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What is Google BERT?

Google BERT is a cutting-edge AI language model that is now being applied to search results to provide a more contextually accurate result. This complex model gives Google the power to analyze every word in a search query concerning all of the other words and helps it to better understand the context around the search. Previously, Google would only process words one-by-one in order, but BERT takes the whole sentence into account, including prepositions and context clues that can change the search’s meaning. This natural language processing (NLP) and natural language understanding (NLU) allows for search results to be catered around the context of the search phrase. Google estimates that BERT will have an effect on about 10% of American searches and that the language model within it can also understand languages other than English, meaning its effect will only become wider. As many now search for information using natural language, BERT provides a more complete understanding to produce the desired results.

Is BERT created by Google?

In 2018, Google made a breakthrough in natural language understanding with the introduction of BERT. Its unique characteristics such as tackling challenges such as ambiguity, semantic role labeling, context analysis, and more, have enabled it to rival human “common sense” understanding. In October 2019, Google announced that it would deploy BERT to its production search algorithms for the United States. BERT distinguished itself from word2vec and GloVe, as it understands context and polysemous words. This breakthrough enables the algorithm to correctly interpret words with multiple meanings. Moreover, it is capable of sensing the most challenging of obstacles, ambiguity. Natural language comprehension scientists express the complexity of the task as being the foremost challenge. Given the tremendous successes of BERT, it was no surprise that Google would apply the framework to their search algorithms. Utilizing this technology will certainly add invaluable information on what users are after when making inquiries. It remains to be seen whether or not the BERT-powered algorithms will fulfill the high expectations Google has for the technology.

What is BERT used for?

• Bert is a powerful language processing tool that provides wide capabilities to solve many language tasks:

Sentiment Analysis

• Sentiment Analysis can be used to assess how positive or negative reviews are.

Question Answering

• Question Answering aids chatbots in responding to user queries.

Text Prediction

• Gmail has used the Text Prediction feature to accurately suggest the next text.

Text Generation

• Text Generation can craft articles from just a few sentences.

Summarization

• Summarization helps contract documents take up less space.

Polysemy resolution

• Polysemy resolution allows for differentiating words with multiple definitions based on context.

What data is BERT trained on?

The BERT framework underwent extensive training on vast textual sources such as Wikipedia, which contained approximately 2.5 billion words, and the Google Books Corpus, comprising around 800 million words. These expansive and informative datasets equipped BERT with profound knowledge of not only the English language but also our world’s intricacies. The training process for BERT was a time-consuming endeavor that became feasible due to the revolutionary Transformer architecture and the utilization of Tensor Processing Units (TPUs). These TPUs are custom circuits devised by Google explicitly for handling large-scale ML models. Employing approximately 64 TPUs, BERT’s training duration amounted to roughly four days, a remarkable achievement given the immense scale of the task. Initially, Google introduced two versions of BERT: BERTlarge and the comparatively smaller BERTbase. While BERTbase exhibited slightly lower accuracy, it remained on par with other cutting-edge models in terms of performance. This ensured that BERT, in its various iterations, represented a significant advancement in language understanding and processing capabilities.

Why do we need BERT?

Effective language representation plays a pivotal role in enabling machines to comprehend language comprehensively. Traditional models like word2vec or GloVe produce a solitary word embedding for each word in their vocabulary, disregarding contextual nuances. This means that a word like “Rock” would have the same representation in both “Rock Music” and “River Rock.” Conversely, contextual models generate word representations based on the surrounding words in a sentence. BERT, as a contextual model, adeptly captures these intricate relationships in a bidirectional manner. BERT’s development draws inspiration from a range of pre-training techniques, such as Semi-supervised Sequence Learning, Generative Pre-Training, ELMo, the OpenAI Transformer, ULMFit, and the Transformer. While these models are primarily unidirectional or shallowly bidirectional, BERT stands out as a fully bidirectional framework. By employing diverse methodologies and innovative ideas, BERT emerges as a cutting-edge approach to achieving comprehensive language understanding. Its contextual nature allows for a more nuanced and accurate representation of words, enhancing the overall performance and versatility of language processing models.

