How AI services can help our planet and ultimately benefit humanity. In this blog on Artificial Intelligence Applications, I discuss how AI has impacted healthcare, finance, agriculture, and various sectors.
Marketing
Marketing is one way to sugar coat your products to attract more customers. 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. It's like these search engines read our minds! In a matter of seconds, we get a list of all the relevant items. An example of this is finding the right movies on Netflix.
AI Applications - AI in marketing
AI in marketing
One reason we all love Netflix is that it provides the most accurate assessment technology based on the customer's reactions to films. It examines millions of records to indicate which shows and films you love based on your previous actions and selection of images. As the data set grows, the technology gets smarter and smarter every day. With the increasing advances in AI, it is possible for consumers on the web to purchase products by taking a photo of it in the near future. Companies like Comfind and their competitors are already experimenting with this.
Banking
AI in banking is growing faster than you think! Most banks have already adopted AI-based systems to provide customer support and to identify anomalies and credit card frauds. An example of this is the HDFC Bank.HDFC Bank has developed an AI-based chatbots called EVA (Electronic Virtual Assistant) built by Sense forth AI Research, based in Bangalore. Since its launch, Eva has solved over 3 million customer queries, interacted with more than half a million unique customers and conducted over one million conversations. Eva can gather knowledge from thousands of sources and provide simple answers in less than 0.4 seconds.
Artificial Intelligence Applications - AI in Banking
AI in banking
Using AI to prevent fraud is not a new concept. In fact, AI solutions can be used to increase security in many business sectors, including retail and finance. By identifying card usage and endpoint access, security professionals are effectively preventing fraud. Organizations rely on AI to learn those steps by analyzing transaction behaviors. Companies like MasterCard and RBS World Pay rely on AI and deep learning to detect fraudulent transactions and prevent card fraud. This has saved millions of dollars.
Finance
Ventures rely on computers and data scientists to determine future models in the market. Business depends mainly on the ability to accurately predict the future. Machines are great at this because they can crunch huge amounts of data in a short period of time. Machines can also learn to observe patterns in past data and it how these patterns can be repeated in the future. In the ultra-high-frequency trading era, financial institutions are leaning towards AI to improve their stock trading performance and maximize profits.
Artificial Intelligence Applications - AI in Finance
AI in finance
One such company is Nomura Securities, Japan's leading brokerage house. The company is reluctantly achieving a goal that is, analyzing the insights of experienced stock traders with the help of computers. After years of research, Nomura jumped in to introduce a new stock trading system. The new system stores large amounts of price and trading data on its computer. By tapping into this information reservoir, it performs assessments. For example, it determines that current market conditions are identical to those of two weeks ago, and how it may change for a few minutes. It helps to make good trading decisions based on market prices.
Agriculture
The terrible fact here is that by 2050 the world will have to produce 50 percent more food because we are eating everything! The only way this is possible is if we use our resources more carefully. With that said, AI helps farmers get the most out of the land while using resources more consistently. 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. The Image Recognition app detects possible errors with images captured by the user's smartphone camera. Customers offer soil restoration techniques, tips, and other solutions. The company claims that its software can achieve pattern recognition with an estimated accuracy of up to 95%.
Health care
When it comes to survivors, most organizations and medical care centers rely on AI. There are many examples of how AI has helped patients around the world in health care. A company called Cambio Health Care has developed a Clinical Decision Support System for stroke prevention, which can alert a physician when a patient is at risk of heatstroke.
Artificial Intelligence Applications - AI in Health Care
AI in Healthcare
Another example of this is Cola Life, a digitalized device that can detect heart disease. Similarly, iFloo is developing a system to track how people are doing in nursing homes and home care. The best thing about AI in health care is that you don't even have to develop new. By using existing medicines in the right way, you can save lives too.
Gaming
Over the past few years, artificial intelligence has become an integral part of the gaming industry. One of the biggest achievements of AI is in the gaming industry.DeepMind's AI-based Alpha Go software, known for beating world champion Lee Se dol in the GO game, is considered one of the most important achievements in the AI field.Shortly after the victory, DeepMind created an advanced version of Alfago called AlphaGo Zero, which defeated its predecessor in the face of AI-AI. Unlike the original AlphaGo, which was trained over time by using Deep Mind large amounts of data and monitoring, the advanced system taught them to master the game. Other examples of artificial intelligence in gaming include First Encounter Assault Recon, also known as F.E.A.R, a first-person shooter video game.
But what makes this game so special?
The actions taken by rival AI are inconceivable because the game is designed to train opponents throughout the game and never repeat the same mistakes. They improve as the game gets harder. This makes the game more challenging and asks players to constantly change tactics and never sit in the same position.
Space exploration
Space missions and discoveries will always require analyzing large amounts of data. Artificial intelligence and machine learning are the best way to manage and process data at this level. After rigorous research, astronomers used artificial intelligence to detect an eight-year planetary solar system acquired by the Kepler telescope. Artificial intelligence is also being used for NASA's next rover mission, the Mars 2020 rover. AEGIS, an AI-based Mars rover, is already on the red planet. Rover is responsible for the autonomous goal of the cameras to research Mars.
Autonomous vehicles
For a long time, self-driving cars have been a buzzword in the AI industry. The development of autonomous vehicles will surely revolutionize the transportation system.
Companies such as Waymo conducted several test drives in Phoenix before implementing their first AI-based public ride-hailing service. The AI system collects data from the vehicle's radar, cameras, and GPS and cloud services and generates control signals that govern the vehicle. Advanced deep learning algorithms can accurately predict what objects are near the vehicle. This makes Waymo cars more efficient and safer.
Another well-known example of an autonomous vehicle is Tesla's self-driving car. Artificial intelligence allows computer vision, image detection and deep learning to automatically detect objects to build cars and rotate without human intervention.Elon Musk talks a ton about how AI is implemented in Tesla's self-driving cars and autopilot features. He quoted it,"Tesla will have fully self-driving cars by the end of this year, and the" Robotaxi "version - which can carry passengers without anyone behind the wheel - is ready for the streets next year."
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There is also a lot of exciting research around "ML on code": automated detection of risky pull requests, automatic bug localization, intelligent IDE help, and so on.
Overall, I think we are going through a really interesting time to apply ML techniques to software development, security, and operations.
Here are 30 more tests that can help you achieve greater coverage.
The thing that takes 10 to 20 years can only take five years.
By solving and automating small tasks daily to make intelligent decisions, you will be able to deliver more, faster, more quality and with less human involvement.
The simplicity with which we introduce feedback and iterative cycles has allowed us to redirect and focus on outcome-led programming.
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.
