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Artificial Intelligence Revolution 2020
sherry 0 Data Science, Design, Engineering, Explaining of Israel, Technology,
By Avraham David Sherwood
The Editor in Chief
The field of artificial intelligence has been gaining momentum in recent years and has revolutionized diverse industries. This article concentrates on all the latest technology news, backed up by facts and statistics about revolutionary technology: artificial intelligence.
Facts:
These statistics clearly demonstrate that the field of artificial intelligence will capture a significant share of the lives of us all in the coming years. Following the hype outbreaks of recent years, researchers estimate that 41% of consumers think their lifestyle will change following the AI revolution. Statistics cannot be ignored, especially in the field of which statistics is a relatively central issue, so the question arises how will this actually affect people’s lives in the future? And where did the idea of AI come from?
Artificial intelligence means acquiring processing, analyzing, and computing capabilities for computer-based (machine) -based software and the human brain. All applications running on the Internet or cellular that are AI-based are capable of performing the various functions in place of humans. This rationale for an alternative to logical and cognitive thinking is now being carried out well using AI technologies.
Artificial intelligence technologies, including machine learning and deep learning, have revolutionized the software world to such an extent that developers could lose their jobs. AI-based applications have become supportive, interactive, and even successful in demonstrating humanity and accuracy in their task performance.
Although the concept did not exist hundreds of years ago and in principle there was no obstacle that prevented anyone from developing it, only about 6 decades ago there were a number of technology enthusiasts who began to explore this wonderful technology.
An evolutionary timeline of artificial intelligence:
From the 1950s to the present, the field of artificial intelligence (including machine learning, deep learning and other AI-based technologies) has reached almost all industries and high-quality and professional computer scientists have been recruited at high salaries to develop AI-based systems to provide added value to companies and organizations.
What is happening right now in the field of artificial intelligence?
Today there are companies that choose not to invest in this race, but this is a wrong move that will prove to be a critical business mistake. An organization that neglects streamlining and improving its database can bring about a business closure. On average, only about 22% of companies choose to invest in AI-based technologies, which is a very worrying situation. AI technologies provide a strategically important service that can add great value to businesses in all areas. We recommend incorporating AI experts into your business today to reap the benefits of artificial intelligence in the future.
(From a study by MIT Sloan Management and Boston Consulting Group).
Here are the top AI technologies for 2019:
Biometrics:
Provision of utmost security to citizens through artificial intelligence technologies. The best example of the impact of AI on our lives is the use of all of us in connecting to the face recognition system and unlocking the fingerprint on our smartphones.
Machine Learning:
This sub-domain is currently on the rise. It’s a subset of AI that allows computers (machines) to learn independently from a developer who will enter codes. Machine learning operates on algorithms based on complex statistical calculations and methods. The most successful machine learning packages for 2019 are: SPSS by IBM, Apache Spark ML, Scikit learning and Microsoft Azure.
The various uses that can be developed through machine learning-based software applications include image processing, search recommendations, autonomous cars, Google Maps, Google Translation and more.
Robotic Process Automation:
The robotics industry has sharpened the field of AI and catapulted it. The advent of automation in robotic processes has created the exchange of software products in human interactions, in a way that robots are capable of replacing human actions. This involves developing robots that are able to process mission information and provide sensory feedback.
AI technologies allow us to perform various tasks, such as: solving queries and performing complex calculations and with exceptional accuracy and speed.
Researchers believe that automation using robots based on AI technologies can have an impact on finance, human resources, accounting, hospitality, healthcare and more.
The Internet of Things:
The Internet of Things or IoT for short is usually using AI. Their applications and benefits define the trends of the software industry.
One of the most popular examples is automatic vacuum cleaners set up in 2002.
Today, the IoT industry is at its beginnings and is not widely public. You can briefly explain the combination of technologies as AI-based computer software that communicates with your mobile device. There are several examples of integrated IoT uses of AI, such as: self-driving vehicles and smart thermostat solutions. Researchers estimate that in the future we will see solutions that use these technologies for the purposes of emotional analysis, security, face recognition and more.
Neural Networks:
The human brain is activated by means of neurons that are interconnected in complex wands. Similarly, a programming structure is developed which manages to make computers (machines) work in a method known as neural networks. This term is known by its professional name as neural network programming (ANN) and is under technology called deep learning. Machine learning is the main technology of deep learning that runs through AI.
The term artificial neural networks first came into being in 1989. Researchers at Carnegie Mellon University were the first people to develop autonomous vehicles through neural networks.
Neural networks have evolved over the years, helping scientists understand how our brains work. Using artificial neural networks, attempts can be made instead of applying different stimuli to a human brain. This development improves our knowledge and contributes to significant advances in neuroscience, a separate field but which can bring breakthroughs in collaboration with the latest developments in artificial intelligence.
