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Artificial intelligence (AI) has introduced many opportunities for various industries. Today, developed to developing and under-developed countries all are racing the battle of becoming AI-driven nations. Governments worldwide perceive AI as a key defining technology that can bring socio-economic growth development. Hence, various countries’ leaders and their policymakers are indulging in pushing AI towards a strategic and regulation innovative ecosystem. Let’s have a look at how artificial intelligence applications in government sectors are catalyzing disruption. The revenue collection department deals with vast amounts of data and information about the taxpayers. This data helps in identifying risks, estimate taxes, monitor tax collections, tax returns verification and more. Nowadays, tax authorities are leveraging artificial intelligence algorithms to predict those unlikely to pay their taxes, how people will react to tax interventions and new regulations. These help authorities to take action that can boost revenue collection and lower tax avoidance. E.g. Swedish Tax Agency uses a chatbot named Skatti to handle around 15,000 citizen queries about tax returns a month, making services more accessible, personal and efficient. Artificial intelligence applications for documentation are not limited to the revenue collection department. It can also be used in other sectors where government authorities and employees are frustrated with documenting and recording information processes. According to Deloitte , every year US$16 billion gets wasted in this and an additional US$15 billion in the procuring and processing of information. So, artificial intelligence can help to improve bureaucratic activities. Since the electric pace of developments in artificial intelligence makes predicting and forecasting easier, it is also being employed for other vital purposes too. For instance, a financial crime like money laundering alone costs US$1.4 trillion and US$3.5 trillion globally a year. AI software can help government agencies and financial institutions to detect money laundering and prevent corruption. The University of Valladolid in Spain has created a neural network model that calculates the probability of corruption taking place in Spanish provinces, as well as identifies the conditions that encourage corruption. Artificial intelligence has also become an indispensable asset in tracking and tracing cases during the COVID-19 pandemic . Countries like China were relying on AI-powered drones and robots to detect population movement and social gatherings and to identify individuals with a fever or who aren’t wearing masks – via computer vision. It also used agricultural drones as disinfectant sprays to sterilize the areas. Nabta Health in the United Arab Emirates used artificial intelligence to provide risk and symptom diagnosis to people. Even the Norwegian Labor and Welfare Administration (NAV)’s virtual chatbot Frida helped NAV to cope with a 250% increase in inquiries and unprecedented demand for its services. Generally, chatbots help citizens get quick answers to important questions improves service while reducing costs and backlog. Even the US Army website is using a chatbot that answers questions, checks qualifications and refers prospective recruits to human recruiters. With an accuracy rate of over 94%, the chatbot uses machine learning to improve recognition and helpful responses – doing the work of 55 human recruiters at once. State and national governments also need to be prepared to spring into action as a response to any kind of possible natural disaster. AI can be very resourceful here too. E.g., artificial intelligence researchers from the Georgia Institute of Technology, Emory University, and the University of California, Irvine collaborated with the Atlanta Fire Rescue Department (AFRD) to co-develop up a predictive analytics software (Firebird) that prioritizes buildings for inspection that have a higher likelihood of fire incidents. A recent research report from Microsoft reveals that apart from finding patterns to identify hazardous events, artificial intelligence tools also enable an analysis of how those patterns and trends, like the length or intensity of droughts or floods, could change in the future.
