What are the current trends in AI ?

• Read original article here

Top Artificial Intelligence Trends to Watch Out this Year

Artificial Intelligence (AI) was invented several decades ago. In the past, many people associated AI with robots. But, it plays a crucial role in our lives now. Personal gadgets, media streaming gadgets, smart cars, and home appliances use artificial intelligence. Also, businesses use it to improve customer experience and management functions. Here are six artificial intelligence trends to look out for in 2021

Each business should strive to offer an enjoyable customer experience. Satisfying existing customers helps businesses market new products and services. AI enables firms to improve their customer service by offering better response time and interaction.

Artificial system assistance includes sales tasks and customer services. It will be more streamlined this year.

Digital marketing experts predict that customer service representatives won’t be required to manage over 85 percent of customer support communication by December. Companies can use programs and applications with Artificial Intelligence systems to build brand reputation and loyalty. They will help them increase their revenue.

Data is making Artificial Intelligence more versatile. Data access enabling ubiquity is one of the recent Artificial Intelligence innovations this year. Reliable and accurate information helps businesses shift to AI-powered automated decision making. It has cut operational cost, streamlined processes, and improved the research capabilities of many organizations.

For example, developers of the autonomous car software can access a lot of driving data without driving the vehicles. Soon, we will witness a drastic increase in the application of Artificial Intelligence in real-world simulations. As AI becomes more sophisticated, it will cause cost-effective and widespread availability of crucial data.

Artificial Intelligence, NLP, and machine learning to process data have a positive effect on augmented analytics . More companies will start using predictive analytics this year. It is essential in customer service, recruitment, price optimization, retail sales, and supply chain improvement. Predictive analytics will help businesses use real data to prepare for outcomes and behaviors thus being more proactive.

Companies need to understand delivery services and customer preferences to have an edge over their competitors. All-pervasive location and real-time data have conformed customer services in online marketplaces and urban mobility. Businesses need to offer relevant and personalized services to remain relevant and widen their client base.

Instant data on current marketing decisions is part of real-time marketing. It relies on relevant trends and customer feedback to prepare strategies. The number of real-time marketing activities is expected to soar in 2020, and Artificial Intelligence will drive most of them. Besides, more companies will apply AI to manage real-time user interactions and satisfy clients.

Many businesses use chatbots to market products and make payments. They are efficient in offering exemplary customer service. Many chatbots use data from huge databases. But, they might not comprehend particular phrases. Chatbots will match human conversation this year. For instance, AI-driven chatbots can recall some parts of a conversation with a client and make a personalized conversation using them.

Artificial intelligence has many possibilities. It is one of the most important technologies in Industry 4.0 and automation, agriculture, aerospace, construction, logistics, robotics, and connected mobility. AI customer support and assistance, data access enabling ubiquity, predictive analysis, enhanced customization, real-time marketing activities, and AI-powered chatbots are the top artificial intelligence trends this year.

The Growing Role Of AI And Machine Learning In Hyperautomation

an IT mega-trend identified by market research firm Gartner, is the idea that most anything within an organization that can be automated — such as legacy business processes — should be automated. The pandemic has accelerated adoption of the concept, which is also known as “digital process automation” and “intelligent process automation.” AI and machine learning are key components — and major drivers — of hyperautomation (along with other technologies like robot process automation tools). To be successful hyperautomation initiatives cannot rely on static packaged software. Automated business processes must be able to adapt to changing circumstances and respond to unexpected situations.

That’s where AI, machine learning models and deep learning technology come in, using “learning” algorithms and models, along with data generated by the automated system, to allow the system to automatically improve over time and respond to changing business processes and requirements. (Deep learning is a subset of machine learning that utilizes neural network algorithms to learn from large volumes of data.)

Only about 53 percent of AI projects successfully make it from prototype to full production, according to Gartner research. When trying to deploy newly developed AI systems and machine learning models, businesses and organizations often struggle with system maintainability, scalability and governance, and AI initiatives often fail to generate the hoped-for returns.

Businesses and organizations are coming to understand that a robust AI engineering strategy will improve “the performance, scalability, interpretability and reliability of AI models” and deliver “the full value of AI investments,” according to Gartner’s list of Top Strategic Technology Trends for 2021 Developing a disciplined AI engineering process is key. AI engineering incorporates elements of DataOps, ModelOps and DevOps and makes AI a part of the mainstream DevOps process, rather than a set of specialized and isolated projects, according to Gartner.

Increased Use Of AI For Cybersecurity Applications

Artificial intelligence and machine learning technology is increasingly finding its way into cybersecurity systems for both corporate systems and home security.

Developers of cybersecurity systems are in a never-ending race to update their technology to keep pace with constantly evolving threats from malware, ransomware, DDS attacks and more. AI and machine learning technology can be employed to help identify threats, including variants of earlier threats.

AI-powered cybersecurity tools also can collect data from a company’s own transactional systems, communications networks, digital activity and websites, as well as from external public sources, and utilize AI algorithms to recognize patterns and identify threatening activity — such as detecting suspicious IP addresses and potential data breaches.

AI use in home security systems today is largely limited to systems integrated with consumer video cameras and intruder alarm systems integrated with a voice assistant, according to research firm IHS Markit . But IHS says AI use will expand to create “smart homes” where the system learns the ways, habits and preferences of its occupants — improving its ability to identify intruders.