The researchers are divided into two main groups. On the one hand there are those who support and encourage the use of artificial intelligence based technologies and on the other there are those who seek to limit its use and closely monitor the uses of artificial intelligence based technologies. Both groups agree that the impact of AI will be significant for all of us over the next few years.
Already today we are managing our lifestyle through AI when we use Amazon’s Alexa, Apple’s Siri, or Microsoft’s Courtenna.
Here are the key trends for the coming years:
Who doesn’t know the robot Sophia by Hanson Robotics?
So in the near future, more robots of this kind are expected to arrive in the world, which are also based on artificial intelligence technologies combined with robotics, face recognition technologies and visual data processing.
Competition in the field of smart virtual assistants will increase. AI-based gadget performance will improve significantly and new features will be added to them. The smartwatches will be synchronized to Amazon’s Echo and Alexa that already dominate the smart virtual assistant market, making our lives simpler and more accessible.
In the chatbots field, serious improvements will be introduced in the users’ answers. They will be more efficient, accurate and interactive. All apps will be synchronized to allow you to interact with empathetic and user-friendly chat bots, such as understanding your individual needs and more.
In the healthcare field, diverse AI-based devices will be developed to help doctors accurately diagnose medical problems, such as heart rate and breathing. The service that patients receive will be more efficient and faster, thanks to the use of artificial intelligence.
In the biometric field, a security revolution is expected to improve citizens’ security, so that face recognition becomes universal and even credit card billing can be carried out through voice commands and gestures. Traffic cops will be able to identify drivers without the need for a driver’s license, because their systems will be synchronized to the government biometric database and all they have to do is stand in front of the driver and the body camera will recognize it.
In the field of education, a personalization revolution is expected to help teachers understand the needs of each student, accurately assess achievement and performance, and adapt the learning material to an average classroom level and each student personally. Students will realize their potential at an early age and find the passion of what they do best and want to do in their lives and teachers will receive meaningful help that includes effective and effective management that is within their reach.
In the marketing field, AI-based platforms will be developed that can interact with customers through customized messaging that is targeted by new discovery and search methods including voice search. Optimization processes will be enhanced using artificial intelligence algorithms, so marketing executives will need to optimize their content according to new criteria applied by AI algorithms.
In conclusion, the field of artificial intelligence is a fascinating field that has entered our lives in a storm. Artificial intelligence technologies have tremendous power to influence the future and lifestyle of us all. The combination of computer science and neuroscience has created amazing technological developments that are capable of assisting in various complex situations and optimizing the capabilities of human beings to a high professional level.
Businesses are already interested in the latest developments today because if you are a true entrepreneur you cannot ignore the upcoming AI revolution and you should definitely harness the unique power of AI to leverage your business. Adding significant AI power to your business means providing efficiency, accuracy and a lot of added value.
Tags: A/B testing, academia, Accuracy, action, activation function, active learning, AdaGrad, administrative perspectives, agent, agglomerative clustering, AI, AI methods, analytics, analytics space, applied machine learning, ar, area under the PR curve, area under the ROC curve, artificial general intelligence, artificial intelligence, association, attribute, AUC (Area under the ROC Curve), Augmented Reality, automation bias, Automation of Machine-Learning, Autonomous driving, average precision, backpropagation, bag of words, baseline, batch, batch normalization, batch size, Bayesian, Bayesian neural network, Bellman equation, bias (ethics/fairness), bias (math), Big Data Analytics, bigram, binary classification, binning, boosting, broad spectrum, broadcasting, bucketing, calibration layer, candidate generation, candidate sampling, categorical data, centroid, centroid-based clustering, checkpoint, class, class-imbalanced dataset, classification, classification model, classification threshold, clipping, Cloud TPU, clustering, co-adaptation, collaborative filtering, comments, Computational neural networks, computer vision, Conference, confirmation bias, confusion matrix, content, Continuous delivery, continuous feature, convenience sampling, convergence, convex function, convex optimization, convex set, convolution, convolutional filter, convolutional layer, convolutional layers, convolutional neural network, convolutional operation, cost, counterfactual fairness, coverage bias, crash blossom, critic, Cross validation, cross-entropy, custom Estimator, Cyber, data analysis, data augmentation, Data Science, Data Scientists, data set or dataset, database, DataFrame, Dataset API (tf.data), decision boundary, decision threshold, decision tree, decision trees, deep learning, deep model, deep neural network, Deep Q-Network (DQN), delivering, demographic parity, dense feature, dense layer, depth, depthwise separable convolutional neural network (sepCNN), DESIGN, Developers, device, DevOps, Dialogue