Artificial neural networks and related deep learning are conquering other areas of the industry. It underpins most deep learning models. As a result, deep learning may sometimes be referred to as deep neural learning or deep neural networking. The use of networks built of artificial neurons allows to create software that imitates the work of the human brain, which translates into an increase in the efficiency of business processes and companies.
The Neural Network is constructed from 3 type of layers:
Input layer — initial data for the neural network.
Hidden layers — intermediate layer between input and output layer and place where all the computation is done.
Output layer — produce the result for given inputs.
The input layer is used to retrieve data and pass it on to the first hidden layer. In hidden layers, calculations are performed, as well as the learning process itself. The output layer calculates the output values obtained from the entire network, and then sends the obtained results to the outside.
Each node has a weight and a threshold – when the threshold value exceeds the allowable value, it activates and sends data to the next layer. Neural networks need training data from which they learn to function properly. As they receive more data, they can improve their performance.
Neural networks come in several different forms, including recurrent neural networks, convolutional neural networks, artificial neural networks and feedforward neural networks, and each has benefits for specific use cases. However, they all function in somewhat similar ways — by feeding data in and letting the model figure out for itself whether it has made the right interpretation or decision about a given data element.
Neural networks involve a trial-and-error process, so they need massive amounts of data on which to train. It’s no coincidence neural networks became popular only after most enterprises embraced big data analytics and accumulated large stores of data. Because the model’s first few iterations involve somewhat educated guesses on the contents of an image or parts of speech, the data used during the training stage must be labeled so the model can see if its guess was accurate. This means, though many enterprises that use big data have large amounts of data, unstructured data is less helpful. Unstructured data can only be analyzed by a deep learning model once it has been trained and reaches an acceptable level of accuracy, but deep learning models can’t train on unstructured data.
Deep learning will be developed, and deep neural networks will find application in completely new areas. It is already predicted that they can be used in driving autonomous cars or in the entertainment sector to analyze the behavior of users of a streaming service, or add sound to silent movies.
You can read more about Artificial Neural Network here.
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Deep learning is a type of machine learning and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge. Deep learning is an important element of data science, which includes statistics and predictive modeling. It is extremely beneficial to data scientists who are tasked with collecting, analyzing and interpreting large amounts of data; deep learning makes this process faster and easier. At its simplest, deep learning can be thought of as a way to automate predictive analytics. While traditional machine learning algorithms are linear, deep learning algorithms are stacked in a hierarchy of increasing complexity and abstraction.
Computer programs that use deep learning go through much the same process as the toddler learning to identify things around him. Each algorithm in the hierarchy applies a nonlinear transformation to its input and uses what it learns to create a statistical model as output. Iterations continue until the output has reached an acceptable level of accuracy. The number of processing layers through which data must pass is what inspired the label deep.
Unlike the toddler, who will take weeks or even months to understand the concept of eg. bed, a computer program that uses deep learning algorithms can be shown a training set and sort through millions of images, accurately identifying which images have beds in them within a few minutes.
To achieve an acceptable level of accuracy, deep learning programs require access to immense amounts of training data and processing power, neither of which were easily available to programmers until the era of big data and cloud computing. Because deep learning programming can create complex statistical models directly from its own iterative output, it is able to create accurate predictive models from large quantities of unlabeled, unstructured data. This is important as the internet of things (IoT) continues to become more pervasive because most of the data humans and machines create is unstructured and is not labeled.
Deep learning examples
Because deep learning models process information in ways similar to the human brain, they can be applied to many tasks people do. Deep learning is currently used in most common image recognition tools, natural language processing (NLP) and speech recognition software. These tools are starting to appear in applications as diverse as self-driving cars and language translation services.
Use cases today for deep learning include all types of big data analytics applications, especially those focused on NLP, language translation, medical diagnosis, stock market trading signals, network security and image recognition.
Specific fields in which deep learning is currently being used include the following:
Customer experience (CX). Deep learning models are already being used for chatbots. And, as it continues to mature, deep learning is expected to be implemented in various businesses to improve CX and increase customer satisfaction.
Text generation. Machines are being taught the grammar and style of a piece of text and are then using this model to automatically create a completely new text matching the proper spelling, grammar and style of the original text.
Aerospace and military. Deep learning is being used to detect objects from satellites that identify areas of interest, as well as safe or unsafe zones for troops. Industrial automation. Deep learning is improving worker safety in environments like factories and warehouses by providing services that automatically detect when a worker or object is getting too close to a machine.
Adding color. Color can be added to black-and-white photos and videos using deep learning models. In the past, this was an extremely time-consuming, manual process.
Medical research. Cancer researchers have started implementing deep learning into their practice as a way to automatically detect cancer cells.
Computer vision. Deep learning has greatly enhanced computer vision, providing computers with extreme accuracy for object detection and image classification, restoration and segmentation.
DoNotPay, the company that offers the “world’s first robot lawyer,” has announced a new AI-powered chatbot that will help you negotiate bills, unsubscribe, and more.
The latest tool from DoNotPay can have a back-and-forth conversation with a company’s customer service representative through live chat or email.
In a demo of the tool posted by DoNotPay CEO Joshua Browder, the chatbot manages to get a discount on a Comcast internet bill through Xfinity’s live chat. Once it connects with a customer service representative, the bot asks for a better rate using account details provided by the customer. The chatbot cites problems with Xfinity’s services and threatens to take legal action, to which the representative responds by offering to take $10 off the customer’s monthly internet bill.
