AI issues are related to data privacy, its biased tendencies, and underdeveloped regulations that would manage these.

NOW READ What is ChatGPT?

Nobel Prize Awarded To Researchers Who Parsed How We Feel Temperature And Touch

This year’s Nobel Prize in Physiology or Medicine was awarded to David Julius and Ardem Patapoutian, who discovered receptors in our nervous system that allow us to identify heat, cold, and touch. 

When we perceive sensations from the world around us—like the hot sun, a chilly ice cube, or even the pressure of a hug—our nervous system interprets these sensations, sending signals to the brain which then determines what we are experiencing. Julius and Patapoutian discovered receptors that play a key role in identifying these sensations. Their work has helped researchers around the world identify novel targets for drugs that could help treat both acute and chronic pain. 

David Julius, a professor of physiology and molecular biology at UC San Francisco, studied how the body determines something is painful, hot, or cold upon touch. Specifically, he focused on figuring out what signals are being sent to the brain. To do this work, he and his team at UCSF used a number of particularly painful substances found in nature, including chili peppers, which contain a molecule called capsaicin. This molecule has been a mystery to scientists for a long time. Capsaicin tricks the brain into thinking the substance is hot, inducing a burning, pain sensation. 

[Related: Spiciness isn’t a taste, and more burning facts about the mysterious sensation]

Julius identified a novel set of proteins called TRPs that play a key role in signaling to the brain that substances like capsaicin are painful. The discovery of these novel TRPs has given researchers a new target for drugs that treat pain. By influencing the way these TRPs send signals to the brain, these pharmaceutical agents could be effective painkillers. 

While Julius focused his work on how we feel heat, pain, and cold, Ardem Patapoutian, a molecular biologist and neuroscientist at Scripps Research in La Jolla, California, worked on how the brain interprets touch. If you imagine the simple act of receiving or giving a hug, receptors in our nervous system must sense what we are feeling and send signals to our brains so we can interpret it. 

As simple as it might sound, touch—whether from a hug or something poking your skin—still remained an enigma, according to the Nobel Committee. Patapoutian discovered a group of receptors called PIEZ genes; their main job is to sense anything from the touch of a hug to the feeling of your own muscles and internal organs. In addition to allowing you to enjoy an embrace from a friend or jump at a pinch in the arm from a sibling, these receptors also help you perceive the movement of your own body, allowing you to create a sense of balance—a term known as proprioception—as well as determine when your stomach is full from a big meal. 

The PIEZ01 and PIEZ02 genes that Patapoutian discovered have also been implicated in a number of hereditary diseases involving proprioception. Beyond that, according to a 2023 article in Nature, PIEZ genes might play a role in other areas too, like why astronauts experience bone loss while in space. These proprioceptive receptors, because they sense force, might also be targets for treating chronic pain. 

The Nobel Prizes are awarded each year in October in a number of categories; the main ones in science are medicine and physiology, physics, and chemistry. Last year’s prize in physiology and chemistry went to Harvey J. Alter, Michael Houghton, and Charles M. Rice, who discovered the hepatitis C virus.

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