Artificial Intelligence is a field of computing where intelligent machines uplift human cognitive abilities & experiences. AI can reproduce certain human-like behavior, such as interacting, recognizing, learning & understanding, making it a powerful technology. AI refers to a large field of science enclosing not only computer science but also philosophy, psychology & other areas. This technology is concerned with getting computers to do jobs that would generally require human intelligence.
The founder of AI Alan Turing defines this discipline as: “AI is the science and engineering of making intelligent machines, especially intelligent computer programs”.
It’s high time for the technology leaders to look at how AI can be used to improve speed, quality, functionality & even lead to excellent revenue growth.
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Narrow AI vs General AI
Narrow AI: A chess computer could defeat a human in playing chess, but it couldn’t solve a tough math problem. Practically all recent Artificial Intelligence is “narrow”, meaning it can only do what it is designed for. It means for every problem a particular algorithm requires to be designed to solve it. Narrow AI is mostly much better at the job they were made for than humans, like calculus, chess computers, translation, face recognition.
General AI: The holy dish of AI is a General AI, a single system that can learn about each & every problem and then solve it. This is precisely what humans do: we can specialize in a particular topic, from sports to art, from abstract mathematics to psychology and, we can become experts at all of them. An Artificial Intelligence and machine learning system integrates & uses mainly machine learning & several other types of data analytics methods to achieve artificial intelligence capabilities.
Applications of AI
The growing availability, precision, and ease of implementation of artificial intelligence methods generate opportunities for companies to use them in their business.
For example, insurance companies get started with AI to read claims from their clients, to have the understanding, of the claim is easy or difficult & it can give a suggestion on how to handle the claim. The insurance employee then only needs to do a quick check before approving the recommendation. It can save precious time & increase the quality of the work. This is just one example. Here we share 5 applications in which we will see a huge development in the coming years of lean transformation.
- Image recognition.
- Speech recognition.
These developments will make applications cheaper & more precise, opening the door for business to use them during organization transformation.
1. Image Recognition:
Different vendors like IBM & Google are offering their preprogrammed algorithms open source & software libraries like Tensorflow make it possible to develop your algorithms, visual recognition is becoming more accessible for the public. Popular applications of image recognition are Google’s shopper app or facial recognition for security cams.
IBM Watson, which we know from playing Jeopardy, has matured its image recognition expertise in the field of medicine. IBM Research has been operating on sound learning techniques for computer vision that could be utilized to recognize whether skin irregularities are melanoma. They developed a group of methods that can separate skin lesions & methods that can find the area and surrounding tissue for melanoma and tested it on a large publicly available dataset.
The vision of IBM is that at a particular point medical staff can send a picture of skin irregularities to Watson, the same way that they send blood samples to the lab. Facial recognition, which was used in security cameras, now has also been generated in other areas. In a survey, it was found that a quarter of all British shops use facial recognition software. The software is used for security, but also to track customers to observe their behavior as an effect of product displays or the traffic flow in the store.
It is the process of translating text from one language to another by using the artificial intelligence system. In place of working in a regulated way, powered by human decision-making, translation using a neural network is completely based on mathematics. On comparatively basic texts, the GNMT system translations approach the quality of human translators. A demonstration even conveyed that when you translate English to French and subsequently translate English to Japanese, the model can translate French to Japanese honestly well, without any previous training concentrated on the official link between the two languages.
3. Speech Recognition:
Speech recognition is an application for Artificial Intelligence that recognizes speech and can revolve spoken words into written words. It is barely used on its own, but it is widely used as an addition to Chatbots, virtual agents & mobile applications.
Popular examples are Microsoft’s Alexa, Apple’s Siri & Google Home. Today we have applications on our phone & in our home that can respond to our voice. One of the business applications is the use of speech recognition in health care. A lot of physicians are working with an electronic health record (HER) to record patient information. Using speech recognition, the patient record can be recorded in a flexible & quick manner, which allows the physician to give more attention to the patient.
4. Question Answering:
Q&A agents or Chatbots are other examples of applying AI technology to language. A chatbot can be concentrated on answering questions in an open or closed domain. When it operates in an open domain, it should be able to answer common questions that can concern any topic (for example Cleverbot).
