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100 ChatGPT terms explained from NLP to Entity Extraction

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If you are interested in learning more about ChatGPT and artificial intelligence put together a quick introductory list of 100 ChatGPT terms explained in just a few sentences. Allowing you to easily grasp its application and research it more thoroughly if required. Here are some terms that are often used in discussions, papers and documentation relating to ChatGPT and similar AI models. Don’t forget to bookmark this glossary of terms or link to it for future reference.

100 ChatGPT terms explained :

  1. Natural Language Processing (NLP): This is the field of study that focuses on the interaction between computers and humans through natural language. The goal of NLP is to read, decipher, understand, and make sense of human language in a valuable way.
  2. Artificial Intelligence (AI): AI refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions.
  3. Machine Learning (ML): ML is a type of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
  4. Transformers: This is a type of ML model introduced in a paper titled “Attention is All You Need”. Transformers have been particularly effective in NLP tasks, and the GPT models (including ChatGPT) are based on the Transformer architecture.
  5. Attention Mechanism: In the context of ML, attention mechanisms help models focus on specific aspects of the input data. They are a key part of Transformer models.
  6. Fine-tuning: This is a process of taking a pre-trained model (like GPT) and training it further on a specific task. In the case of ChatGPT, it’s fine-tuned on a dataset of conversations.
  7. Tokenization: In NLP, tokenization is the process of breaking down text into words, phrases, symbols, or other meaningful elements called tokens.
  8. Sequence-to-Sequence Models: These are types of ML models that transform an input sequence into an output sequence. ChatGPT can be viewed as a kind of sequence-to-sequence model, where the input sequence is a conversation history and the output sequence is the model’s response.
  9. Function Calling: In the context of programming, a function call is the process of invoking a function that has been previously defined. In the context of AI like ChatGPT, function calling can refer to using the model’s “generate” or “complete” functions to produce a response.
  10. API: An API, or Application Programming Interface, is a set of rules and protocols for building and interacting with software applications. OpenAI provides an API that developers can use to interact with ChatGPT.
  11. Prompt Engineering: This refers to the practice of crafting effective prompts to get the desired output from language models like GPT.
  12. Context Window: This refers to the number of recent tokens (input and output) that the model considers when generating a response.
  13. Deep Learning: This is a subfield of ML that focuses on algorithms inspired by the structure and function of the brain, called artificial neural networks.
  14. Neural Networks: In AI, these are computing systems with interconnected nodes, inspired by biological neural networks, which constitute the brain of living beings.
  15. BERT (Bidirectional Encoder Representations from Transformers): This is a Transformer-based machine learning technique for NLP tasks developed by Google. Unlike GPT, BERT is bidirectional, making it ideal for tasks that require understanding context from both the left and the right of a word.
  16. Supervised Learning: This is a type of machine learning where the model is trained on a labelled dataset, i.e., a dataset where the correct output is known.
  17. Unsupervised Learning: In contrast to supervised learning, unsupervised learning involves training a model on a dataset where the correct output is not known.
  18. Semi-Supervised Learning: This is a machine learning approach where a small amount of the data is labelled, and the large majority is unlabelled. This approach combines aspects of both supervised and unsupervised learning.
  19. Reinforcement Learning: This is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a goal. The agent receives rewards or penalties for the actions it takes, and it learns to maximize the total reward over time.
  20. Generative Models: These are models that can generate new data instances that resemble the training data. ChatGPT is an example of a generative model.
  21. Discriminative Models: In contrast to generative models, discriminative models learn the boundary between classes in the training data. They are typically used for classification tasks.
  22. Backpropagation: This is a method used in artificial neural networks to calculate the gradient of the loss function with respect to the weights in the network.
  23. Loss Function: In ML, this is a method of evaluating how well a specific algorithm models the given data. If the predictions deviate too much from the actual results, loss function would cough up a very large number. It’s used during the training phase to update the weights.
  24. Overfitting: This happens when a statistical model or ML algorithm captures the noise of the data. It occurs when the model is too complex relative to the amount and noise of the training data.
  25. Underfitting: This is the opposite of overfitting. It occurs when the model is too simple to capture the underlying structure of the data.
  26. Regularization: This is a technique used to prevent overfitting by adding a penalty term to the loss function.
  27. Hyperparameters: These are the parameters of the learning algorithm itself, not derived through training, that need to be set before training starts.
  28. Epoch: One complete pass through the entire training dataset.
  29. Batch Size: The number of training examples in one forward/backward pass (one epoch consists of multiple batches).
  30. Learning Rate: This is a hyperparameter that determines the step size at each iteration while moving toward a minimum of a loss function.
  31. Activation Function: In a neural network, the activation function determines whether a neuron should be activated or not by calculating the weighted sum and adding bias.
  32. ReLU (Rectified Linear Unit): This is a type of activation function that is used in the hidden layers of a neural network. It outputs the input directly if it is positive, else, it will output zero.
  33. Sigmoid Function: This is an activation function that maps34. Softmax Function: This is an activation function used in the output layer of a neural network for multi-class classification problems. It converts a vector of numbers into a vector of probabilities, where the probabilities sum up to one.
  34. Bias and Variance: Bias is error due to erroneous or overly simplistic assumptions in the learning algorithm. Variance is error due to too much complexity in the learning algorithm.
  35. Bias Node: In neural networks, a bias node is an additional neuron added to each pre-output layer that stores the value of one.
  36. Gradient Descent: This is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient.
  37. Stochastic Gradient Descent (SGD): This is a variant of gradient descent, where instead of using the entire data set to compute the gradient at each step, you use only one example.
  38. Adam Optimizer: Adam is a replacement optimization algorithm for stochastic gradient descent for training deep learning models.
  39. Data Augmentation: This is a strategy that enables practitioners to significantly increase the diversity of data available for training models, without actually collecting new data.
  40. Transfer Learning: This is a research problem in ML that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem.
  41. Multilayer Perceptron (MLP): This is a class of feedforward artificial neural network that consists of at least three layers of nodes: an input layer, a hidden layer, and an output layer.
  42. Convolutional Neural Networks (CNNs): These are deep learning algorithms that can process structured grid data like an image, and are used in image recognition and processing.
  43. Recurrent Neural Networks (RNNs): These are a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows them to use their internal state (memory) to process sequences of inputs.
  44. Long Short-Term Memory (LSTM): This is a special kind of RNN, capable of learning long-term dependencies, and is used in deep learning because of its promising performance.
  45. Encoder-Decoder Structure: This is a type of neural network design pattern. In an encoder-decoder structure, the encoder processes the input data and the decoder takes the output of the encoder and produces the final output.
  46. Word Embedding: This is the collective name for a set of language modelling and feature learning techniques in NLP where words or phrases from the vocabulary are mapped to vectors of real numbers.
  47. Embedding Layer: This is a layer in a neural network that turns positive integers (indexes) into dense vectors of fixed size, typically used to find word embeddings.
  48. Beam Search: This is a heuristic search algorithm that explores a graph by expanding the most promising node in a limited set.
  49. Temperature (in the context of AI models): This is a parameter in language models like GPT-3 that controls the randomness of predictions by scaling the logits before applying softmax.
  50. Autoregressive Models: This is a type of random process where future values are a linear function of its past values, plus some noise term. ChatGPT is an example of an autoregressive model.
  51. Zero-Shot Learning: This refers to the ability of a machine learning model to understand and act upon tasks that it has not seen during training.
  52. One-Shot Learning: This is a concept in machine learning where the learning algorithm is required to classify objects based on a single example of each new class.
  53. Few-Shot Learning: This55. Language Model: A type of model used in NLP that can predict the next word in a sequence given the words that precede it.
  54. Perplexity: A metric used to judge the quality of a language model. Lower perplexity values indicate better language model performance.
  55. Named Entity Recognition (NER): An NLP task that identifies named entities in text, such as names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc.
  56. Sentiment Analysis: An NLP task that determines the emotional tone behind words to gain an understanding of the attitudes, opinions, and emotions of a speaker or writer.
  57. Dialog Systems: Systems that can converse with human users in natural language. ChatGPT is an example of a dialog system.
  58. Seq2Seq Models: Models that convert sequences from one domain (e.g., sentences in English) to sequences in another domain (e.g., the same sentences translated to French).
  59. Data Annotation: The process of labelling or categorizing data, often used to create training data for machine learning models.
  60. Pre-training: The first phase in training large language models like GPT-3, where the model learns to predict the next word in a sentence. This phase is unsupervised and uses a large corpus of text.
  61. Knowledge Distillation: A process where a smaller model is trained to reproduce the behaviour of a larger model (or an ensemble of models), with the aim of creating a model with comparable predictive performance but lower computational complexity.
  62. Capsule Networks (CapsNets): A type of artificial neural network that can better model hierarchical relationships, and are better suited to tasks that require understanding of spatial hierarchies between features.
  63. Bidirectional LSTM (BiLSTM): A variation of the LSTM that can improve model performance on sequence classification problems.
  64. Attention Models: Models that can focus on specific information to improve the results of complex tasks.
  65. Self-Attention: A method in attention models where the model checks each word in the input sequence for all the other words to better understand their impact on the sentence.
  66. Transformer Models: Models that use self-attention mechanisms, often used in understanding the context of words in a sentence.
  67. Generative Pre-training Transformer (GPT): A large transformer-based language model with billions of parameters, trained on a large corpus of text from the internet.
  68. Multimodal Models: AI models that can understand inputs from different data types like text, image, sound, etc.
  69. Datasets: Collections of data. In machine learning, datasets are used to train and test models.
  70. Training Set: The portion of the dataset used to train a machine learning model.
  71. Validation Set: The portion of the dataset used to provide an unbiased evaluation of a model fit on the training dataset while tuning model hyperparameters.
  72. Test Set: The portion of the dataset used to provide an unbiased evaluation of a final model fit on the training dataset.
  73. Cross-Validation: A resampling procedure used to evaluate machine learning models on a limited data sample.
  74. Word2Vec: A group of related models that are used to produce word embeddings.
  75. GloVe (Global Vectors for Word Representation): An unsupervised learning algorithm for obtaining vector representations for words.
  76. TF-IDF (Term Frequency-Inverse Document Frequency): A numerical statistic that reflects how important a word is to a document in a collection or corpus.
  77. Bag of Words (BoW): A representation of text that describes the occurrence of words within80. n-grams: Contiguous sequences of n items from a given sample of text or speech. When working with text, an n-gram could be a sequence of words, letters, or even sentences.
  78. Skip-grams: A variant of n-grams where the components (words, letters) need not be consecutive in the text under consideration, but may leave gaps that are skipped over.
  79. Levenshtein Distance: A string metric for measuring the difference between two sequences, also known as edit distance. The Levenshtein distance between two words is the minimum number of single-character edits (insertions, deletions, or substitutions) required to change one word into the other.
  80. Part-of-Speech Tagging (POS Tagging): The process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context.
  81. Stop Words: Commonly used words (such as “a”, “an”, “in”) that a search engine has been programmed to ignore, both when indexing entries for searching and when retrieving them as the result of a search query.
  82. Stemming: The process of reducing inflected (or sometimes derived) words to their word stem, base or root form.
  83. Lemmatization: Similar to stemming, but takes into consideration the morphological analysis of the words. The lemma, or dictionary form of a word, is used instead of just stripping suffixes.
  84. Word Sense Disambiguation: The ability to identify the meaning of words in context in a computational manner. This is a challenging problem in NLP because it’s difficult for a machine to understand context in the way a human can.
  85. Syntactic Parsing: The process of analysing a string of symbols, either in natural language, computer languages or data structures, conforming to the rules of a formal grammar.
  86. Semantic Analysis: The process of understanding the meaning of a text, including its literal meaning and the meaning that the speaker or writer intends to convey.
  87. Pragmatic Analysis: Understanding the text in terms of the actions that the speaker or writer intends to perform with the text.
  88. Topic Modelling: A type of statistical model used for discovering the abstract “topics” that occur in a collection of documents.
  89. Latent Dirichlet Allocation (LDA): A generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar.
  90. Sentiment Score: A measure used in sentiment analysis that reflects the emotional tone of a text. The score typically ranges from -1 (very negative) to +1 (very positive).
  91. Entity Extraction: The process of identifying and classifying key elements from text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.
  92. Coreference Resolution: The task of finding all expressions that refer to the same entity in a text. It is an important step for a lot of higher level NLP tasks that involve natural language understanding such as document summarization, question answering, and information extraction.
  93. Chatbot: A software application used to conduct an on-line chat conversation via text or text-to-speech, in lieu of providing direct contact with a live human agent.
  94. Turn-taking: In the context of conversation, turn-taking is the manner in which orderly conversation is normally carried out. In a chatbot or conversational AI, it refers to the model’s ability to understand when to respond and when to wait for more input.
  95. Anaphora Resolution: This is a task of coreference resolution that focuses on resolving what a particular pronoun or a noun phrase refers to.
  96. Conversational Context: The context in which a conversation is taking place. This includes the broader situation, the participants’ shared knowledge, and the rules and conventions of conversation.
  97. Paraphrasing: The process of restating the meaning of a text using different words. This can be useful in NLP for tasks like data augmentation, or for improving the diversity of chatbot responses.
  98. Document Summarization: The process of shortening a text document with software, in order to create a summary with the major points of the original document. It is an important application of NLP that can be used to condense large amounts of information.
  99. Automatic Speech Recognition (ASR): Technology that converts spoken language into written text. This can be used for voice command applications, transcription services, and more.
  100. Text-to-Speech (TTS): The process of creating synthetic speech by converting text into spoken voice output.