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In June 2023, India’s national think-tank, the NITI Aayog, released a discussion paper on the transformative potential of Artificial Intelligence (AI) in India. This paper said the country could add US$1 trillion to its economy through integrating AI. Since then, some of the biggest moves made by the government to act on this prediction is the formation of a task force on Artificial Intelligence for India’s Economic Transformation by the Commerce and Industry Department of the Government of India in 2023, and the Union Cabinet in December 2023. These bodies approved an INR3,660 crore national mission on cyber-physical system technologies that involves extensive use of AI, machine learning, deep learning, big data analytics, quantum computing, quantum communication, quantum encryption, data science and predictive analytics. But, what has been the progress in the nation since these ambitious missions were undertaken by the government? According to an analysis by research agency Itihaasa, the progress has been appreciable. When the agency used the number of ‘citable documents’, or the number of research publications in peer-reviewed journals, in the field of AI between 2013 and 2023 as a metric, India ranked third in terms of high quality research publications in Artificial Intelligence. However, when parsed by another metric (citations, or the number of times an article is referred), India ranked only fifth behind the UK, Canada, the US and China which suggests that India must shift its focus to improving the quality of its research output in AI. The report also revealed that the Indian Institutes of Technology and the Indian Institutes of Information Technology were among the primary research centres for AI. Currently, most of the traction in India is in the form of AI pilot projects from the government in agriculture and healthcare, and the emergence of AI startups in cities like Bangalore and Hyderabad. Though these are indications of grassroots level AI adoption, the pace of innovation isn’t comparable to the USA or China today. Some challenges that the progress of AI in India faces are limited availability of manpower and of good quality and clean data, as there is no institutional mechanism to maintain high quality data. A report published by PwC in 2023 revealed another imminent challenge-that even with all the potential benefits of AI, which are envisaged to aid humans, people still have concerns regarding data privacy and are apprehensive to share data for a better experience. A vast majority of participants agree that they have major concerns regarding data privacy to the point that it is near unanimous (93%) and that they are hesitant to even share medical results knowing that it could help provide some personalised knowledge about their health, so data protection still remains a hazy domain hindering the growth of AI.
In June 2023, India’s national think-tank, the NITI Aayog, released a discussion paper on the transformative potential of Artificial Intelligence (AI) in India. This paper said the country could add US$1 trillion to its economy through integrating AI. Since then, some of the biggest moves made by the government to act on this prediction is the formation of a task force on Artificial Intelligence for India’s Economic Transformation by the Commerce and Industry Department of the Government of India in 2023, and the Union Cabinet in December 2023. These bodies approved an INR3,660 crore national mission on cyber-physical system technologies that involves extensive use of AI, machine learning, deep learning, big data analytics, quantum computing, quantum communication, quantum encryption, data science and predictive analytics. But, what has been the progress in the nation since these ambitious missions were undertaken by the government? According to an analysis by research agency Itihaasa, the progress has been appreciable. When the agency used the number of ‘citable documents’, or the number of research publications in peer-reviewed journals, in the field of AI between 2013 and 2023 as a metric, India ranked third in terms of high quality research publications in Artificial Intelligence. However, when parsed by another metric (citations, or the number of times an article is referred), India ranked only fifth behind the UK, Canada, the US and China which suggests that India must shift its focus to improving the quality of its research output in AI. The report also revealed that the Indian Institutes of Technology and the Indian Institutes of Information Technology were among the primary research centres for AI. Currently, most of the traction in India is in the form of AI pilot projects from the government in agriculture and healthcare, and the emergence of AI startups in cities like Bangalore and Hyderabad. Though these are indications of grassroots level AI adoption, the pace of innovation isn’t comparable to the USA or China today. Some challenges that the progress of AI in India faces are limited availability of manpower and of good quality and clean data, as there is no institutional mechanism to maintain high quality data. A report published by PwC in 2023 revealed another imminent challenge-that even with all the potential benefits of AI, which are envisaged to aid humans, people still have concerns regarding data privacy and are apprehensive to share data for a better experience. A vast majority of participants agree that they have major concerns regarding data privacy to the point that it is near unanimous (93%) and that they are hesitant to even share medical results knowing that it could help provide some personalised knowledge about their health, so data protection still remains a hazy domain hindering the growth of AI. Another cultural challenge that India faces is the fact that the cost of failure is much higher here than in the West. While failing in an attempt at big innovation and grand goals might be seen as brave in Silicon Valley, failure often implies a loss of face in India and this has historically meant a lack of room for experimentation. All these challenges tell us that even with government funding and industry participation, India is just at the starting point of what seems to be a promising long road.