The Intersection Of AI/ML and IoT

The Internet of Things has been a fast-growing area in recent years with market researcher Transforma Insights forecasting that the global IoT market will grow to 24.1 billion devices in 2030, generating $1.5 trillion in revenue.

The use of AI/ML is increasingly intertwined with IoT. AI, machine learning and deep learning, for example, are already being employed to make IoT devices and services smarter and more secure. But the benefits flow both ways given that AI and ML require large volumes of data to operate successfully — exactly what networks of IoT sensors and devices provide.

In an industrial setting, for example, IoT networks throughout a manufacturing plant can collect operational and performance data, which is then analyzed by AI systems to improve production system performance, boost efficiency and predict when machines will require maintenance.

What some are calling “Artificial Intelligence of Things: (AIoT) could redefine industrial automation.

Earlier this year as protests against racial injustice were at their peak, several leading IT vendors, including Microsoft, IBM and Amazon, announced that they would limit the use of their AI-based facial recognition technology by police departments until there are federal laws regulating the technology’s use, according to a Washington Post story That has put the spotlight on a range of ethical questions around the increasing use of artificial intelligence technology. That includes the obvious misuse of AI for “deepfake” misinformation efforts and for cyberattacks. But it also includes grayer areas such as the use of AI by governments and law enforcement organizations for surveillance and related activities and the use of AI by businesses for marketing and customer relationship applications.

That’s all before delving into the even deeper questions about the potential use of AI in systems that could replace human workers altogether.

December 2019 Forbes article said the first step here is asking the necessary questions — and we’ve begun to do that. In some applications federal regulation and legislation may be needed, as with the use of AI technology for law enforcement.

In business, Gartner recommends the creation of external AI ethics boards to prevent AI dangers that could jeopardize a company’s brand, draw regulatory actions or “lead to boycotts or destroy business value.” Such a board, including representatives of a company’s customers, can provide guidance about the potential impact of AI development projects and improve transparency and accountability around AI projects.

Informing clinical decision making through insights from past data is the essence of evidence-based medicine. Traditionally, statistical methods have approached this task by characterising patterns within data as mathematical equations, for example, linear regression suggests a ‘line of best fit’. Through ‘machine learning’ (ML), AI provides techniques that uncover complex associations which cannot easily be reduced to an equation. For example, neural networks represent data through vast numbers of interconnected neurones in a similar fashion to the human brain. This allows ML systems to approach complex problem solving just as a clinician might by carefully weighing evidence to reach reasoned conclusions. However, unlike a single clinician, these systems can simultaneously observe and rapidly process an almost limitless number of inputs. For example, an AI-driven smartphone app now capably handles the task of triaging 1.2 million people in North London to Accident & Emergency (A&E).

Furthermore, these systems are able to learn from each incremental case and can be exposed, within minutes, to more cases than a clinician could see in many lifetimes. This is why an AI-driven application is able to out-perform dermatologists at correctly classifying suspicious skin lesions or why AI is being trusted with tasks where experts often disagree, such as identifying pulmonary tuberculosis on chest radiographs.

Although AI is a broad field, this article focuses exclusively on ML techniques because of their ubiquitous usage in important clinical applications.

Research has focused on tasks where AI is able to effectively demonstrate its performance in relation to a human doctor. Generally, these tasks have clearly defined inputs and a binary output that is easily validated. In classifying suspicious skin lesions, the input is a digital photograph and the output is a simple binary classification: benign or malignant. Under these conditions, researchers simply had to demonstrate that AI had superior sensitivity and specificity than dermatologists when classifying previously unseen photographs of biopsy-validated lesions.

AI is supporting doctors, not replacing them

Machines lack human qualities such as empathy and compassion, and therefore patients must perceive that consultations are being led by human doctors. Furthermore, patients cannot be expected to immediately trust AI; a technology shrouded by mistrust.

Therefore, AI commonly handles tasks that are essential, but limited enough in their scope so as to leave the primary responsibility of patient management with a human doctor. There is an ongoing clinical trial using AI to calculate target zones for head and neck radiotherapy more accurately and far more quickly than a human being. An interventional radiologist is still ultimately responsible for delivering the therapy but AI has a significant background role in protecting the patient from harmful radiation.

A single AI system is able to support a large population and therefore it is ideally suited to situations where human expertise is a scarce resource. In many TB-prevalent countries there is a lack of radiological expertise at remote centres.

Using AI, radiographs uploaded from these centres could be interpreted by a single central system; a recent study shows that AI correctly diagnoses pulmonary TB with a sensitivity of 95% and specificity of 100%.

Furthermore, under-resourced tasks where patients are experiencing unsatisfactory waiting times are also attractive to AI in the form of triage systems.

Developing ML models requires well-structured training data about a phenomenon that remains relatively stable over time. A departure from this results in ‘over-fitting’, where AI gives undue importance to spurious correlations within past data. In 2008, Google tried to predict the seasonal prevalence of influenza using only the search terms entered into its search engine. Because people’s searching habits change dramatically with every passing year, the model was so poorly predictive of the future that it was quickly discontinued.

Additionally, data that are anonymised and digitised at source are also preferable, as this aids in research and development.