To learn more about ChatGPT terminology and the new artificial intelligence recently upgraded by OpenAI jump over to its official website.

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Learn about “ATPBot”, a trading bot dubbed the “ChatGPT of the investing world”

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Sourced from Cryptopolitan

One of the hot topics this year is ChatGPT, an artificial intelligence technology hailed as a turning point in our lives and work. Keep up with the progress of the world.

One of the most noteworthy artificial intelligence innovations this year is the AI-Crypto Trading Bot ATPBot, which has won the reputation of “ChatGPT in the investment world” due to its integration of artificial intelligence technology and quantitative trading. It provides traders with superior asset trading performance beyond any other bot in the industry.

With its huge data processing and analysis capabilities, ATPBot is similar to ChatGPT’s natural language understanding and processing capabilities. It represents the efficient use of artificial intelligence in quantitative trading and empowers investors.

By utilizing data and algorithms to determine trade times and prices, ATPBot minimizes emotional interference and human error. Today, let us explore ATPBot together, discover the magical ability of this trading bot, and improve the efficiency and stability of quantitative trading.

What is ATPBot?

ATPBot is a platform focused on quantitative trading strategy development and services. It develops and implements quantitative trading strategies for its users with the advantages of AI technology.  ATPBot are intending to provide crypto investors with efficient and stable trading strategies.

By analyzing market data in real time and using natural language processing to extract valuable insights from news articles and other text-based data, ATPBot can quickly respond to changes in market conditions and make more profitable trades. Additionally, ATPBot uses deep learning algorithms to continually optimize its trading strategies, ensuring that they remain effective over time.

Comparing ATPBot with other trading bots

ATPBot boasts unique advantages compared to other trading bots in the market. Unlike many other trading bot platforms, which rely solely on predetermined parameters set by the trader, ATPBot adopts extensively tested and verified trading strategies. By conducting rigorous historical data analysis and market analysis, ATPBot has fine-tuned its strategies to minimize risk and losses while maximizing profits. This differs from other trading bots that have no control over the trading process and often lead to traders losing money.

Moreover, ATPBot eliminates the need for users to spend endless hours manually testing different parameters or acquiring expertise in charting and indicator operations. With ATPBot, users can rely on a reliable and mature trading bot that professionally manages their investment for an efficient and effective trading experience.