Disadvantages of BERT

The size of BERT, while advantageous in enhancing prediction and learning capabilities through extensive training on large datasets, also brings forth certain limitations. These drawbacks are inherently tied to its scale and encompass the following:

Large Size

• Primarily, the model’s large size can be attributed to its training structure and the corpus used for training.

Slow Training Process

• Due to its magnitude and numerous weights that require updating, BERT’s training process is notably slow.

Implementation Cost

• Moreover, the sheer size of BERT necessitates substantial computational resources, resulting in higher costs for implementation.

It is essential to note that BERT is specifically designed as an input for other systems rather than a standalone program. Consequently, fine-tuning becomes imperative for downstream tasks, which can be a meticulous and intricate process.

In summary, the drawbacks associated with BERT primarily stem from its considerable size. Despite the manifold benefits it offers, such as improved prediction and learning capabilities, the trade-offs include slower training speed, higher computational requirements, increased costs, and the need for meticulous fine-tuning when integrated into other systems. Awareness of these limitations is crucial in determining the optimal implementation of BERT for specific use cases.

How does ChatGPT improve productivity?

ChatGPT improves your productivity by increasing your capabilities at certain stages of the work process.

Specifically, the brainstorming and planning parts of your work can be significantly enhanced by ChatGPT (GPT-4).

How Do You Leverage ChatGPT for Productivity?

One of the great things about using ChatGPT to be more productive is that you can start small and simple.

We recommend starting by asking ChatGPT for ideas for good prompts to put in about a single goal or objective you have.  Explain the situation as if you were explaining it to a consultant.  For example, give as much detail as you think is relevant.  Putting in 5 paragraphs about your objective will be helpful in providing better and more relevant outputs from the AI models.

To put things more broadly, ChatGPT (using GPT-4) is excellent at brainstorming and planning.

What can you use ChatGPT To-Do?

ChatGPT can do an incredibly wide variety of things for you.

It tends to be more relevant for knowledge work and white collar jobs…it even can be amazing at doing high skilled work like software development, data analysis, and healthcare.

Here are some more examples of what you can do with it:

1. Answer Questions: ChatGPT is able to answer a wide range of questions on various topics based on its pre-existing knowledge base.
2. Compose and Edit Text: You can use ChatGPT to draft emails, write essays, create stories, generate ideas for content, and even check grammar.
3. Learning and Education: It can assist with explaining complex concepts, providing examples, and answering questions in various fields, thereby supporting your learning process.
4. Brainstorming: ChatGPT can help generate ideas for projects, solve problems creatively, or provide alternative perspectives.
5. Language Learning: It can help you practice foreign languages by providing translations and facilitating conversation practice.
6. Programming Help: ChatGPT can assist in generating code snippets, explaining coding concepts, and even troubleshooting simple errors.
7. Advice and Recommendations: It can provide advice on a variety of topics, such as productivity tips, travel recommendations, book suggestions, and more.
8. Role-playing Scenarios: You can use ChatGPT for role-playing exercises, such as practicing a speech, a job interview, or a sales pitch.
9. Wellness and Mindfulness: It can provide guided meditation scripts, relaxation techniques, and general wellness advice.

Remember, while ChatGPT can handle a wide range of topics, it has limitations. It doesn’t know personal data unless shared in the course of the conversation (and it’s advised to not share sensitive personal information), it can’t access or retrieve personal data from databases or the internet (actually, it can now do so via some of the new ChatGPT plugins), and its responses are generated based on a mixture of licensed data, data created by human trainers, and publicly available data. As of its last update in September 2021, it doesn’t have the ability to learn or remember new information from interactions.

https://www.youtube.com/watch?v=w-3CHq28gHs

What are the Best Tips to Increase Productivity?

Our biggest tip for using ChatGPT to improve productivity is to identify tasks you have to do where you think ChatGPT could help.  We think you should identify these tasks by looking at which tasks can help with brainstorming, planning, or text generation.