Bots, different hyperparameters, different initializations, different overall structure, dimension reduction, dimensions, discrete feature, discriminative model, discriminator, disparate impact, disparate treatment, divisive clustering, domains, downsampling, DQN, dropout regularization, dynamic model, eager execution, early stopping, Education, embedding space, embeddings, empirical risk minimization (ERM), engineering, ensemble, environment, Ethics of artificial intelligence, excellent, experience, experimenter’s bias, Explaining of Israel, fine tuning, Fintech, focus, forget gate, full softmax, fully connected layer, Future of AI, GAN, generalization, generalization curve, generalized linear model, generative adversarial network (GAN), generative model, generator, gradient, gradient clipping, gradient descent, Gradient descent algorithm, graph, graph execution, great success, greedy policy, ground truth, group attribution bias, hashing, Healthcare, helpful feedback, heuristic, hidden layer, hierarchical clustering, high-quality, hinge loss, holdout data, hyperparameter, hyperplane, i.i.d., ideas, image recognition, imbalanced dataset, implicit bias, improvement, in-group bias, incompatibility of fairness metrics, independently and identically distributed (i.i.d), individual fairness, industry, industry tracks, Inference, innovation, input function, input layer, instance, Intelligent robots, inter-rater agreement, interpretability, iot, item matrix, items, iteration, k-means, k-median, Keras, Kernel Support Vector Machines (KSVMs), label, labeled example, lambda, Large scale analytics, layer, Layers API (tf.layers), leading experts, learning rate, least squares regression, linear model, linear regression, Log Loss, log-odds, logistic regression, logits, Long Short-Term Memory (LSTM), loss, loss curve, loss surface, LSTM, Machine ethics, MACHINE LEARNING, majority class, Markov decision process (MDP), Markov property, matplotlib, matrix factorization, Mean Absolute Error (MAE), Mean Squared Error (MSE), metric, Metrics API (tf.metrics), mini-batch, mini-batch stochastic gradient descent (SGD), minimax loss, minority class, ML, MNIST, model, model function, model training, Momentum, momentum (Momentum), more complex math (Proximal and others), multi-class classification, multi-class logistic regression, multi-class regression, multinomial classification, N-gram, NaN trap, Natural language processing, Natural Language Understanding, negative class, neural network, neural networks, neuron, NLU, node (neural network), node (TensorFlow graph), noise, non-response bias, normalization, numerical data, NumPy, objective, objective function, offline inference, one-hot encoding, one-shot learning, one-vs.-all, online inference, Operation (op), optimizer, organizing conference, out-group homogeneity bias, outliers, output layer, Overfitting, pandas, parameter, Parameter Server (PS), parameter update, partial derivative, participants, participation bias, partitioning strategy, perceptron, performance, perplexity, pipeline, policy, pooling, positive class, post-processing, PR AUC (area under the PR curve), pre-trained model, precision, precision-recall curve, prediction, prediction bias, predictive applications, predictive parity, predictive rate parity, premade Estimator, preprocessing, prior belief, proxy (sensitive attributes), proxy labels, Q-function, Q-learning, quantile, quantile bucketing, quantization, queue, random forest, random policy, rank (ordinality), rank (Tensor), rater, re-ranking, real world, real-world domains, recall, recommendation system, Rectified Linear Unit (ReLU), recurrent neural network, Registration, regression, regression model, regularization, regularization rate, reinforcement learning, reinforcement learning (RL), replay buffer, reporting bias, representation, research and application, research innovations, research track, researchers, Retail, return, reward, ridge regularization, RNN, Robot rights, robotics, ROC (receiver operating characteristic) Curve, root directory, Root Mean Squared Error (RMSE), rotational invariance, sampling bias, SavedModel, Saver, scalar, scaling, scikit-learn, scoring, selection bias, semi-supervised learning, sensitive attribute, sequence model, serving, session (tf.session), shape (Tensor), sigmoid function, similarity measure, size invariance, sketching, softmax, sparse feature, sparse representation, sparse vector, sparsity, sparsity/regularization (Ftrl), spatial pooling, sponsorship, squared hinge loss, squared loss, State, state-action value function, state-of-the-art, static model, stationarity, step, step size, stochastic gradient descent (SGD), stride, structural risk minimization (SRM), subsampling, summary, supervised learning, supervised machine learning, support vector machines, synthetic feature, Systems for ML, tabular Q-learning, target, target network, technical presentations, TECHNOLOGY, temporal data, Tensor, Tensor Processing Unit (TPU), Tensor rank, Tensor shape, Tensor size, TensorBoard, TensorFlow, TensorFlow Playground, TensorFlow Serving, termination condition, test set, tf.Example, tf.keras, The conference, The Summit, Threat to human dignity, time series analysis, timestep, topics, tower, TPU, TPU chip, TPU device, TPU master, TPU node, TPU Pod, TPU resource, TPU slice, TPU type, TPU worker, training, training set, trajectory, transfer learning, translational invariance, trigram, true negative (TN), true positive (TP), true positive rate (TPR), tutorial, unawareness (to a sensitive attribute), underfitting, unlabeled example, unsupervised learning, unsupervised machine learning, update frequency, upweighting, user matrix, validation, validation set, vanishing gradient problem, Wasserstein loss, Weaponization of AI, weight, Weighted Alternating Least Squares (WALS), wide model, width
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