What are the advantages of ATPBot

Provide an AI strategy for 24-hour trading: Our team will develop an AI strategy for you with 24-hour trading needs. Whether trading day or night, the strategy will continuously monitor the market and make trading decisions accordingly.

Experienced Strategy Modelling Team: Our team has more than 20 years of experience and manages nearly $1 billion in capital. They will use their expertise and experience to design a strategic model for you to meet your needs.

Powerful computing power support: We will provide huge computing power support to help you determine the best strategy configuration parameters. By using high-performance computing and optimization algorithms, we can quickly and accurately find the best configuration parameters, thereby improving your trading results.

Time-saving and emotion-free trading: Our goal is to save you time and remove the influence of emotions from trading. With automated trading and AI strategies, you can let the system execute your trading decisions, avoiding emotional decisions and human errors.

Strong Profitability: Our strategies are rigorously tested and optimized to ensure their superior profitability in the market. Our actual transaction results far exceed the performance of most funds and private placements in the market, which enables you to obtain higher returns and investment income.

Why Choose ATPBot?

1. World-leading Technology: Cutting-edge algorithms that combine multiple factors are adopted to find profitable methods through complex data types.

2. Simple to Use: All strategies are ready-made that do not require tuning. All you need to begin running a profitable strategy is just a simple click.

3. Millisecond-level Trading: Real-time market monitoring to capture signals and millisecond-level response for quick operations.

4. Ultra-low Management Fee: A permanent one-time payment to achieve a higher return on investment.

5. Security and Transparency: All transactions are processed by the third-party exchange Binance; ATPBot has no access to your funds and we are committed to providing maximum protection for your security.

6.  24/7 Trading: AI trades 24/7 automatically, and you can get profits even when you are sleeping at night.

7. 24/7 Service: One-on-one service; Fix your issues quickly.

Just like ChatGPT is your trusted writing and programming assistant, ATPBot is your exclusive investment analyst and faithful trading partner. Don’t miss out on the opportunity to revolutionize your investment experience with ATPBot.

Register the ATPBot  today to open the door to AI quant trading, and share the profits of AI technology algorithms with ATPBot.

In addition to the functions of the platform itself, ATPBot also has a professional discord community, which gathers a large number of quantitative trading researchers and practitioners. In the community, you can interact with quantitative trading enthusiasts from all over the world, sharing experiences and ideas. Not only will this improve your trading knowledge and skills, but you can also learn and get inspired by other people’s trading strategies. At the same time, our community also provides professional guidance, including guidance on market trends, market analysis and trading skills, to help you go further on the road of quantitative trading.

Disclaimer. This is a sponsored post. Cryptopolitan does not endorse and is not responsible for or liable for any content, accuracy, quality, advertising, products or other materials on this page. Readers should do their own research before taking any actions related to the company. Cryptopolitan is not responsible, directly or indirectly, for any damage or loss caused or alleged to be caused by or in connection with the use of or reliance on any content, goods or services mentioned in this sponsored post.

 

Sourced from Cryptopolitan

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How AI Will Get You Fired—If You Don’t Use It

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By William Arruda

Despite the prevailing concerns about the potential for artificial intelligence to eliminate jobs and harm (or even destroy) the planet, the reality is quite different. AI is not necessarily the harbinger of doom; rather, it has immense potential to enhance human capabilities and drive positive outcomes. The challenge is in understanding and applying AI without being overwhelmed by it.

To learn how leaders and career-minded professionals can embrace AI as a tool for accelerating career advancement and increasing professional happiness, I reached out to Matt Strain, the AI Whisperer, who was featured in the NY Times for using “ChatGPT to create an entire book of cocktails based on the tenets of traditional Chinese medicine written in the style of the J. Peterman catalog.” After a long career at big tech companies (Apple, Adobe …), Matt’s started his own company, The-Prompt.AI, to focus on what he calls “AI for Real People.”

William Arruda: Matt, you shared a quip that’s making the rounds online: “AI won’t replace your job, but someone using AI might.” It’s how I came up with the title of this article, and it’s a sentiment echoing across organizations, from individuals to agencies and companies alike. Interestingly, the Pew Research Center’s report A Majority of Americans Have Heard of ChatGPT, But Few Have Tried It Themselves highlights the public’s simultaneous fascination and fright with the increased use of AI. With the incredible potential that AI promises, why have so few people incorporated it into their work?

Matt Strain: You’re absolutely right. The vast potential of AI invokes fascination and fear. Many people simply don’t know where to start. We’ve seen companies condone the use of AI tools like ChatGPT and DALL-E. Others are requiring usage in the hopes of increased productivity. In addition to the natural fear of change, two main things come into play. First, the fear parlays into scepticism. Many look to find the flaws to confirm their fears. Second, most people simply don’t understand how to get the most out of the tools. Their prompts are ineffective and they have a poor experience.

Arruda: You say that rather than fearing AI, we should embrace it as a catalyst for progress. You suggest that by integrating AI into our work and leveraging its capabilities, we can unlock new opportunities, streamline processes, boost productivity and propel our careers to new heights. You make it sound like the magic bullet for career success.

Strain: There’s an opportunity to reframe this and think of AI as a creative muse that will push us to think more broadly. I believe it will become a non-judgmental co-pilot that is always eager to engage in exploratory discussions. In a nutshell, when you embrace AI right now, you will stand out from your peers and enhance the value of your personal brand.

Having said that, AI is not a magic bullet. We humans still need to invest the energy in forming the right questions and exploring the most important problems. These tools will augment—not replace—our skills.

Arruda: How else do you see AI being used by “real people?”

Strain: Everything everywhere all at once. Well, almost. For career advancement, continuous learning and adaptation are key. Generative AI systems can provide personalized learning resources. For instance, an entrepreneur venturing into the AI tech industry can employ AI for guidance on trends, opportunities, and goal-oriented recommendations. AI can aid in everything from designing research surveys to evaluating corporate strategy. AI is not some future concept. It’s a present-day tool being used by many.

Arruda: Are there any specific AI-powered platforms or applications that you believe can significantly improve networking and professional relationship-building?

Strain: There are many. AI will intelligently recommend contacts, personalize communications, and optimize engagement timings. It will nurture professional relationships through automated scheduling, social monitoring, real-time translation, and insights from data analytics.

These tools are being integrated into major networking platforms like LinkedIn and CRM tools. Microsoft’s relationship with OpenAI ensures that AI will be baked into their office suite. Google is already working on many of these tools. There’s also a new wave of AI start-ups rushing in on a daily basis.

Arruda: What are some real-world work applications that maybe we haven’t even thought about but would help us save time or take the drudgery out of monotonous work activities?

Strain: Meetings and email. In my seventeen years at Adobe, I calculated that I attended more than 40,000 meetings! Imagine a world where meetings are a breeze, and everyone actually looks forward to them. Imagine AI effortlessly aligning schedules, crafting tailor-made agendas, and making sure every voice is heard with real-time transcriptions and translations. With the mundane handled, your post-meeting world is infused with crisp summaries, clear action items, and insightful analytics, turning endless meetings into bursts of creativity and productivity. Might you actually look forward to meetings in the future?

Don’t even get me started about email. AI is coming to optimize that, but I’ll save those thoughts for the next interview.

Arruda: I have heard the emergence of generative AI compared to Oppenheimer’s nuclear bomb. What ethical considerations should career-minded professionals keep in mind when using AI in their work? What are the potential risks or pitfalls?

Strain: Yes, the comparison to Oppenheimer is in terms of AI having the capacity for both good and evil. The main ethical considerations in the short term revolve around ensuring fairness by mitigating biases, safeguarding data privacy and maintaining human accountability for AI-driven decisions. Successful companies will hold on to the human touch and be mindful of deploying AI as an augmentation, not a replacement.

We’re going to see a wave of anxiety in which employees and leaders have to manage short-term fear of change and concerns about jobs, mid-term fear of misinformation and economic disruption, and long-term fears of what it means to be human and the potential for bad actors. These are real and compounding fears. Leaders will have to draw on change management skills to proactively present a vision that demonstrates the ability to direct AI as a productive, creative force. Employees, shareholders and customers will depend on this.