Closed domains, however, have a very good business application such as responding to questions at customer service. Some years ago, there was a development in question answering interest, when IBM Watson defeat humans in a game of Jeopardy, a well-known American quiz show. Google made another development more recently, which now gives chatbots the ability to have a short-term memory, enabling them to mimic real-life conversations more realistically.
In the area of customer service, Chatbots are swiftly becoming the norm, one example being IPsoft’s Amelia. Automated systems already handle standard queries, forwarding only the difficult ones to human decision-makers.
5. Game/ Solver:
Playing a game well needs you to not only know the rules, but to calculate the next possible moves within these rules, and ultimately make a careful judgment on which move would give you to best chance to win.
A computer recently achieved a big milestone in the field of games by defeating the world Champion of Go for the first time. The top Go players of the world depend for a large part on their intuition to come to the best moves. Google’s AlphaGo, understood how to play like a top human player by studying millions of human games. It then became even stronger by playing against another version of itself millions of times, which finally enabled it to defeat the world champion. If computers can defeat human players in one of the most complicated games that currently exist, then where does the possibility for Artificial Intelligence stop?
Promote a culture that values innovation, experimentation, and collaboration between humans and machines by embracing AI as a strategic tool.
Artificial Intelligence in Healthcare
Artificial Intelligence (AI) has become increasingly prevalent in the healthcare industry in recent years. It has the potential to improve healthcare delivery, patient outcomes, and reduce costs.
AI-powered chatbots can also use to answer patient queries and provide basic medical advice. AI-powered tools can help doctors and other healthcare professionals diagnose and treat diseases more accurately and efficiently. For example, machine learning algorithms can analyze medical images to identify potential areas of concern or help radiologists diagnose conditions.
AI can also help healthcare providers identify patients who are at risk of developing certain conditions and provide proactive care. By analyzing patient data, AI can predict disease progression and identify the best treatment plans for individual patients.
Moreover, AI can help streamline administrative tasks such as scheduling appointments and managing patient data. AI-powered chatbots can also use to answer patient queries and provide basic medical advice.
AI-powered medical devices and wearables are also becoming increasingly popular. These devices can monitor vital signs, track patient behavior, and provide real-time alerts to healthcare providers in case of any abnormalities.
However, implementing AI in healthcare requires addressing several challenges such as data privacy, ethical considerations, and ensuring that AI-powered tools are accurate and reliable. Nevertheless, AI has the potential to transform healthcare delivery and improve patient outcomes in ways previously unimaginable.
In conclusion, AI has the potential to revolutionize the healthcare industry and improve patient outcomes. However, we must ensure that we use AI responsibly and ethically to maintain patient trust and privacy.
Which Companies are taking Full Advantage of AI?
- Amazon has used machine learning to lead suggestions for many years. The company is using deep learning to renovate business processes & to develop new product categories, such as its virtual assistant and maintaining its competency in digital transformation.
- Google has sketched its Artificial Intelligence specific chips to stimulate machine learning in its data centers & on IoT devices.
- China’s BATs – Baidu, Alibaba, and Tencent – are investing soundly in composition for agents in artificial intelligence while growing into areas previously controlled by US companies: autonomous vehicles, chip design & virtual assistants.
- These tech firms are using AI to generate billion-dollar services & to modify their operations. To develop their AI services, they’re following a friendly scenario:
- Find a solution to an internal challenge or opportunity,
- Perfect the solution at scale within the company and,
- Launch a service that swiftly attracts mass adoption. Hence, we see Microsoft, Amazon, Google, and China’s BATs launching machine learning and artificial intelligence development platforms and stand-alone applications to the broader market based on their own experience using them.
How Big Companies Use Artificial Intelligence In Practice
ABB progressively implemented ABB’s Predictive Emission Monitoring System (PEMS) as a segment of a complete Environmental Management system in one of the foremost gas processing plants in the world. The system uses an empirical model to forecast emission concentrations based on process data. PEMS – also known as an inferential analyzer – cannot compute emissions directly but uses an empirical model to predict emission concentrations based on process data, such as operating pressure, fuel flow, load & ambient air temperature. Additionally, virtual analyzers serve other purposes:
- Recognize the key variables that cause emissions.
- Automatically verify sensors.
- Revamp emission levels from actual data when the hardware device break down.
- Complement & amplify process optimization strategies.