This tool builds upon the many neat services DoNotPay already offers, which mainly allows customers can generate and submit templates to various entities, helping them to file complaints, cancel subscriptions, fight parking tickets, and much more. It even uses machine learning to highlight the most important parts of a terms of service agreement and helps customers shield their photos from facial recognition searches. But this is the first time DoNotPay’s using an AI chatbot to interact with a representative in real time.
DoNotPay’s bot issues convincingly human-like answers throughout the entire interaction with Xfinity, save for a hiccup where the tool says “[insert email address]” instead of providing the customer’s actual email. Browder tells The Verge that DoNotPay will clean up some of its responses before it goes live — and make the bot sound less polite, as it’s pretty heavy on the “thank-yous.”
DoNotPay’s bot is built on top of OpenAI’s GPT-3 API, the underlying toolset used by OpenAI’s ChatGPT chatbot that tons of people have been playing around with to generate detailed (and sometimes nonsensical) responses. DoNotPay’s tool is made for a specific purpose, though, and Browder seems to view it as an opportunity to expand the number of tasks it can tackle, like chatting with a representative to cancel a customer’s subscription or negotiating a credit report.
If the chatbot doesn’t know an answer to a particular question, Browder says it won’t start making things up. “It will just stop in its tracks and ask the user for help” when it’s unsure, Browder explains. The company’s working on ways to alert users whenever this happens so that they don’t have to sit in front of their computer and monitor the tool. Browder tells The Verge that users could eventually respond to the AI’s questions over text message so that it can continue its “conversation.”
By building CICERO, Meta AI has created the first AI agent to achieve human-level performance in the complex natural language strategy game Diplomacy. CICERO demonstrated this by playing with humans on webDiplomacy.net, an online version of the game, where CICERO achieved more than double the average score of the human players and ranked in the top 10% of participants who played more than one game. The AI sent 5,277 messages to players in 72 hours of gameplay, and almost no one realized that they weren’t communicating with a human. Only one person expressed some suspicion, that one of the AI accounts is a bot.
This breakthrough rests in the achievement of combining two different areas of AI: strategic reasoning and natural language processing. The integration of these techniques gives CICERO the ability to reason and strategize with regard to players’ motivations, then use natural language to communicate, reach agreements to achieve shared objectives, form alliances and coordinate plans.
The Cicero is only able to play Diplomacy, but the technology behind it is relevant to many other applications. Current AI assistants can perform simple question-and-answer tasks, such as providing information about the weather, but with the further development of artificial intelligence, according to Meta AI specialists, they could even conduct long conversations to teach someone a new skill.
The British artificial intelligence company – DeepMind – has created an AI tool that helps in writing the script – it will generate character descriptions, plot points, as well as dialogue and location descriptions. Artificial intelligence has certainly helped many people become an “artist”, and with the help of a new tool designed by DeepMind, it will also support aspiring screenwriters.
Dramatron is a system that uses large language models that could be useful for authors for co-writing theatre scripts and screenplays. Dramatron uses hierarchical story generation for consistency across the generated text. Starting from a log line, Dramatron interactively generates character descriptions, plot points, location descriptions and dialogue. These generations provide human authors with material for compilation, editing, and rewriting.
To assess the usability and capabilities of Dramatron, DeepMind engaged 15 playwrights and screenwriters for two-hour research sessions with users to co-create scenarios with the tool. Respondents suggested that it would be useful for world building and would help them test other approaches in terms of changing plot elements or characters. They also noted that AI can be a great way to “generate creative ideas.”
What about copyright? Using Dramatron may raise questions about authorship. Last year, a UK appeals court ruled that artificial intelligence could not legally be considered an inventor and patented. DeepMind points out that Dramatron may display snippets of text that were used to train the language model, which, if used in a produced script, could lead to accusations of plagiarism. Therefore, it is always worth checking the generated scenario carefully in this respect.
Teknita has the expert resources to support all your technology initiatives. We are always happy to hear from you.
Metaverse is the next evolution of digital technologies. It includes 3D virtualization and will transform digital technologies in the next 5–10 years. It is a collective virtual space, created by the convergence of virtually enhanced physical and digital reality. In other words, it is device-independent and is not owned by a single vendor. It is an independent virtual economy, enabled by digital currencies and nonfungible tokens (NFTs).
A Metaverse represents a combinatorial innovation, as it requires multiple technologies and trends to function. Contributing tech capabilities include augmented reality (AR), flexible work styles, head-mounted displays (HMDs), an AR cloud, the Internet of Things (IoT), 5G, artificial intelligence (AI) and spatial technologies.
There is a lot of excitement around Metaverse, much of it driven by technology companies preemptively claiming to be Metaverse companies, or creating Metaverses to enhance or augment the digital and physical realities of people. Moreover, activities that currently take place in siloed environments will eventually take place in a single Metaverse, such as:
Purchasing outfits and accessories for online avatars
Buying digital land and constructing virtual homes
Participating in a virtual social experience
Shopping in virtual malls via immersive commerce
Using virtual classrooms to experience immersive learning
Buying digital art, collectibles and assets (NFTs)
Interacting with digital humans for onboarding employees, customer service, sales and other business interactions
Elements of a Metaverse
Gartner is a technology research and consulting company. It described the elements of a Metaverse in the below diagram.
Gartner expects that by 2026, 25% of people will spend at least one hour a day in the Metaverse for work, shopping, education, social media and/or entertainment.