On the positive side, AI can be directed to assist with all these concerns.

Arruda: How can AI assist professionals in enhancing their personal branding and online presence? Are there any specific strategies or tools you recommend? Any examples of people who are doing it right?

Strain: Absolutely. AI has the remarkable ability to study an individual, identifying their strengths and weaknesses, and distilling their authentic values and unique qualities. By observing professionals, AI can offer proactive guidance, aiding in their development and helping them create a genuine and compelling story that sets them apart. Once this story is formed, AI can further assist in creating a strategic plan to effectively communicate this narrative to the right audience. AI-infused tools will help with designing imagery, creating content and monitoring your brand mentions.

Arruda: I know you have been traveling the globe lately as a consultant to corporate leaders at companies in a variety of industries. You’re helping them establish their AI strategies. Without divulging any corporate secrets, what are these leaders’ biggest concerns and hopes for AI?

Strain: It’s a challenging time for leaders. They need to keep a positive attitude and be actively engaged, even as they tackle a long list of concerns such as where to begin, not wanting to disrupt what’s working, how to manage data, ethical issues, and the costs of bringing AI into the fold. There’s also the human element; they’re worried about how this affects their employees in terms of morale, the need for additional training, and the possibility of job losses. These are very real concerns, but they’re also manageable issues that can be addressed with a level-headed plan.

I foresee that the benefits are going to be greater than the worries. Right off the bat, AI can take over mundane work, which means people can focus on more strategic, higher-value tasks. For example, being able to use data effectively will spur innovation and enhance the experience for customers and employees alike. Once we get past this early transitionary stage, I see a future where our teams benefit from working side-by-side with AI-driven tools that can learn and augment their human counterparts.

Arruda: Thanks, Matt. It sounds like the power of AI as a tool for expanding your career success is bound only by the imagination. Readers, to see how you can push past the fear and leverage AI, check out this podcast where Matt dispels myths and reduces the stress that surrounds artificial intelligence.

Feature Image Credit: getty

By William Arruda

William Arruda is a keynote speaker, co-founder of CareerBlast.TV and creator of the 360Reach Personal Brand Survey that helps you get candid, meaningful feedback from people who know you.

Follow me on Twitter or LinkedIn. Check out my website.

Sourced from Forbes

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8 ChatGPT Prompts To Finish Hours Of Work In Seconds

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By Jodie Cook

Co-creating with artificial intelligence can make your work better. What used to take days can now take hours, what used to require trawling through freelancers can be created in a few clicks. For individual creators, an AI co-pilot makes a lot of sense. But rather than outsourcing every part of your work to ChatGPT, use it for the preparation and the ideation. Use it for those grunt work tasks that you don’t really enjoy.

Rowan Cheung is founder of The Rundown, a fast-growing AI newsletter providing an in-depth look at the latest developments in AI. In less than 4 months, The Rundown has gained a following of over 170,000 subscribers who rely on its content to stay informed about the latest advancements in artificial intelligence. Cheung is on a mission to inform millions of people about the latest advancements in AI and highlight how technology is transforming the world. His AI database, Supertools, records the best tools mentioned in the newsletter.

Cheung shares eight ChatGPT prompts to finish hours of work in seconds, to supercharge your output without breaking a sweat.

1. Explain like I’m a beginner

Perhaps there’s a concept you haven’t fully grasped, but it’s fundamental to whatever you’re writing or working on. Rather than struggle away trying to wrap your head around what it means, ask ChatGPT to find an explanation that resonates. After using this prompt, your entire project might make more sense, opening a clear picture of the way forward.

Here’s the prompt: “Explain [topic] in simple terms. Explain to me as if I’m a beginner.” What follows should be basic concepts, simple analogies and memorable ways of demystifying the field.

2. Create unique content ideas

Perhaps you’re right at the beginning of your content creation journey and you need the ideas to get you started, optimized for a certain platform. If you have your topic and you know your audience, ask ChatGPT to come up with ideas of how you can most effectively share, in such a way that the content could go viral. Discard the bad ideas and move forward with the best.

Here’s the prompt, according to Cheung: “Topic: How to [go viral on Twitter, write a viral blog post] talking about [your topic]. Come up with unique and innovative content ideas that are unconventional for this topic for the medium of [Twitter, article, LinkedIn, etc].”

3. Quiz yourself

So you’ve been learning a new subject but you’re not sure it’s sticking. In school, you’d learn and revise to pass a test. Now, you can use ChatGPT to create that test. Ask for a quiz to test your existing knowledge on a topic, to figure out your gaps and how much is left to learn. Or, ask for a quiz about a topic you know nothing about, perhaps before you begin a project on that topic, to set the scene and motivate you to conquer it.

Cheung recommends using this very simple prompt: “Give me a short quiz that tests me on [what you want to learn]” and be sure to fact-check, because the program has been known to deviate from the facts.

4. Change the writing style or tone

Imagine you wrote something in a bad mood and now it shows in the tone. Or you sent a bio in first person and someone wants it in third. Whatever you have made can be transformed with this prompt, saving you the time of doing it manually.

The prompt: “Change the writing style of the text below to [style or tone]” then paste the text, hit return and see the new version. If you need further edits, ask for them too.

5. Consult an expert

When you know there’s room for improvement in what you have written, get ChatGPT to be your trusted editor. Whether you want it to play the part of a lawyer, subject matter expert or simply a proof-reader, ask for commentary from that point of view.

Cheung prompts ChatGPT in the following way: “I will give you a sample of my writing. I want you to criticize it as if you were [role]” Then add your writing, submit to ChatGPT and brace for its critique. Take the parts you agree with and ask it to rewrite the text with them in mind.

6. Train it to learn your writing

Not only can you train ChatGPT to learn your writing style, you can train it to create its own prompt to write in your style. And who better to create prompts for ChatGPT than the program itself? It will be instructing itself in its own preferred way of learning, a self-guiding method that brings you the best results.

The prompt is simple: “Analyse the text below for style, voice, and tone. Create a prompt to write a new paragraph in the same style, voice, and tone.” After adding your text, what follows will be the prompt that you can paste into future instructions to write in your style.

7. Specify the audience and purpose

Let’s imagine you’ve asked ChatGPT to write some articles on a certain topic, but it’s missing the mark. Or imagine you’ve written the content yourself but you know it could be better. Here’s where more specific prompting can bring forth more detailed work, that resonates far better with your audience

Within this prompt, specify the audience, tone and goal. Cheung’s example on an article with, “Topic: How to grow your Twitter following,” was to add, “Audience: Twitter users trying to grow their account. Tone: Inspiring Goal: Inspire audience to feel excited about growing their Twitter following and teach them how to do it in simple terms.” Now, the text will be reworked to fulfil that goal, without any further input from you.

8. List long articles in bullet points

Much of the content on the internet is simply curation. Academics and philosophers did the research and the thinking, and the rest of us are turning those vast studies into bite-sized nuggets that our audiences can consume. As with most tasks of this nature, there’s a prompt for that.

This prompt for ChatGPT, according to Cheung, is to: “Summarize this paragraph into bullet points that a beginner would understand.” You then copy a paragraph or more from any given text and see a summary. This summary might be used as a social media post, a LinkedIn carousel, or simply used to help you paraphrase in a way that suits your style and medium.

Don’t get stuck with writer’s block, chained to your desk struggling for inspiration to start, keep going or finish. Use these simple prompts to expand your reach, unlock new ideas and create more consistently. Build a habit of co-creating with AI and take steps in the right direction of prolific production.

Feature Image Credit: getty

By Jodie Cook

Follow me on Twitter or LinkedIn. Check out my website or some of my other work here.

Founder of Coachvox.ai – we make AI coaches. Forbes 30 under 30 class of 2017. Post-exit entrepreneur and author of Ten Year Career. Competitive powerlifter and digital nomad.

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The Future Of Generative AI Beyond ChatGPT

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By Bernard Marr

Generative tools like ChatGPT and Stable Diffusion have got everyone talking about artificial intelligence (AI) – but where is it headed next?

