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Home Articles

Machine Learning And AI Trends: Update Your Knowlegde

by Feeba Mahin
April 16, 2022
in Articles, Data Science and Machine Learning
Machine Learning And AI Trends Update Your Knowlegde
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Table of Contents

  • By 2022, top artificial intelligence (AI) trends will boost the technology-driven industry.
  • Conclusion

Artificial intelligence (AI) and machine learning (ML) have been popularized over recent years, as more industries are looking at their potentials and seeing how they can use one or both to evolve their business and customer experiences. Also, as the usage of these technologies increase, pricing becomes more affordable for SMBs.Eventhough some may feel these miracles as something from science fiction, the truth is, they have already become a reality in many workplaces.

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By 2022, top artificial intelligence (AI) trends will boost the technology-driven industry.

Artificial intelligence is transforming the technology sector by assisting organizations in achieving their objectives, making important choices, and developing novel goods and services. Companies are expected to have 35 artificial intelligence initiatives in their businesses by 2022. The AI and machine learning industry are expected to expand at a CAGR of 44% to US$9 billion by 2022. Several advancements in AI and machine learning technology have occurred in recent years. In this blog, we will discuss some of the most important AI trends for 2022.

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AI’s Expanded Role in Hyper Automation

Hyper automation is the process of automating operations utilizing sophisticated technology. Digital process automation and intelligent process automation are other terms for the same thing. Robotic process automation (RPA), artificial intelligence (AI), machine learning (ML), cognitive process automation, and intelligent business process management software are some of the advanced technologies commonly utilized in hyper-automation (iBPMS). Companies may use conversational AI and RPA to react to client inquiries automatically and enhance their CSAT score. Companies can minimize employee manual labor and boost productivity by automating time-consuming procedures. Hyper automation allows companies to integrate digital technology into their processes. Hyper automation is one of the finest AI trends.

 

Artificial Intelligence (AI) in Cybersecurity

Information security is increasingly reliant on AI technologies. Organizations are creating new techniques to make cybersecurity more automated and risk-free with the aid of AI. AI is assisting businesses in enhancing their cloud migration plan and enhancing the effectiveness of big data technologies. By 2026, the market for AI and machine learning in cybersecurity is expected to reach US$38.2 billion. Cybersecurity entails a large number of data points. As a result, AI may be utilized in cybersecurity to cluster, categorize, analyze, and filter data. AI helps you correlate multiple data sets and search for risks by organizing data in a certain manner.

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Forecasting and analysis of the business

Business forecasting and analysis using Artificial Intelligence and Machine Learning have shown to be far more straightforward than any prior approach or technology. You may consider thousands of matrices using AI and ML to generate more accurate predictions and forecasts. Fintech businesses, for example, are using AI to anticipate demand for multiple currencies in real-time based on market circumstances and customer behavior. It aids Fintech firms in having the correct quantity of supply to satisfy demand.

The Evolution of Augmented Intelligence

One of the popular AI trends is Augmented Intelligence. The combination of robots and humans to improve cognitive performance is known as augmented intelligence. According to Gartner, 40% of infrastructure and operations teams will utilize AI-augmented automation to boost IT efficiency by 2023. In fact, by 2022, the contribution of digital employees will have increased by 50%. Platforms with augmented intelligence may collect all sorts of data, both structured and unstructured, from multiple sources and show it in a 360-degree picture of consumers. Financial services, healthcare, retail, and travel are all examples of industries where augmented intelligence is becoming more prevalent.

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The intersection of AI and ML with the Internet of Things (IoT)

Artificial Intelligence (AI) and machine learning (ML) are rapidly being used to make IoT devices and services smarter and more secure. According to Gartner, by 2022, over 80% of IoT initiatives in companies will use AI and ML. The Internet of Things entails connecting all of your gadgets to the internet and allowing them to respond to various scenarios based on the data they collect. The following are the key segments where AI and machine learning intersect:

Fitness and health trackers, heart rate monitoring apps, and AR/VR gadgets that employ AIoT, such as smartwatches, AR & VR goggles, and wireless earphones, are examples of wearables.

AIoT is being utilized to make cities safer and easier to live in. Smart energy networks, smart street lighting, and smart public transit are just a few examples.

AIoT is utilized to optimize operations, logistics, and supply chain by providing real-time data analytics.

AI in Healthcare

COVID patients have been identified using big data extensively. AI is already assisting the healthcare industry in a significant way and with high accuracy. Thermal cameras and mobile applications have also been created by researchers to monitor individual temperatures and collect data for healthcare institutions. Artificial Intelligence can help healthcare institutions in a variety of ways by analyzing data and anticipating various outcomes. AI and machine learning tools provide insights into human health and also propose illness prevention measures. AI technologies also allow doctors to follow their patients health from afar, increasing teleconsultation and remote treatment.

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Natural Language Processing (NLP)

NLP is currently one of the most commonly utilized artificial intelligence applications. The rising popularity of NLP can be attributed to its widespread use by Amazon Alexa and Google Home. NLP has reduced the necessity for writing or interacting with a screen since humans can now speak with machines that comprehend their language. Sentiment analysis, machine translation, process description, auto-video caption creation, and chatbots are all anticipated to grow in popularity by 2022.

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Conversational Artificial Intelligence

Conversational Artificial Intelligence, or AI-powered chatbots, improve the reach, responsiveness, and customization of the customer experience. Conversational AI solutions, according to Forrester, result in improved customer service automation. An AI-powered chatbot utilizes natural language processing (NLP) and machine learning to create a more natural, near-human-level conversation by better understanding what the human says and needs. This is also one of the best AI trends.

 

Demand for ethical AI is on the rise

There is a rising need for ethical AI, which is at the top of the list of new technological advances. According to researchers, CIOs will be required to adapt to digital acceleration while also proactively managing uncertainty and business continuity through the ethical use of artificial intelligence in the coming decade. Given how quickly trends change, customers and employees with strong values want firms to use artificial intelligence responsibly. In the next years, businesses will actively seek out partners that are devoted to data ethics.

Quantum AI 

Advanced businesses will begin exploiting quantum supremacy to measure qubits for usage in supercomputers. Quantum computers solve problems faster than traditional computers because of quantum bits. They also aid in the understanding of data and the forecasting of numerous distinct trends. Quantum computers will assist a variety of businesses in identifying inaccessible challenges and predicting viable remedies. Future computers will also be able to handle a wide range of applications in sectors such as healthcare, finance, and chemistry.

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Conclusion

1: Which of the following algorithms is most suitable for classification tasks?

2: Which metric is generally used to evaluate the performance of a regression model?

3: Which of the following is not a feature scaling method?

4: In machine learning, what is overfitting?

5: Which type of machine learning algorithm is used to predict a continuous output?

    Fill out the form to see the results



    Ever wondered how much you really know? It's time to put your brain to the test!

    So, as you can see, not only do IoTs benefit from the combined power of AI and Machine Learning, but so do businesses and industries. With faster data generations, more accurate analyses, and more concise forecasts, it seems that both AI and ML are on the right track. Despite both parties still being in the “testing” processes, as time progresses, and we see unprecedented things like COVID-19 (which we haveve discussed in one of the above points), they continue to show plenty of promise of what’s to come in many industries. And it seems that both trends show no signs of slowing down any time soon.

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    Feeba Mahin

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