The world runs on data, and humans alone could never monitor or safeguard all of it.
When applied thoughtfully, artificial intelligence (AI)-enhanced cybersecurity can add essential layers of protection for modern enterprise networks.
Research firm Technavio expects the AI-based cybersecurity market to grow by $19 billion from 2023 to 2025. The company cites the increased complexity of enterprise networking environments, which often include a mix of legacy, on-premises infrastructure, and cloud resources, all of which need to be accessed remotely. AI approaches add efficiency and accuracy and reduce the impact of the ongoing worker shortage in this field.
As organizations have become more comfortable with autonomous applications that help streamline workflows and reduce human error, it’s only natural that we would see more AI in cybersecurity adoption as well. These five trends in cybersecurity underscore the overall shift toward AI business applications across many fields:
As workers around the world were sent home from their offices to work remotely in 2023 during the COVID-19 pandemic, cybercriminals were already lying in wait, ready to exploit vulnerabilities widened by the mass influx of unsecure network connections. Those same tactics have played out across the SecOps field, which has been dealing with a significant skilled worker shortage for several years.
(ISC)2 estimates the cybersecurity market is in need of about 3 million qualified workers, according to its 2024 Cybersecurity Workforce report. Additionally, the report shows 64% of the cybersecurity professionals surveyed said their organization is impacted by the cybersecurity skills shortage.
When SecOps teams are lacking in staffing, vulnerabilities naturally increase. No human could keep up with every viable threat, as cybercriminals know.
AI is playing a role in these situations. Sophisticated AI-driven algorithms can recognize patterns of attacks, suspicious email activity, and identify the most vulnerable network endpoints. AI can also tackle repetitive, error-prone tasks, like data labeling, and generate automated reports for human analyst review. All of these features will help to reduce SecOps teams’ bandwidth, so team members can focus on other security functions.
Identity and access management (IAM) is becoming more important than ever with the increased adoption of zero-trust security frameworks, which require every network user to be authenticated, authorized, and continuously validated.
AI can greatly reduce the amount of manual labor required to carry out these goals by introducing smart automation into security systems. AI can monitor and analyze user activities, including typing and mouse movements. It can also power supervised algorithms and unsupervised learning, both of which help SecOps teams identify anomalous behavior.
AI can improve security across the customer authentication experience as well, from the point of account creation and login to interacting with service accounts. AI monitoring of these activities helps organizations to assign risk scores related to potentially suspicious events, instead of simply locking users out or terminating their connections mid-session. This more nuanced approach improves efficiency and helps analysts zero in on genuine threats.
See more: Artificial Intelligence (AI) In Cybersecurity 2023
Blockchain adoption has been increasing dramatically, as cryptocurrencies have become more widely understood. Grandview Research values the global blockchain technology market size at around $3.67 billion in 2023 and expects that figure to skyrocket, growing at a compound annual growth rate (CAGR) of 82.4% from 2023 to 2028.
Bitcoin and other crypto coins are built on blockchain solutions that keep transactions secure and decentralized. Blockchain is also used within the medical field to better secure and monitor access to electronic records.
Advances in AI-powered blockchain have reduced the need for time-consuming secure sockets layer (SSL) and transport layer security (TLS) “handshake” methods that involve verification keys. Instead, newer systems can analyze data chains in bulk using high-powered AI, which is a much faster and far more secure process overall.
AI can apply regulatory rules and requirements to data across complex networks, which is a quicker, more foolproof compliance method versus manual search processes.
AI-based data processing will be critical as over 300 million new regulations are expected over the next decade, according to LogicGate.
Enterprises can use AI to track regulatory agencies worldwide to help monitor and maintain ongoing compliance, as rules change and new rules are adopted, LogicGate says.
Regulatory compliance enhanced with AI is a smart investment, considering the financial ramifications of being found out of compliance with broad data laws, like GDPR and HIPAA.