It’s already clear that this exciting technology will have a big impact on the way we live and work. UK energy provider Octopus Energy has said that 44% of its customer service emails are now being answered by AI. And the CEO of software firm Freshworks has said that tasks that previously took eight to 10 weeks are now being completed in days as a consequence of adopting AI tools into its workflows.

But we’re still only at the beginning. In the coming weeks, months, and years we will see an acceleration in the pace of development of new forms of generative AI. These will be capable of carrying out an ever-growing number of tasks and augmenting our skills in all manner of ways. Some of them may seem as unbelievable to us today as the rise of ChatGPT and similar tools would have done just a few months back.

So, let’s take a look at some of the ways we can expect generative AI to evolve in the near future and some of the tasks it will be lending a hand with before too long:

Beyond ChatGPT

Text-based generative AI is already pretty impressive, particularly for research, creating first drafts, and planning. You might have had fun getting it to write stories or poems, too, but probably realized it isn’t quite Stephen King or Shakespeare yet, particularly when it comes to coming up with original ideas. Next-generation language models – beyond GPT-4 – will understand factors like psychology and the human creative process in more depth, enabling them to create written copy that’s deeper and more engaging. We will also see models iterating on the progress made by tools such as AutoGPT, which enable text-based generative AI applications to create their own prompts, allowing them to carry out more complex tasks.

As well as text, current generative AI technology is quite good at creating images based on natural language prompts, and there are even some tools that use it to generate video. However, they have some limitations due to the intensive nature of the required data processing. As this domain of generative AI becomes more advanced, it’s likely that it will become easy to create images and videos of just about anything, to the extent that it becomes difficult to distinguish generative AI content from reality. This could lead to issues such as deepfakes becoming problematic, resulting in the spread of fake news and disinformation.

Generative AI in the Metaverse

There are many predictions about how the way we interact with information and each other in the digital domain will involve. Many of these focus on immersive, 3D environments and experiences that can be explored through virtual and augmented reality (VR/AR). Generative AI will speed up the design and development of these environments, which is a time and resource-intensive process, and Meta (formerly Facebook) has indicated that this could play a part in the future of its 3D worlds platforms. Additionally, generative AI can be used to create more lifelike avatars that help to bring these environments to life, capable of more dynamic actions and interactions with other users.

Generative Audio, Music, and Voice AI

AI models are already impressively capable when it comes to generating music and mimicking human voices. In music, generative AI is likely to increasingly become an invaluable tool for songwriters and composers, creating novel compositions that can serve as inspiration or encourage musicians to approach their creative process in new ways. We are also likely to see it being used to create real-time, adaptive soundtracks – for example, in video games or even to accompany live footage of real-world events such as sports. AI voice synthesis will also improve, bringing computer-generated voices closer to the levels of expression, inflection, and emotion conveyed by a human voice. This will open new possibilities for real-time translation, audio dubbing, and automated, real-time voiceovers and narrations.

Generative Design

AI can be used by designers to assist in prototyping and creating new products of many shapes and sizes. Generative design is the term given for processes that use AI tools to do this. Tools are emerging that will allow designers to simply enter the details of the materials that will be used and the properties that the finished product must have, and the algorithms will create step-by-step instructions for engineering the finished item. Airbus engineers used tools like this to design interior partitions for the A320 passenger jet, resulting in a weight reduction of 45% over human-designed versions. In the future, we can expect many more designers to adopt these processes and AI to play a part in the creation of increasingly complex objects and systems.

Generative AI in Video Games

Generative AI has the potential to significantly impact the way video games are designed, built, and played. Designers can use it to help conceptualize and build the immersive environments that games use to challenge players. AI algorithms can be trained to generate landscapes, terrain, and architecture, freeing up time for designers to work on engaging stories, puzzles, and gameplay mechanics. It can also create dynamic content – such as non-player characters (NPCs) that behave in realistic ways and can communicate with players as if they are humans (or orcs or aliens) themselves, rather than being restricted to following scripts. Once game designers get to grips with implementing generative AI into their workflows, we can expect to see games and simulations that react to players’ interactions on the fly, with less need for scripted scenarios and challenges. This could potentially lead to games that are far more immersive and realistic than even the most advanced games available today.

Feature Image Credit: Adobe Stock

By Bernard Marr

Follow me on Twitter or LinkedIn. Check out my website or some of my other work here.

Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a strategic business & technology advisor to governments and companies. He helps organisations improve their business performance, use data more intelligently, and understand the implications of new technologies such as artificial intelligence, big data, blockchains, and the Internet of Things. Why don’t you connect with Bernard on Twitter (@bernardmarr), LinkedIn (https://uk.linkedin.com/in/bernardmarr) or instagram (bernard.marr)?

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5 Artificial Intelligence Use Cases More Promising Than ChatGPT

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By Tim Clark

If you’re reading this article, chances are good you’ve had a chance to play around with ChatGPT or similar Artificial Intelligence (AI) offerings that are moving the AI adoption needle. And make no mistake, that needle will be redlining for the next few years. According to IDC, as companies incorporate AI into their business operations, spending on AI-centric systems will rise at a compound annual growth rate of 27% between 2022 and 2026, to exceed a staggering $300 billion. Obviously, there’s more than meets the AI when it comes to real uses cases. Here are five promising ones you may not be aware of.

Generative AI Won’t Take Off Without 6G

6G connectivity is waiting in the wings to help companies make AI models more efficient and responsible while data from machines and connected devices continues to grow, according to the recent Forbes article, Generative AI Won’t Take Off Without 6G. “Imagine the possibilities in a world of unlimited immersive experiences,” said John Licata, innovation foresight strategist at SAP. “We’re talking about going beyond initial data inputs from AR and tapping into it in new hyper-personalized and hyper-contextualized 3D experiences generated by AI and used by businesses both ethically and responsibly.”

There Goes The (Search Engine) Neighbourhood: AI Generated SEO Is Here

If you haven’t yet subscribed to the weekly AI newsletter, The Neuron what are you waiting for? Weekly AI gems are delivered right to your inbox daily, like this one that highlights a Twitter thread showing how AI can significantly boost SEO efforts. While the results—using 100% AI-generated content—are impressive, marketing creatives will be delighted to know that Step #1 in the process involves “understanding the target audience” and that a large part of successful AI content campaigns is “knowing what your audience is searching.” Amen!

Madison Avenue Rocks AI For Innovative Campaign

Advertising Agency BBDO New York relied on generative AI to help enterprise software firm SAP create awareness about the speed of business in a new campaign in which the creative changed daily based on current events in culture and business.

Here’s how it worked: AI software generated a composite image of each day’s headlines (based on four inputs), and a human illustrator finalized the images for the Digital out-of-home-advertising boards. Here’s what Ada Agrait, senior vice president of corporate marketing at SAP, told AdAge in a recent article: “Business-built AI gives organizations the agility to be ready for whatever comes every day. This campaign activation exemplifies an organization’s ability to continuously pivot when AI is infused into its business processes.”

AI Podcasts: Boring or Better?

A recent Wired article makes a pretty good case as to why we really don’t need more podcasts, let alone AI generated ones. “Apart from listening to the podcast because of its technological advancement, there’s no point. It’s just wasted time, said Hugo, creator of The Joe Rogan AI Experience, the first AI-generated podcast to take off. Indeed, “robot chit-chat” may not be for everyone, so perhaps the more reasonable AI advancement is for Spotify to use AI to make host-read podcast ads that sound like real people?

AI Helps Businesses Address The Talent Gap

Companies face a growing challenge as they manage the gap between the skills they have in their work force and the skills they need for the future. Closing that gap means optimizing how they recruit and hire new talent in today’s competitive market, as well as how they deliver learning and development programs to help employees grow. In an effort to address these challenges SAP has integrated generative AI into SAP SuccessFactors. Amy Wilson, senior vice president of products and design for SAP SuccessFactors, highlighted some of the capabilities during a demonstration at the Sapphire conference. “It understands requirements for a position and helps HR professionals create job descriptions and interview questions,” said Wilson. “It also provides ideas and tips for planning a recruiting campaign. Aligned with customers’ business strategy, AI will be able to find out the skills that employees need to acquire in the coming years.”

