AI is not a new word. It dates back many decades, and computer scientists in the early 80s developed algorithms that learn and simulate human behavior. On the learning side, the most important algorithm is the neural network, which has not been very successful due to over-fitting (the model is very powerful and there is not enough data). However, in some specific tasks, the idea of using data to fit a function has been a considerable success and is the foundation of machine learning today. On the other hand, AI is very focused on image recognition, speech recognition, and natural language processing. Experts spend a lot of time creating features like edge detection, color profiles, n-grams, and syntax trees. However, success is moderate.Traditional machine learning (ML) techniques have played an important role in assessment, and ML has a rich set of model structures that have been subject to many generations: Linear regression. Logistic Regression. The decision tree. Support vector machine. The Bayesian model. Regularization Model. The ensemble model. The neural network. These models are based on some algorithmic structure for each of the attendance models, with the parameters being tunable knobs. Model Attendance model training consists of the following steps: Select the model structure (for example, logistic regression, random forest, etc.). Fill the form by exercise data (both input and output). The learning algorithm generates the optimal model (i.e. a model with specific parameters that minimizes training errors). Each model has its characteristics and works well in some tasks and is bad in others. But in general, we can classify them as a low-power (simple) model and a high-power (complex) model. Choosing between different models is a very tricky question. Traditionally, it is preferred to use a lower power / simple model than to use a higher power / complex model for the following reasons: Until we have huge processing power, training a high power model takes a long time. As long as we have a large amount of data, training a high power model results in a high fitting problem (since the high power model has great parameters and fits a wide range of data structure, we can train a very specific model) Not generalized enough to make a good estimate). However, selecting a low power model suffers from an "under-fit" problem where the model structure is very simple and cannot be matched if training data is more complex. (Note that g has a quadratic relation to the underlying data: y = 5 * x ^ 2; there is no way you can fit the linear regression: y = a * x + b, we choose $$ anonymous $$ n b.) The main obstacle to machine learning is this feature engineering phase, which requires domain professionals to identify vital signs before feeding into the training process. The feature engineering phase demands a lot of manual and very rare domain expertise and has therefore become a major barrier to most machine learning tasks today. In other words, if we do not have enough processing power and insufficient data, then we must use a low-power / linear model, which requires considerable time and effort to create appropriate input features. Most data scientists spend their time today. Return of the neural network In the beginning 2000s, computer processing power increased exponentially with cloud computing and massively parallel processing infrastructure, where large amounts of fine-grained event data were collected. We are no longer limited to a low power / simple model. For example, two of the most popular mainstream machine learning models today are random forest and gradient enhancing trees. However, although both are very powerful and provide non-linear model fitting to training data, data scientists still need to create features to achieve better performance. At the same time, computer scientists have re-examined the use of multiple layers of the neural network in performing these human simulations. This is a new birth for the DNN (Deep Neural Network) and provides significant advances in image classification and speech recognition. The main difference in DNN is that you can enter raw codes (for example, the RGB pixel value) directly into the DNN without creating domain specific input properties. Through several layers of neurons
We, humans, are pretty good at sugar coating, but what if we only built an algorithm or bot brand or company for the purpose of marketing?
It does the most amazing work!In the early 2000s, finding a product would be a nightmare if we searched online stores without knowing the exact name of the product.
But now when we search for an item in any e-commerce store, we get all the results of that item.
Eva can gather knowledge from thousands of sources and provide simple answers in less than 0.4 seconds.Artificial Intelligence Applications - AI in BankingAI in bankingUsing AI to prevent fraud is not a new concept.
This has saved millions of dollars.
Issues such as climate change, population growth, and food security have pushed the industry to seek more innovative approaches to improving crop yields.Early-based agricultural tech called Peat has developed an app called Start-Up Planets, which identifies potential deficiencies and nutrient deficiencies in the soil through images.
Resolving the problem of bias and variation is only about coping with over-fitting and under-fitting.
Bias is minimized, and the variance concerning the complexity of the model is increased.Read Full Article: https://bit.ly/2NsIaF4
Artificial intelligence matches human performance by learning, deriving conclusions on its own, and executing a clearly defined task.
Machine Learning is an artificial intelligence technology that uses data to automatically discover patterns and trends, resulting in deriving more accurate predictions of future events.Many organizations are leveraging ML and AI to advance their product development, sales, and overall customer experience.
Industries like manufacturing, construction, energy etc.
Most companies understand the value of a safety culture and have ideologies about what a hyper-vigilant safety culture looks like — where employees are on high alert of their surroundings, perpetually evaluating risks for the task at hand and taking precautions accordingly.
Algorithms can identify objects, edges, and velocity because of the advancements of Machine Vision.Workplace Safety Solutions through AI Computer Vision:Process and Production Safety - Majority of the workplace facilities have a CCTV system installed on the premises.
Vision-based object detection and tracking use feature such as shape, color, motion etc.
IntroductionArtificial Intelligence and data wisdom are good fields of operations, systems, and further that end at replicating mortal intelligence through machines.
AI represents action-planned feedback of perception.Data Science uses different corridors of this pattern or circle to interrupt specific problems.
Perception, data scientists attempt to identify patterns with the assistance of the info.
planning, there are two aspects Artificial Intelligence Artificial Intelligence (AI) is that the reproduction of human knowledge measures by machines, particularly PC frameworks.
Explicit utilizations of AI contain master frameworks, normal language preparing, discourse acknowledgment, and machine vision.Artificial Intelligence is critical because it can give endeavors experiences into their activities that they could not have known about already and since, now and again, AI can execute assignments better than people.
Especially with regards to dull, thorough assignments like examining enormous quantities of authoritative archives to ensure significant fields are filled in appropriately, AI apparatuses regularly complete positions rapidly and with somewhat a few blunders.Data scienceData science, analytics, and machine learning demand are growing at an astronomical rate and top companies are now trying to find professionals in Data science who can sift through the goldmine of knowledge and help them to require business decisions efficiently.Data science may be a concept wont to tackle big data and includes data cleansing, preparation, and analysis.
The process of data annotation, for speech, image, video, audio, or text, is a highly specialized task that needs expertise.
When we talk about high quality data, it means that the labels are both accurate and consistent.
By outsourcing data annotation services, companies save time and effort freeing up their in-house data scientists to focus on areas they are experienced in instead of annotating data.Here are a few of data annotation techniques used for specific AI initiatives.Audio Transcription for Speech Recognition Audio transcription is used to train speech recognition models.
High-accuracy audio transcription enables interactions to accurately happen between human speech and AI-based models like smart TVs, phones, virtual assistants, watches, computers, or other in-home or on-the-road technology.Human speech must be accurately recognized to understand not only what words are being spoken but also what they mean.
Text annotation detects and labels words depending on a predetermined set of categories required by an AI company.
Text annotation for Natural Language Processing or NLP helps machines predict and understand the human language more easily.