As more organizations move portions of their data to the cloud, cybersecurity has become more complex. Many legacy systems are incapable of monitoring cloud data, but newer AI-enhanced cybersecurity is specifically designed for the cloud.
Hybrid cybersecurity solutions involving AI that are able to monitor and analyze data across multiple environments will become a must. Many organizations have been getting by with an ad hoc approach, where enterprise data is pulled from various architectures, compiled, and then analyzed by a software platform. Not only are these approaches complicated and expensive, they are also prone to missing important data.
See more: Top Performing Artificial Intelligence Companies of 2023
Artificial intelligence has come a long way from the fictional AI character JARVIS to real-life ChatGPT. However, human intelligence is an attribute that supports individuals in learning, comprehending, and coming up with innovative solutions to challenges, versus artificial intelligence, which imitates humans based on provided data. Since AI has become so prevalent today, a new discussion, artificial intelligence vs. human intelligence, has emerged comparing the two rival paradigms.What is Artificial Intelligence?
A subfield of data science called artificial intelligence is associated with creating intelligent computers that can carry out various tasks that often call for human intelligence and perception. These sophisticated machines can learn from previous errors and historical data, analyze the surrounding circumstances, and decide on the necessary measures.
AI is an integrated field that draws on ideas and methods from many other disciplines, including computational science, cognitive sciences, language studies, neuroscience, psychology, and mathematics.
The machine is capable of self-learning, self-analysis, and self-improvement, and while processing, it requires minimal or almost no human effort.
It is utilized in practically every business, including the media industry, the healthcare industry, graphics and animation, and more, to help technologies replicate human action based on their behavior.What is Human Intelligence?
Human intelligence refers to a person’s intellectual capacity, which enables them to reason, understand a variety of expressions, comprehend challenging concepts, solve mathematical problems, adapt to changing circumstances, use knowledge to control their environment and communicate with others.
Human intelligence and behavior have their roots in a person’s distinctive admixture of genetics, childhood development, and experience with diverse events and surroundings. Furthermore, it entirely relies on the individual’s ability to use their newly acquired knowledge to transform their surroundings.Artificial Intelligence vs. Human Intelligence
Here is an elaborated difference between human intelligence and artificial intelligence:
ParameterHuman IntelligenceArtificial Intelligence Origin Humans are born with the capacity to reason, think, assess, and perform other cognitive chúng tôi is an innovation created by human insights; Norbert Wiener is associated with helping to progress the field by theorizing about the mechanisms of criticism.Learning CapabilitiesHuman intelligence can pick up new information via observations, experience, and educating oneself and put it into novel scenarios.Using statistical models and algorithms, AI can learn from enormous amounts of data. They cannot build a uniquely human analytical style; they can only learn through data and regular training.Creativity Using innovative thinking and creativity, human intelligence can generate fresh concepts, literature, music, and chúng tôi can create novel approaches using existing trends and data but lacks inherent innovation and originality.Decision MakingHuman decisions can be subject to subjective factors not based solely on chúng tôi interprets according to completely collected data, which makes it strongly objective in decision-making.Nature Human intelligence is analogous. Artificial intelligence uses digital machines.Energy Use The human brain uses around 25 watts of energy.Modern-day computers use around 2 watts of energy.Social Skills The capacity to comprehend abstract concepts, the degree of self-awareness, and sensitivity to the sentiments of others distinguish humans from other social animals. Artificial intelligence is still developing the capability to read and recognize relevant interpersonal and passionate signals.What AI Cannot Do without – The “Human” Factor
People show their emotions and have the ability to interpret the facial expressions and moods of others, but artificially intelligent machines are not trained to do that. Although AI-enabled machines can mimic human speech, they lack human touch since they cannot express empathy and other emotions.
AI is based on codes that restrict it from finding creative answers to new challenges. They operate as intended, which limits their capacity to understand the context and devise sophisticated solutions.