By Tim Clark

Follow me on Twitter or LinkedIn.

I am a former Editor-in-Chief and now Head of Brand Journalism for SAP, leading the company’s native advertising strategy.

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I Asked ChatGPT How to Recession-Proof a Business. Here’s What It Suggested (and What I Would Do Differently).

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By Amine Rahal

Silicon Valley’s leading artificial intelligence weighs in on how you can make your business more resilient to economic downswings.

Entrepreneurs everywhere always have to look out for the dreaded “r-word.” They come around every so often and wreak havoc on businesses by reducing sales, dropping revenues and cutting employment. Of course, we’re talking about recessions — a natural, but certainly painful, part of the economic cycle.

While there’s no way to completely insulate a company from the effects of recessions, there are steps you can take to help mitigate them.

As a marketing and technology entrepreneur, I was curious to learn more about how to “recession-proof” my businesses. That’s why I asked ChatGPT, the world’s leading large language model (LLM) and the artificially intelligent darling of Silicon Valley.

Below, I’ll share my conversation with ChatGPT about how entrepreneurs can protect their businesses from recessions and, ultimately, share my own thoughts on these ideas.

The prompt

I opened our conversation by asking the following question in the form of a written prompt:

How can I make my business recession-proof?

Then, ChatGPT responded with the following steps after providing a brief disclaimer that no business can completely protect itself from inflation.

ChatGPT’s “recession-proof” entrepreneurship formula

Below are, verbatim, the seven recommendations offered by ChatGPT to help businesses weather the storm during recessions:

  1. Build a strong cash reserve.
  2. Diversify your offerings.
  3. Focus on efficiency.
  4. Maintain good customer relationships.
  5. Keep an eye on your finances.
  6. Prepare for the worst.
  7. Stay flexible.

My thoughts on ChatGPT’s formula

Personally, I think ChatGPT’s advice is excellent, and I generally agree with each point. However, I have slight qualifications for some. Below, I’ll share my thoughts on each:

1. Build a strong cash reserve:

To make it through down periods, you need to have cash saved for a rainy day. This is as true for businesses as it is for your personal finances. However, I’d go a step further and recommend holding non-cash savings as well to protect against inflationary effects. An asset such as gold and other precious metals, or even real estate, can serve as highly resilient stores of wealth during recessions — although they’re far less liquid than cash on hand.

2. Diversify your offerings:

This is a big one. Ensure you don’t count on a single product or service to carry your business. Diversify your revenue streams by offering several products or services so that if one gets hit badly by the recession, another can keep your business afloat.

For example, a car dealership could diversify its offerings by adding commercial vehicles and trucks to its pre-existing line up of passenger vehicles.

3. Focus on efficiency

This one deserves a caveat. Prepare for a lean, hyper-efficient operation if economic circumstances require it, but don’t single-mindedly focus on efficiency by automating, downsizing and streamlining each and every task. Sometimes customer satisfaction and product refinement require a larger crew and more time dedicated to non-core functions, so allow space for that as well.

4. Maintain good customer relationships

This one is a given. Longstanding, loyal customers are far more likely to stick around during recessionary periods if you offer friendly, high-quality service. I suggest adding deal-sweeteners and discounts to repeat customers to keep them coming back.

5. Keep an eye on your finances

Create a budget, and stick to it. ChatGPT emphasizes the importance of monitoring your cash flow, and it’s right. If cash inflows aren’t leaving enough left over to cover all expenses while saving for a rainy day, you need to reevaluate your expenses and re-budget accordingly.

6. Prepare for the worst

Actively plan for an upcoming recession. In modern history, recessions have occurred every 3.25 years on average. Good entrepreneurs should use this as a baseline for when they should anticipate periodic business slowdowns, and contingency plans should account for these. This way, you can respond quickly if economic events lead to decreased sales.

7. Stay flexible

Always be willing to adapt. Market conditions can change suddenly, and savvy business owners need to be prepared for that by being flexible and able to pivot when necessary.

Overall, ChatGPT presents a great set of principles to abide by if you want your business to be more resilient to recessions. But it’s worth reiterating that no business strategy is “recession-proof” as deep, economy-wide events can and will have unmitigable effects on businesses of all kinds.

Yet, keeping a flexible and responsible approach to business management — as ChatGPT suggests above — would certainly make your company more likely to survive an economic downturn than one that doesn’t.

By Amine Rahal

Entrepreneur Leadership Network Contributor. CEO and Founder. Amine is a tech entrepreneur and writer. He is currently the CEO of IronMonk Solutions.

Sourced from Entrepreneur

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Google Unveils Plan to Demolish the Journalism Industry Using AI

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Sourced from Futurism

Remember back in 2018, when Google removed “don’t be evil” from its code of conduct?

It’s been living up to that removal lately. At its annual I/O in San Francisco this week, the search giant finally lifted the lid on its vision for AI-integrated search — and that vision, apparently, involves cutting digital publishers off at the knees.

Google’s new AI-powered search interface, dubbed “Search Generative Experience,” or SGE for short, involves a feature called “AI Snapshot.” Basically, it’s an enormous top-of-the-page summarization feature. Ask, for example, “why is sourdough bread still so popular?” — one of the examples that Google used in their presentation — and, before you get to the blue links that we’re all familiar with, Google will provide you with a large language model (LLM) -generated summary. Or, we guess, snapshot.

“Google’s normal search results load almost immediately,” The Verge’s David Pierce explains. “Above them, a rectangular orange section pulses and glows and shows the phrase ‘Generative AI is experimental.’ A few seconds later, the glowing is replaced by an AI-generated summary: a few paragraphs detailing how good sourdough tastes, the upsides of its prebiotic abilities, and more.”

“To the right,” he adds, “there are three links to sites with information that Reid says ‘corroborates’ what’s in the summary.”

As it goes without saying, this format of search, where Google uses AI tech to regurgitate the internet back to users, is wildly different from how the search-facilitated internet works today. Right now, if you Google that same query — “why is sourdough bread still so popular?” — you’d be met with a more familiar scene: a featured excerpt from whichever website won the SEO race (in this case, that website was British Baker), followed by that series of blue links.

At first glance, the change might seem relatively benign. Often, all folks surfing the web want is a quick-hit summary or snippet of something anyway.

But it’s not unfair to say that Google, which in April, according to data from SimilarWeb, hosted roughly 91 percent of all search traffic, is somewhat synonymous with, well, the internet. And the internet isn’t just some ethereal, predetermined thing, as natural water or air. The internet is a marketplace, and Google is its kingmaker.

As such, the demo raises an extremely important question for the future of the already-ravaged journalism industry: if Google’s AI is going to mulch up original work and provide a distilled version of it to users at scale, without ever connecting them to the original work, how will publishers continue to monetize their work?

“Google has unveiled its vision for how it will incorporate AI into search,” tweeted The Verge’s James Vincent. “The quick answer: it’s going to gobble up the open web and then summarize/rewrite/regurgitate it (pick the adjective that reflects your level of disquiet) in a shiny Google UI.”

Research has shown that information consumers hardly ever make it to even the second page of search results, let alone even the bottom of the page. And worse, it’s not like Google’s taking clicks away from its long-time information merchants by hiring an army of human content writers to churn out summarization. Google’s new search interface, which is built on a model that’s already been trained by way of boatloads upon boatloads of unpaid-for human output, will seemingly be swallowing even more human-made content and spitting it back out to information-seekers, all the while taking valuable clicks away from the publishers that are actually doing the work of reporting, curating, and holding powerful interests like Google to account.

As of now, it’s unclear whether or how Google plans to compensate those publishers.

In an emailed statement to Futurism, a Google spokesperson said that “we’re introducing this new generative AI experience as an experiment in Search Labs to help us iterate and improve, while incorporating feedback from users and other stakeholders.”