Here are some more ChatGPT Productivity Tips:

1. Set Specific Goals: Setting clear and achievable goals can provide a road map for your tasks. Use SMART goals – Specific, Measurable, Achievable, Relevant, and Time-bound.
2. Prioritize Tasks: Not all tasks are of equal importance. Prioritize your tasks based on their relevance and importance. The Eisenhower Box is a good tool for this.
3. Break Big Tasks into Smaller Ones: Large tasks can seem daunting and hard to start. Break them into smaller, manageable parts to reduce overwhelm.
4. Time Management: Use time management techniques like the Pomodoro technique (25 minutes of focused work, 5-minute break) to ensure you stay focused and avoid burnout.
5. Eliminate Distractions: Identify what commonly distracts you and find ways to minimize these distractions when you’re working.
6. Use Tools and Apps: There are many tools and apps designed to increase productivity, like task management apps, note-taking apps, and apps that block distracting websites.
7. Delegate: If you’re in a position to do so, delegate tasks to others. You can’t do everything yourself, and it’s important to let go of the need to control every task.
8. Maintain a Healthy Lifestyle: Regular exercise, a healthy diet, and plenty of sleep can significantly impact your energy levels and ability to focus.
9. Take Breaks: Working non-stop doesn’t equate to high productivity. Taking short breaks can refresh your mind and keep you energized.
10. Practice Mindfulness and Meditation: These practices can improve focus, creativity, and stress management, leading to increased productivity.
11. Continuous Learning: Always look for ways to improve your skills and knowledge. This can make you more efficient and open up new ways to get work done.
12. Establish a Routine: Routines can help us build positive habits that can lead to increased productivity.
13. Learn to Say No: Overcommitting can lead to poor performance and stress. Learn to say no to tasks and activities that are not important or do not align with your goals.
14. Stay Organized: Keep your workspace and your tasks organized. This can prevent time wasted looking for resources or deciding what to work on.
15. Use Positive Reinforcement: Reward yourself when you complete tasks. This can be as simple as taking a short break, having a snack, or doing something you enjoy.

Remember, different techniques work for different people. The key to increasing productivity is to try out different methods and find what works best for you.

Does ChatGPT Improve Productivity?

Yes, ChatGPT can indeed be a tool to improve productivity in various ways:

1. Information and Research: ChatGPT can provide information on a wide range of topics quickly, helping you reduce the time spent on online searches.
2. Task Management: It can assist you with setting reminders, creating to-do lists, or even providing strategies for task management.
3. Drafting and Editing: It can assist in drafting emails, reports, or other types of written content. It can also help review text for grammar or style issues.
4. Brainstorming: ChatGPT can help generate ideas or provide alternative perspectives on problems, which can be useful in brainstorming sessions.
5. Learning and Education: It can assist with explaining complex concepts in simple language, providing examples, and answering questions in various fields, thereby supporting your learning process.
6. Practice and Role-play: You can practice presentations, negotiations, or other scenarios with ChatGPT as your partner.
7. Mental Well-being: ChatGPT can provide stress-relief techniques, meditation guidance, and general advice for maintaining mental wellness, which indirectly contributes to productivity.

It’s important to note, however, that while ChatGPT can assist in these areas, it is a tool, and its effectiveness largely depends on how you use it. Just like any other tool, it works best when used appropriately and in conjunction with good productivity habits and techniques.

Can ChatGPT Help with Sales?

Yes, ChatGPT can indeed be a valuable tool in the sales process.