While AI can learn extremely fast, it lacks logical thinking and is, therefore, unable to reason and challenge the facts to the same extent humans can.In-demand Machine Learning Skills
There are certain machine learning skills that are currently in demand for the artificial intelligence industry:
Programming languages such as Python, C++, R, etc.
Natural language processing
Communication and data visualization skills
Statistics and probabilityArtificial Intelligence vs. Human Intelligence: What Future Holds?
Digital existence is enhancing human abilities while challenging long-standing human activity. Code-driven technologies have reached more than half of the world’s population regarding ambient data and connection, providing previously unthinkable potential and significant risks.
When the discussion of AI vs. humans comes into action, we look at a bright coexisting future. The next generation will be raised in an era where human beings and humanoids coexist, with humanoids functioning to assist humans.
Also Read: This is How Experts Predict the Future of AIBest Machine Learning and AI Courses Online
Here are the top 5 online courses that can help individuals intrigued about artificial intelligence progress in the field:Getting Started with Decision Trees
Checkout the course here!Machine Learning Certification for Beginners
This free certification course is the perfect start for the machine learning journey. The course comprises basics of ML, the introduction of Python to data science, using tools like NumPy, sci-kit- learn and more, real-life, hands-on projects, concepts of feature engineering and more. It is a short course that requires only 8-10 hours per week.
Checkout the course here!Loan Prediction Practice Problem Using Python
A short and interesting free course designed for people who want to learn how to implement machine learning and data science in their real-life monotonous problems. The course majorly focuses on the use of classification. It includes a practical problem that will be solved using classification and other approaches that can be implemented in machine learning.
Checkout the course here!Support Vector Machine (SVM) in Python and R
If an individual wants to learn about what is SVM? How to use SVM in machine learning? Applications of SVM and more, this free course will answer many other questions. The course design includes the basics of SVM and an understanding of how to implement SVM in Python and R.
Checkout the course here!Evaluation Metrics for Machine Learning Models
Evaluation metrics form the core of various ML models. This course will perfectly guide you on how to use evaluation metrics in machine learning, the ways to enhance your models and several other concepts that would help you build interesting models. The course also elaborates on the types of evaluation metrics and evaluation using classification and other methods.
Checkout the course here!Conclusion Frequently Asked Questions
In this webinar, we discussed:
1) Where is the market in terms of real adoption of ML and AI?
2) The data issues executives need to solve – or at least understand – before considering ML. Advice for getting started?
3) Challenges: What are the unstated assumptions that cause projects to get tripped up?
4) How does miscommunication contribute to AI project stumbles?
5) Ethics and AI: How should executives be thinking about this?
Ya Xue, VP of Data Science, Infinia ML
James Kotecki, VP of Marketing and Communications, Infinia ML
Xue: Oh, I think actually companies are using it. There are some well known example like Tesla self-driving and Google, Google search, Apple’s Siri and all the other application, famous applications. Even just talk about those less famous examples like our company, Infinia, we’ve been in business for almost three years. We’ve done over, I would say, over 30 different projects, different application area and different companies. So we’ve seen a lot of actually companies are using it, using AI as a powerful tool to reduce their cost, to improve business efficiency, and then creating real business value.
Kotecki: But there’s also some nuance to this, in that what does it mean to do AI? And how do people define what it means to actually do it? There’s some truth to that 4% metric as well, whatever… It’s your different numbers but it’s always this very low, this paltry number of people that are actually doing AI and getting real business value from it. I think there’s probably a lot more people who are dabbling, who say they’re doing AI, who throw the label of AI on it, because as a marketer, I can say this from a marketing perspective, it’s better to say that you’re doing something sexy in AI than may be something else. And it’s just a term of the moment.