“As we experiment with new LLM-powered capabilities in Search, we’ll continue to prioritize approaches that will allow us to send valuable traffic to a wide range of creators and support a healthy, open web,” the spokesperson added.

Asked specifically whether the company has plans to compensate publishers for any AI-regurgitated content, Google had little in response.

“We don’t have plans to share on this, but we’ll continue to work with the broader ecosystem,” the spokesperson told Futurism.

Publishers, however, are extremely wary of these changes.

“If this actually works and is implemented in a firm way,” wrote RPG Site owner Alex Donaldson, “this is literally the end of the business model for vast swathes of digital media lol.”

At the end of the day, there are a lot of questions that Google needs to answer here, not the least being that AI systems, Google’s included, spew fabrications all the time.

The Silicon Valley giant has long claimed that its goal is to maximize access to information. SGE, though, seemingly seeks to do something quite different — and if the company doesn’t figure out a way to compensate publishers for the labour it’ll be gleaning from the journalists, the effects on the public’s actual access to information could be catastrophic.

Updated with comment from Google.

Feature Image Credit: Getty

Sourced from Futurism

 

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Vector Databases: Long-Term Memory for Artificial Intelligence

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By Mark Hinkle

Vector databases store data such as text, video or images that are converted into vector embeddings for AI models to access them quickly.

Artificial Intelligence, such as ChatGPT, acts much like someone with endemic memory who goes to a library and reads every book. However, when you ask an AI a question that was not in the book at the library, it either admits it doesn’t know or hallucinates.

An AI hallucination refers to instances where an artificial intelligence system generates an output that may seem coherent or plausible but is not grounded in reality or accurate information. These outputs can include text, images or other forms of data that the AI model has produced based on its training but may not align with real-world facts or logic.

For example, we could use a generative AI for images like the ones Midjourney provides to generate a picture of an old man. However, the prompt (the way you communicate with an AI like Stable Diffusion or others) has to be something that the model understands. For example, you may ask the AI to create a picture of a man who is over the hill. In this case, I used Midjourney, a popular generative AI for images, to do just that. I used an example that I thought might cause it to hallucinate.

Midjourney-generated image of a man over the hill

Midjourney doesn’t understand euphemisms like over the hill, so it generated a picture of a man who was literally over the top of a hill.

How could you inform the AI what you mean by “over the hill,” and other nuances of language it doesn’t know of? First, you could provide training data. The way you would do this is to convert that data into something known as embeddings, and then import them into a vector database.

While this example is a bit far-fetched for effect, many other contexts apply. For example, industry-specific terminology for medical and legal fields would benefit from being able to train AI on their specific terminology and meanings. Enterprises will want to provide their data to AI without introducing public models.

A critical use case for vector databases is large language models to retrieve domain-specific or proprietary facts that can be queried during text generation. Therefore, vector databases will be essential for organizations building proprietary large language models.

Vector vs. NoSQL and SQL Databases

Traditional databases, such as relational databases (e.g., MySQL, PostgreSQL, Oracle) and NoSQL databases (e.g., MongoDB, Cassandra), have been the backbone of business data management for decades. They store and organize data in structured formats like tables, documents or key-value pairs, making it easier to query and manipulate using standard programming languages.

These databases excel at handling structured data with fixed schema, but they often struggle with unstructured data or high-dimensional data, such as images, audio and text. Moreover, as the volume and velocity of data increase, they may face performance bottlenecks, leading to slower response times and scalability issues.

Vector databases, on the other hand, represent a paradigm shift in data storage and retrieval. Instead of relying on structured formats, they store and index data as mathematical vectors in high-dimensional space. This approach, called “vectorization,” allows for more efficient similarity searches and better handling of complex data types, such as images, audio, video and natural language.

Imagine a vector database as a vast warehouse and the AI as the skilled warehouse manager. In this warehouse, every item (data) is stored in a box (vector), organized neatly on shelves in a multidimensional space. The warehouse manager (AI) knows the exact position of each box and can quickly retrieve or compare the items based on their similarities, just like a skilled warehouse manager can find similar group products.

The boxes represent different types of unstructured data, such as text, images or audio, which have been transformed into a structured numerical format (vectors) to be efficiently stored and managed. The more organized and optimized the warehouse is, the faster and more accurately the warehouse manager (AI) can find the items needed for various tasks, such as making recommendations, recognizing patterns or detecting anomalies.

This analogy helps convey the idea that vector databases serve as a crucial foundation for AI systems, enabling them to efficiently manage, search and process complex data in a structured and organized manner. Just as a well-managed warehouse is essential for smooth business operations, a vector database plays a vital role in the success of AI-driven applications and solutions.

The key advantage of vector databases is their ability to perform approximate nearest neighbour (ANN) search, quickly identifying similar items in a large dataset. Using techniques like dimensionality reduction and indexing algorithms, vector databases can perform these searches at scale, providing lightning-fast response times and making them ideal for applications like recommendation systems, anomaly detection and natural language processing.

Embeddings — Turning Words, Images and Videos into Numbers

Embeddings are techniques that convert complex data, such as words, into simpler numerical representations (called vectors). This makes it easier for AI systems to understand and work with the data. Probability helps create these representations by analysing how often certain pieces of data appear together.

Probability helps quantify the similarity of two pieces of data, allowing the AI system to find related items. Probability-based techniques help AI systems quickly find similar data points in large databases without examining every item. Probability helps AI systems group similar data points together and reduce the complexity of the data, making it easier to process and analyse.

Popular Vector Databases

While there are an ever-growing number of vector databases, several factors contribute to their popularity. These factors include efficient performance in storing, indexing and searching high-dimensional vectors, ease of use in integrating with existing machine learning frameworks and libraries, scalability in handling large-scale, high-dimensional data, flexibility in offering multiple backends and indexing algorithms, and active community support with valuable resources, tutorials and examples.

Vector databases that are more likely to be popular among users are ones that provide fast and accurate nearest-neighbour search, clustering, and similarity matching, and that can be easily deployed on cloud infrastructure or distributed computing systems. Based on popularity among users and the number of stars on Github, here are some of the most popular vector databases.

  • Pinecone: Pinecone is a cloud-based vector database designed to efficiently store, index and search extensive collections of high-dimensional vectors. Pinecone’s key features include real-time indexing and searching, handling sparse and dense vectors, and support for exact and approximate nearest-neighbour search. In addition, Pinecone can be easily integrated with other machine learning frameworks and libraries, making it popular for building production-grade NLP and computer vision applications.
  • Chroma: Chroma is an open source vector database that provides a fast and scalable way to store and retrieve embeddings. Chroma is designed to be lightweight and easy to use, with a simple API and support for multiple backends, including RocksDB and Faiss (Facebook AI Similarity Search — a library that allows developers to quickly search for embeddings of multimedia documents that are similar to each other). Chroma’s unique features include built-in support for compression and quantization, as well as the ability to dynamically adjust the size of the database to handle changing workloads. Chroma is a popular choice for research and experimentation due to its flexibility and ease of use.
  • Weaviate: Weaviate is an open source vector database designed to build and deploy AI-powered applications. Weaviate’s key features include support for semantic search and knowledge graphs and the ability to automatically extract entities and relationships from text data. Weaviate also includes built-in support for data exploration and visualization. Weaviate is an excellent choice for applications that require complex semantic search or knowledge graph functionality.
  • Milvus: Milvus is an open source vector database designed for large-scale machine-learning applications. Milvus is optimized for both CPU and GPU-based systems and supports exact and approximate nearest-neighbour searches. Milvus also includes a built-in RESTful API and support for multiple programming languages, including Python and Java. Milvus is a popular choice for building recommendation engines and search systems that require real-time similarity searches. Milvus is part of the Linux Foundation’s AI and Data Foundation, but the primary developer is Zilliz.
  • DeepLake: DeepLake is a cloud-based vector database that is designed for machine learning applications. DeepLake’s unique features include built-in support for streaming data, real-time indexing and searching, and the ability to handle both dense and sparse vectors. DeepLake also provides a RESTful API and support for multiple programming languages. DeepLake is a good choice for applications that require real-time indexing and search of large-scale, high-dimensional data.
  • Qdrant: Qdrant is an open source vector database designed for real-time analytics and search. Qdrant’s unique features include built-in support for geospatial data and the ability to perform geospatial queries. Qdrant also supports exact and approximate nearest-neighbour searches and includes a RESTful API and support for multiple programming languages. Qdrant is an excellent choice for applications that require real-time geospatial search and analytics.