Here are some ideas:

1. Lead Generation: ChatGPT can help with ideas and strategies for finding and attracting potential clients.
2. Email Drafting: It can assist in drafting and refining sales emails or messages, helping to create clear, compelling communication.
3. Sales Scripts: It can assist in creating or improving sales scripts, incorporating best practices and persuasive techniques to help increase success rates.
4. Handling Objections: ChatGPT can offer advice on how to respond to common objections, providing salespeople with well-crafted responses.
5. Sales Training: It can be used for role-play scenarios to practice sales conversations and techniques, which can be particularly helpful for new salespeople.
6. Market Research: ChatGPT can provide information on effective market research methods and can synthesize simple data to support market understanding.
7. CRM Notes: It can help with the creation of CRM notes, summaries, and updates after client interactions.
8. Strategy Development: You can use it to brainstorm sales strategies or discuss ways to improve sales performance.

Remember, while ChatGPT is a powerful tool, it’s not a substitute for human judgment, especially when it comes to understanding and responding to the unique needs and context of individual customers. Always use it as a tool to augment your sales process, not to replace the human touch that’s essential in sales.

What is OpenAI Whisper?

Whisper, the prodigious automatic speech recognition (ASR) wizard, is flexing its muscles in the arena of speech processing.

With the wind of a staggering 680,000 hours of global, multitask web data in its sails, it is remarkably resistant to the hurdles of accents, background hubbub, and the quirks of tech jargon.

Beyond its transcription expertise in a multilingual context, it has a knack for translating from an array of languages into English. A grand giveaway, we’re rolling out our models and inference code to spark inspiration, stimulate practical implementations, and fuel ongoing speech processing research.

How does OpenAI Whisper work?

Painting the Whisper Masterpiece

Whisper’s blueprint is uncomplicated yet effective, embracing the famed encoder-decoder Transformer design. It all starts with an audio clip, divided into 30-second fragments, which are then turned into a log-Mel spectrogram. The encoder seizes these fragments while a decoder is on a mission to divine the corresponding text caption. Our star model is guided by unique tokens, choreographing tasks such as identifying languages, establishing phrase-level timestamps, transcribing multilingual speech, and performing to-English speech translation.

The Training Method Behind the Maestro

Contrary to many existing strategies, Whisper doesn’t limit itself to smaller, tightly-coupled audio-text training datasets, nor does it adhere to the trend of unsupervised audio pretraining. Having been trained on a generous and diverse dataset without any specific fine-tuning, Whisper doesn’t top the charts on LibriSpeech performance, a renowned speech recognition benchmark. Nevertheless, its adaptability shines when it’s thrown into the wild, demonstrating versatility across numerous diverse datasets and slashing errors by half compared to the conventional models.

Whisper’s Melting Pot of Languages

About one-third of Whisper’s training diet consists of non-English audio, providing it with a rich multilingual banquet. It is alternatively tasked with transcribing in the original language or translating into English. This approach has proven especially potent for learning speech-to-text translation, outstripping supervised state-of-the-art results on CoVoST2 to English translation tasks, and managing to achieve this without any prior task-specific training.

How do I Use OpenAI’s Whisper?

Currently, you need to use Whsiper through OpenAI’s API.

Click here to view OpenAI’s Whisper documentation to learn how to use the API.

How Much is Whisper OpenAI?

Whisper costs 6 cents per 10 minutes of transcriptions.

Is Whisper OpenAI Free?

No, whisper costs 6 cents per 10 minutes transcribed.

Is OpenAI Whisper Open Source?

Yes, Whisper is open source!

Did Whisper Get Shut Down?

No, it is still available via OpenAI’s API

Can Whisper AI do Text to Speech?

No, Whisper only does speech to text, not the other way around, text to speech.

How do AI Text Generators Work?

AI Copy writers, AKA AI Text Generators, work primarily the same way as Large Language Models that are text in and text out.

The services you can purchase to generate AI content often have some additional layers upon the core LLMs, so there cane be differences between 2 different services even if they are both using OpenAI’s API on the backend.

AI text generators work based on a machine learning architecture known as transformers, specifically the transformer decoder.

Here’s a simplified explanation of the process:

Training:

These models are trained on vast amounts of text data. They learn to predict the next word in a sequence of words. During training, they adjust millions of internal parameters to minimize the difference between their predictions and the actual words in the text.