- In the US, several states allow artificial intelligence (AI) technologies based on models like PEMS as an alternative monitoring technique.
2. Alphabet – Google-
Alphabet is Google’s parent company. Waymo, the company’s self-driving technology division, started as a project at Google. Today, Waymo wants to bring self-driving technology to the world not only to move people around but to decrease the number of crashes. Its autonomous vehicles are recently plying riders around California in self-driving taxis.
Chinese company Alibaba is the world’s biggest e-commerce platform that sells more than eBay & Amazon integrated. Alibaba uses artificial intelligence in its daily tasks and forecasts what customers might want to purchase. With natural language processing, the company automatically produces product descriptions for the site.
Another way Alibaba utilizes artificial intelligence is in its City Brain project to develop smart cities. The project uses computer language used for artificial intelligence algorithms to help decrease traffic jams by evaluating every vehicle in the city. Additionally, Alibaba, through its cloud computing division called Alibaba Cloud, is helping farmers detect crops to improve yield & cuts costs with artificial intelligence.
Apple which is one of the world’s biggest tech companies, selling customer electronics such as Apple Watches & iPhones and, as well as computer software & online services. This company uses machine learning & artificial intelligence in products like the iPhone, where it provides the FaceID feature, or in products like the HomePod, AirPods, Apple Watch, or smart speakers, where it enables the smart assistant Siri. It is using AI to help you find your photo in the iCloud & to suggest songs on Apple Music.
Amazon uses Artificial Intelligence in the game with its digital voice assistant, Alexa. Another ingenious way Amazon uses AI is to parcel things to you before you even think about buying them. By gathering extensive customer data and utilizing predictive analytics. They confidently anticipate and suggest items, catering to individual buying habits even before the customers realize their needs.
Amidst the struggle for market relevance, America’s largest e-tailer introduces the innovative Amazon Go, a cutting-edge convenience store concept. You don’t need any checkout in this store. Utilizing AI technology, the stores track selected items and seamlessly charge customers through the Amazon Go app on their phones. As there is no checkout, you bring your bags to collect with items & cameras are stalking each & every activity to detect every item you put in your bag to finally charge you for it.
As a leading AIaaS vendor, they incorporate intelligent features into products like Skype, Cortana, Office 365, and Bing.
One of the primary ways Facebook uses artificial intelligence technology & deep learning is to add structure to its unstructured data. They use DeepText, a text understanding engine, to automatically understand & interpret the content and emotional feeling of the hundreds of posts (in multiple languages) that its users post every millisecond. DeepFace helps social media to identify you in a photo that is shared on their platform. This technology is better at facial recognition than human beings. The company also uses artificial intelligence to automatically find & delete images that are published on its site as revenge porn.
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Leaders across industries must urgently consider how and where to invest in AI-based technologies. To harness the full potential of AI, businesses must grasp available technologies. Evaluate processes, data, and markets, aiming to boost speed, quality, functionality, and revenue growth.
But visualizing the possible is not just about the opportunities. Executives require to put on their insight. AI has the power to disrupt their business or even their entire industry. Now, it is time to start this discussion. In two to four years, it may be behind time.
Ensure openness, fairness, and responsible usage of AI while addressing potential biases and privacy problems by starting with ethical considerations.
Frequently Asked Questions
Where Artificial Intelligence is used?
Artificial Intelligence is used in a wide range of industries and applications, including healthcare, finance, transportation, manufacturing, education, and entertainment. AI is used for tasks such as image and speech recognition, natural language processing, predictive analytics, decision-making, and autonomous systems, among others.
When Artificial Intelligence invented?
Artificial Intelligence (AI) has a long and complex history, with roots going back to the early 20th century. However, the modern era of AI began in the 1950s, with the development of the first neural networks and the birth of the field of AI as a scientific discipline. Over the subsequent decades, AI has undergone numerous advances and setbacks.
- Automation, predictive analytics, and personalised experiences are made possible by AI, which boosts production, efficiency, and client pleasure across all industries.
- Upskilling, talent acquisition, and fostering an atmosphere that values diversity, innovation, and lifelong learning are all necessary for effective leadership in AI.
- Understanding AI’s limitations, taking into account its effects on society, and guaranteeing data security and quality are all necessary for effective AI applications.