As in the case of SQL and NoSQL databases, vector databases come in many different flavours and address various use cases.

Use Cases for Vector Databases

Artificial intelligence applications rely on efficiently storing and retrieving high-dimensional data to provide personalized recommendations, recognize visual content, analyse text and detect anomalies. Vector databases enable efficient and accurate search and analysis of high-dimensional data, making them essential for developing robust and efficient AI systems.

Recommender Systems

In recommender systems, vector databases have the crucial function of storing and proposing items that best match users’ interests and preferences. These databases facilitate fast and effective searches for similar items by representing items as vectors. This feature allows AI-powered systems to provide personalized recommendations, thus improving user experiences on social networks, streaming services and e-commerce websites.

One commonly used AI-powered recommendation system is the one used by Amazon. Amazon uses a collaborative filtering algorithm that analyses customer behaviour and preferences to make personalized recommendations for products they might be interested in purchasing.

This system considers past purchase history, search queries and items in the customer’s shopping cart to make recommendations. Amazon’s recommendation system also uses natural language-processing techniques to analyse product descriptions and customer reviews to provide more accurate and relevant recommendations.

Image and Video Recognition

In image and video recognition, vector databases store visual content as high-dimensional vectors. These databases empower AI models to efficiently recognize and understand images or videos, find similarities, and perform object recognition, face recognition, or image classification tasks. This has applications in security and surveillance, autonomous vehicles and content moderation.

One commonly used image and video recognition system powered by AI is the TensorFlow Object Detection API. This open source framework developed by Google allows users to train their own models for object detection tasks, such as identifying and localizing objects within images and videos.

The TensorFlow Object Detection API uses deep learning models, such as the popular Faster R-CNN and SSD models, to achieve high accuracy in object detection. It also provides pre-trained models for everyday object detection tasks, which can be fine-tuned on new datasets to improve performance.

Natural Language Processing (NLP)

Vector databases play a critical role in NLP by storing and managing information about words and sentences as vectors. These databases enable AI systems to perform tasks such as searching for related content, analysing the sentiment of a piece of text or even generating human-like responses. By harnessing the power of vector databases, NLP models can be used for applications like chatbots, sentiment analysis or machine translation.

One commonly used NLP system is the Natural Language Toolkit (NLTK). NLTK is a comprehensive platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources and a suite of text-processing libraries for classification, tokenization, stemming, tagging, parsing, semantic reasoning and more. Researchers and practitioners widely use NLTK in academia and industry, and it is a popular choice for teaching NLP concepts and techniques.

Anomaly Detection

Vector databases can help detect unusual activities or behaviours in various areas, such as cybersecurity, fraud detection or industrial equipment monitoring. These databases can quickly identify patterns that deviate from the norm by representing data as vectors. AI models integrated with vector databases can then flag these anomalies and trigger alerts or mitigation measures, ensuring timely and effective responses.

Microsoft Azure Anomaly Detector is a cloud-based service that allows users to monitor and analyse time series data to identify anomalies, spikes and other unusual patterns. Azure Anomaly Detector uses advanced AI algorithms such as Seasonal Hybrid ESD (S-H-ESD) and Singular Spectrum Analysis (SSA) to automatically detect and alert users when anomalous behaviour is caught in the data. It also provides a simple REST API for developers to integrate the service into their applications and workflows efficiently.

Summary

Vector databases are critical to many artificial intelligence (AI) applications, including recommender systems, image and video recognition, natural language processing (NLP) and anomaly detection. By storing and managing data as high-dimensional vectors, these databases enable efficient and accurate search and analysis of large datasets, leading to enhanced user experiences, improved automation, and timely detection of anomalies. In the realm of recommender systems, vector databases allow for the quick identification of items most relevant to users’ preferences.

At the same time, image and video recognition enables efficient object and face recognition. Vector databases play a crucial role in NLP by storing and managing information about words and sentences as vectors. In anomaly detection, they enable quick identification of unusual patterns or behaviours. Overall, vector databases are essential for developing robust and efficient AI systems across various domains.

Feature Image Credit: tikisada from Pixabay

By Mark Hinkle

Mark has a long history in emerging technologies and open source. Before co-founding TriggerMesh, he was the executive director of the Node.js Foundation and an executive at Citrix, Cloud.com and Zenoss where he led their open source efforts.

Sourced from THENEWSTACK

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Artificial Intelligence Is Going To Destroy Social Media

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By John Brandon

Remember the date of March 3, 2023.

It might be just another Friday on the calendar, but it’s actually the day a well-known social media company announced their own demise. It’s also the beginning of the end for all social media.

That’s right, March 3 is when LinkedIn announced a new “collaborative article” concept, which (if you follow AI trends and know how these things usually pan out) seems harmless enough at first. Prior to this, it was — a voicebot will always be available in your home or a robotic car will drive you to work. In the announcement, LinkedIn mentioned this innocuous phrase: “These articles begin as AI-powered conversation starters, developed with our editorial team.”

What’s really happening here? My guess is that LinkedIn is using AI to scan their own platform (what they claim is “10 billion years of professional experience”) to generate AI-created content. As humans, we’ll respond to these posts because they will be tailor-made to encourage a response and debate. How these posts will be labelled is still unknown. What’s clear is that there will be a plethora of AI-enabled content meant to encourage more engagement.

One report called this semi-automated social media. I tend to take a darker view. I recently wrote about how an AI chatbot is posting on Twitter, and that the commenters are often a bit confused about whether the account is powered by a real human or not. It’s a curious development. I’m in favour of AI helping us do our work. I’m not in favour of people thinking content created by a human is actually something cooked up by an AI, mostly because it means the entire experience will degrade, one post at a time. I’ve already experienced way more LinkedIn spam messaging of late, to the point where I now barely read any direct messages at all. The last thing I need is AI spam.

The question is where this all will lead. Once AI starts controlling the algorithm and posting content to lure us into more discussions, it’s just a matter of time before more and more accounts that appear to be human (with an AI-generated face and a fake location) start invading these networks, ruining the experience for all of us.

Imagine how this might work.

On a typical day, you might login to LinkedIn or Facebook, scrolling through your feed. You see plenty of comments and lively discussion. But it’s all a ruse. The social media platform has allowed and even enabled the AI accounts to create the discussions (and the comments), and they are geared for you — your interests and proclivities. The chats will always look appealing because the social media networks know what you like and what you usually follow.

On Instagram and TikTok, bots will know which photos and videos you like the best, but without the human element, it will all become nothing more than a way to grab your attention even more and keep you hooked longer on the apps, showing you ads that are also fine-tuned to your interest. Not to make it all sound too dire, but think of The Matrix and the moment Neo realized he was (spoiler alert for the five people who don’t know this) nothing more than a battery in a tube.

When we are all surrounded by AI bots acting like humans, looking at content that was not generated by humans and looking at ads powered by algorithms, it will feel about the same as The Matrix. None of it will seem real. And then one of it will have value.

With apologies to Elon Musk and Mark Zuckerberg, this might be when we reach behind our neck and pull the cord out. It might be when social media finally loses its grip on us and we realize it was all designed to keep us hooked to their advertising formulas after all. I hope we do wake up before that nightmare occurs.

Feature Image Credit: getty

By John Brandon

John Brandon is a well-known journalist who has published over 15,000 articles on social media, technology, leadership, mentoring, and many other topics. Before starting his writing career in 2001, he worked as an Information Design Director at Best Buy Corporation. Follow him on Twitter: https://twitter.com/johnbrandonmn. @johnbrandonmn

Sourced from Forbes

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