Language Understanding:

As they train, they begin to pick up on patterns in the language, including grammar, punctuation, and even style and tone. More advanced models also begin to understand context, themes, and even some factual information.

Tokenization:

When you give the model a prompt, it first breaks down (tokenizes) the input into smaller pieces, which can be as short as one character or as long as one word (e.g., “ChatGPT” might be a single token).

Contextual Analysis:

The model then considers each token in the context of the ones that came before it. This is where the “transformer” part of the architecture comes in, allowing the model to consider multiple tokens simultaneously and understand how they relate to each other.

Output Generation:

The model generates an output one token at a time. After generating each token, it adds that token to the sequence and uses it as part of the context for generating the next token. This process continues until a maximum length is reached or a stop condition is met.

Fine-tuning:

After initial training, these models can be fine-tuned on specific tasks or styles of text. This involves additional training on a more specific dataset, which helps the model to better generate text in a specific style or better perform a specific task.

It’s important to note that these models do not “understand” text in the same way humans do. They don’t have beliefs, desires, or consciousness. They generate text based on patterns they’ve learned during training, but they don’t have a deep understanding of the content they’re generating.

Which AI Text Generator is the Best?

For a long time, the standard answer was Jasper AI, formerly known as Jarvis.

Nowadays, we don’t think the general consensus is still the same…

…we think it is now a bit more spread out into a larger variety of camps.

In our opinion, ChatGPT with GPT-4 is probably the best, but requires some skill and practice to use well, so it isn’t necessarily the best for all people in all situations.

Here’s a good video on this:

Is there a Free AI Text Generator?

Yes, ChatGPT and others are free.

What is the Top Free AI Writing Generator?

ChatGPT, in our opinion, is the best free AI writer.

What is the AI that can Write Text for You?

Soooo many AI models can write text for you.  ChatGPT, Jasper, and many others are able to do this quite well!

 

What is GPT-6?

GPT-6 is a theoretical OpenAI Model proceeded by GPT-5.  It may or may not come out.

Will there be a GPT-6?

We think it is likely that GPT-6 will eventually come out, but it is also possible that OpenAI will go in a different direction and there will never be a GPT-6.

When will GPT-6 Come Out?

We estimate GPT-6 to be out in 2025 or 2026.

How Powerful will GPT-6 Be?

Umm, gigapowerful seems to be an understatement for how powerful we expect GPT-6 to be.

GPT-4 is impressively powerful.  GPT-5 seems likely to be crazy powerful.  GPT-6 has incredibly high expectations.

Will GPT-6 be AGI?

At this point, it seems likely that GPT-5 will be powerful enough to be considered AGI or be a major part in an AGI.

Therefore, I think we can assume that GPT-6 is even more likely to be so.

However, there is a consideration that GPT-WhateverNumber will NOT be an AGI because an AGI probably needs more pieces to it than a Large Language Model (LLM).

What we Can Expect with GPT-6?

Hopefully, no hallucinations.

Reasoning of a genius.

New capabilities that few, if any, people expected.

The sky is the limit.

Text Summarization

Text summarization is a key aspect of natural language generation. It involves condensing larger bodies of text into shorter, more digestible summaries. For instance, if you’re curious about antibiotics, you could ask the model to explain it to you. The model might provide a detailed explanation, but if you want a more concise summary, you can instruct the model to condense the information into a single sentence.

[text to summarize copied from elsewhere]

Please summarize the above text:

Information Extraction

Language models are also adept at extracting specific information from a given text. For example, if you provide a paragraph and ask the model to identify a specific product mentioned, it can do so accurately.

[text to extract information from copied from elsewhere]

Please identify the key factors in the above report:

Question Answering

The model can also answer specific questions based on a given context. By providing a structured prompt that includes instructions, context, and a question, the model can generate a concise and accurate answer.

[context text]

Please use the above text to answer this question: [Question about above text].

Text Classification

The model can also classify text based on instructions provided in the prompt. For instance, it can classify a given text as neutral, negative, or positive. If you need the model to provide the label in a specific format, you can provide examples in the prompt to guide the model’s output.