Kotecki: And also people that are doing a lot of things in AI and haven’t figured out how to operationalize it, how to productionize it, how to deploy it in a widespread way that’s actually getting day-to-day value. It’s not just creating a cool algorithm, which we can certainly do. It’s getting all the way to people in your organization actually getting use from it, which involves a lot of things involving AI, and then a lot of steps after that that involve a lot of change management that’s not as easy as writing an algorithm. So it’s a long process that we’re in the middle of now, a long transformation.
Xue: Oh, yes. There are many gaps, actually. So first one is the illusion about AI. So some people have the misunderstanding, like the AI is just a miracle. If you download some off-the-shelf software and you threw the data in, you can get the results you want. So the part that people don’t get is AI is pretty much like any other kind of research and development. It takes time and effort to happen. So that’s one thing, it’s set the right expectation, that’s very important. And another thing executives don’t understand is the consequence of AI success. Yes, it’s a success, and you can make a prediction at 99% accuracy. However, when you deploy it, it’s gonna change your work flow under your business process. Are you ready for it? That’s a bigger question. It has happened multiple times. We develop something, finally you could actually look at, when it’s time to put it into production, oops, we may not be able to do it.
Kotecki: I think there’s a related factor here, which is, I’ve gotten a sense from several past clients, and I think there’s just a sense in the zeitgeist out there that executives would not say this publicly, but a lot of the reason that they might wanna use AI or ML is to replace people, right? Reduce headcount. And they go into projects thinking that that’s what they’re gonna do. Oftentimes, we have seen, just in our narrow corner of the universe, the executives who think that are often then shown that they still need those people to do what Ya is talking about, to do the kind of reviews or they need to reassign those people to higher level projects.
Xue: I think the first executive have to recognize is there’s a potential risk because it happens. Bias introduced either in design or in the data, mostly from the data, I would say. Data like machine learning algorithm models need to be trained with large amount of data. Then if the data is under-representing or over-representing a certain group, say, it could be a gender group, it could be a racial group, then the machine learning algorithm will learn that, and they embed that information into the model then will produce biased results. So it’s a well-known problem and the machine learning community is working very hard trying to address this issue.
Kotecki: It’s certainly something to be concerned about. Even if you were completely unethical as an executive yourself, you should be concerned about the headline risks. And any time that you see in the headline of a major mainstream publication, the term bias and the term AI, it’s going to be problematic for whatever company is being highlighted in that article. Even though I think it’s also important to remember that when you hear the term bias, it actually doesn’t necessarily have a negative connotation in and of itself in a data science context. Because Ya was hinting at this, you want to maybe bias an algorithm in favor of candidates that are smart, for example, if you’re looking at a job screening application. So it’s not that we necessarily wanna not have any bias, but we wanna understand it and we wanna get rid of bias that is untoward.
Here is the list of the Top 10 Artificial Intelligence Apps in the market
When it comes to the mobile app industry, businesses of all sizes and specialisations confront strong competition. This position compels them to keep up with all developing digital developments in order to maintain their worth. Recognizing the huge influence of artificial intelligence on business, top firms such as Amazon, eBay, and Tinder make extensive use of AI in their applications to generate tailored mobile user experiences and improve profitability. Start-ups also raise more investment for AI integrations, propelling them to high marketability and competitiveness. Annually, more AI apps go viral, bringing greater exposure and revenues to their owners. Some of these applications have even proven to be vital. Let’s take a look at the top 10 AI apps in the market now.1. Google Assistant
Google Assistant, Google’s AI-powered virtual assistant, is primarily available on smartphones and other electronic home platforms. It is built on a set of technologies designed to handle languages such as English, Español, German, French, Italian, Japanese, Portuguese, Chinese, and many more. Users may interact with Google Assistant using natural speech and keyboard input. This AI assistant will perform a variety of tasks that people would expect it to perform, such as playing music, searching for a favourite restaurant, phoning individuals, and even assisting them in obtaining vital information from Google.2. Alexa
Alexa is Amazon’s artificial intelligence-powered robotic assistant. This AI programme works in tandem with Amazon Echo. Typically, Amazon Alexa improves voice commands, natural language processing (NLP), and other technologies to give a broad range of capabilities to its consumers, such as voice interaction, music streaming, query resolution, and so on. It can also create to-do lists, set up alarms, download podcasts, play audiobooks, and offer real-time information on weather, transportation, news, sports, and other topics. Alexa converts speech to text and utilises Wolfram language to deliver the best responses to all of the clients’ questions.3. Youper
Youper is an AI-powered mental health assistant app accessible on both Android and iOS. When individuals can connect with them quickly, this software can help them take care of their mental health. Users can be guided via customised meditations by Youper. With the help of Youper, users may better understand themselves and track their moods. Youper uses AI to customise a variety of approaches. The programme has received rave ratings in app stores and is highly regarded by specialists.4. Siri
Siri is based on speech inquiries and a natural language user experience. This AI-powered voice assistant will make phone calls, send a text messages, answer queries, and make recommendations. Siri learns from its users’ language usage, queries, and expectations over time.5. Fyle
Fyle is an AI-powered expense tracking programme that is available for PC, Android, and iOS. Fyle is a significant player in smart expenditure accounting, and it has announced direct connectivity with Google G Suite and Microsoft Office 365. Fyle is used by companies like Royal Enfield and Communicorp because it offers useful features like real-time data extraction, trying to report operating expenses, business card tracking, real-time decision evaluations, compliance business processes, travel improvements, travel requests, research, and assimilation with important transport management, HRMS, accounting, and ERP applications.6. DataBot
DataBot is a virtual assistant powered by AI that is accessible on Windows 10, Android, and iOS. It is also released on the Xbox One, Android tablets, iPad, iPod and Windows Mobile devices. This app uses voice to answer your questions and discuss the issues that are important to you. DataBot offers digital platforms that deliver pictures, information, and interactive presentations centred on the topic of your choice. Obtaining information from Google searches, Wikipedia, RSS networks, and other sources. You may customise DataBot based on your accent, voice, and other preferences. DataBot understands and speaks English, French, German, Italian, Spanish and Portuguese.7. ELSA Speak
ELSA Speak is a popular AI-assisted English language learning software. This programme allows users to practise pronouncing English and conversing in the language in short bursts. They can hope to make rapid progress since AI gives them timely evaluations. The buyer must order ELSA Pro within the trial period, which lasts seven days. ELSA Speak is accessible for Android and iOS, however, at the time of publication, there were no PC or Mac versions available.8. Cortana
Cortana, Microsoft’s virtual assistant, is AI technology that hardly requires an introduction. Invoke, Microsoft Band, Windows Mixed Reality (MR), Amazon Alexa, Windows 10, Windows Smartphone, Android, iOS and Xbox One all support this AI-powered virtual assistant. Cortana gives hands-free assistance, answers questions remember, takes notes, manages assignments, and assists with scheduling.9. Socratic
Google recently announced that it has bought Socratic, an AI-enabled software that helps kids with arithmetic and other projects. Students will use their mobile phones to snap photographs, and Socratic will utilise its AI tools to provide visual examples for the concepts they need to learn. Socratic is a text and voice processing programme that can assist students in learning physics, math, art, and social sciences, among other subjects. This application is compatible with the iPad and is available for Android and iOS. Users just need to snap a screenshot of their assignment to obtain help from this software, which will then present them with relevant concepts that they may utilize to solve issues quickly.10. Replika
Replika, widely regarded as the best AI buddy, converses with her users as if she were a person. It was initially created for Apple users, but it is now available for Android users as well. The software may learn user preferences and behaviours, starting with general replies and progressing to more detailed and personalised ones. The AI apps we just looked at will undoubtedly demonstrate how beneficial AI can be to your business, but developing an AI app may be difficult! Because AI is a specialised skill, you might want to enlist the help of a respected mobile app development firm.Conclusion
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