[input text]

Please tell me if the above response was positive, neutral, or negative:

Conversation

You can also use the model to generate conversational responses. By providing a role for the model (e.g., an AI research assistant), and specifying the tone of the responses (e.g., technical and scientific), you can guide the model’s behavior in the conversation.

[sample email text]

Please suggest 3 different responses to the above email:

Code Generation

The model can generate code based on instructions provided in the prompt. For example, you can ask the model to write a simple program that greets the user, or a more complex MySQL query based on a given database schema.

Please write a simple program in Python that greets the user and asks how they are doing.

Reasoning

While current language models struggle with tasks that require reasoning, they can perform basic arithmetic tasks. However, for more complex reasoning tasks, you might need to provide more detailed instructions and examples in the prompt.

Riddle: What has to be broken before you can use it?

Can you solve the above riddle?

GPT-4’s response:

The answer to the riddle is “an egg”. An egg has to be broken before you can use it for cooking or eating.

Guide To Prompt Engineering Basics:

Here’s a detailed guide on how to interact with Large Language Models like ChatGPT, GPT-4, Bard, etc:

1. Be Specific: The more specific you are with your questions or prompts, the better the model can generate a relevant response. For example, instead of asking “What’s the weather like?”, you could ask “What’s the weather like in New York City on May 14, 2023?”.
2. Provide Context: If your question or statement refers to something mentioned earlier in the conversation, make sure to include enough context so the model can understand. For example, if you’re asking a follow-up question, briefly summarize what you’re referring to.
3. Use Correct Syntax: Large language models like ChatGPT understand natural language, so you can write to them just like you would write to a human. However, using correct grammar, punctuation, and spelling can help the model understand your input better and generate a more accurate response.
4. Ask for Clarification: If the model’s response is unclear or seems incorrect, don’t hesitate to ask for clarification or more information.
5. Experiment with Different Approaches: If you’re not getting the response you want, try rephrasing your question or providing more information. Different inputs can lead to different outputs.
6. Understand the Model’s Limitations: Remember that while large language models are powerful, they don’t know everything. They don’t have access to real-time or personal data unless it has been shared in the conversation. They also can’t perform tasks outside of generating text, like making phone calls or browsing the internet.
7. Use Tools and Features: Many interfaces for large language models offer additional tools and features. For example, you can use system messages to set the behavior of the assistant, or use the “Wolfram” tool to access dynamic computation and curated data.
8. Respect the Guidelines: Always follow the guidelines provided by the platform you’re using. This includes not using the model to generate inappropriate or harmful content.
9. Give Feedback: If the platform allows it, providing feedback on the model’s responses can help improve its performance over time.
10. Enjoy the Interaction: Lastly, have fun! Interacting with a large language model can be a unique and interesting experience. Enjoy the process of exploring what the model can do.

How Can I be Good at Prompt Engineering?

First of all, study the prompt engineering guide above.

2nd, you can ask GPT-4 this question (this is a great idea, ironically, haha)

Also, we suggest taking a course on prompt engineering.

What are The Basics of Prompt Engineering?

The basics are: be specific, give as much context as you can, and be intentional with the language you use.

Checkout Prompt Engineering Tools

Is prompt engineering hard?

Nah, it is just a new skill for, well, EVERYONE, and anything new can seem overwhelming as you first start to learn it.

Here are some Prompt Engineering Examples to get you started.

How much do AI prompt engineers make?

This varies greatly.  We’ve seen job posts of $300k+/yr, but I wouldn’t bank on this type of income were I you.

How to Make Money with Prompt Engineering?

There are basically unlimited ways to make money with prompt engineering because the applications of LLM’s like GPT-4 span across so many areas of human endeavors…

You can make money with Prompt Engineering by becoming better & faster at your job.

You can make money with Prompt Engineering by improving your business.

You can make money with by getting a prompt engineering job.

…ETC

One idea: