Huge technological advancements in the tech space over the decade created lakhs of jobs in India. As per the recent reports from Accenture, it is estimated that the growth of AI can contribute US $957 billion to India’s economy in the next 15 years. 

AI has the potential to bring massive incremental value to various businesses, for example, manufacturing, Agriculture, energy, healthcare, and education. It does play an imperative role in generating jobs in all these businesses. 

However, Indian IT companies have been following the maneuver that is an essential factor to be successful in technology adoption and advancement. The right skilling by Indian tech start-ups has a massive contribution to creating 2 lakh job opportunities in the tech space in 2022. 

Indian tech start-ups experienced a 108% growth in 2018. However, the studies also disclosed that Artificial intelligence witnessed the fastest adoption compared to other distinct technologies. 

It is anticipated that with the growing youth population in India, most businesses will be switching to this groundbreaking technology AI. Many hi-tech cities like Bangalore, Hyderabad, Delhi, and Mumbai are banking on Artificial intelligence because of its potential to reach a larger audience base. 

A career in AI in 2023 & beyond

India has been lagging in terms of healthcare facilities, and less number of doctors are available. It’s an arduous task for the government to assure quality healthcare to its citizens.

However, AI is forecasted to aid India to a greater extent in facilitating better healthcare shortly.

Reputed tech giants like Microsoft are collaborating with startups to facilitate AI-based tools such as the 3Nethra that can detect eye issues. This is just an example, there have been so many projects under the pipeline for the healthcare sector. Hence, AI has the potential to transform the healthcare sector. 

Artificial Intelligence to transform the Education sector

India is becoming the hub of Science, Math, Technology, and Engineering graduates. Tech giants such as Google, Microsoft, and Intel have been providing short-term education plans like internships, classroom training, and other initiatives to assist learners in AI programming. 

Artificial Intelligence intends to enhance the Financial system

The world of finance and banking has transformed a lot with the significant adoption of Artificial Intelligence. 

The future of finance completely relies on AI. This technology enables individuals to not only manage the flow of money, but they can also ameliorate the cash flow, and profitability, optimize compliance, and more. 

Artificial Infrastructure to bring advancement in Infrastructure and Mobility

The ever-increasing population of hi-tech cities leading to a great challenge for India. AI is the ray of hope that could offer Smart infrastructure and mobility with civic tool systems. Further, the tools could help in assisting customer grievances, and appropriate repair, therefore enhancing the quality of living of migrators.

Artificial Intelligence to counter cyber threats

As technological advancements are increasing day by day, cyber threats have become more usual. 

AI is already competing with ever-emerging malware. The significant advancement in the IT field makes AI inevitable to handle cyber threats. India is already working on Artificial Intelligence based cybersecurity tools at a rapid pace. Hence, this enables massive opportunities for aspirants who want to build their careers in AI. 

Collectively, skilled AI engineers are in high demand, specifically, in India. The groundbreaking technology seems to be imperative when it comes to providing jobs for the future generation of India. 

NearLearn is one of the popular Artificial Intelligence and Machine Learning training institute in Bangalore with 4.8 Google ratings. The institute has the best faculty, so if you’re the one who is looking out to pursue a career in AI, then NearLearn is the top choice. 

The post Why Artificial Intelligence is the best career in India in 2023  appeared first on .

Thousands of data breaches have been observed throughout the year including the Crypto.com Data breach, the Texas Department of Insurance Data Leak, and the Apple & Meta Data breach. 

Hence our futurity aims for a competent manoeuvre of protecting data online from potential cyber threats.

Few technological advancements helping tackle these data breaches, so perhaps there’s a ray of hope ultimately. Artificial Intelligence & Machine learning has become a boon to enhance cybersecurity.

Moreover, Artificial Intelligence & Machine learning both can supplement the safety measures of distinct applications that become easy targets. Let us understand how AI & ML together hold a massive contribution to helping businesses enhance their data security measures.

Artificial Intelligence & Machine Learning Augment Data Safety Measures 

As more industries, organisations, and businesses transform to digital, cyber threats have been mushrooming. Artificial Intelligence & Machine Learning proved to be effective tools for tackling such cyber threats. These groundbreaking information technologies analyse billions of pieces of data in real-time and take security measures. 

AI & ML are best positioned to combat the rising cybersecurity challenges. Especially, AI can analyse and counter variance from the norm. As per the reports by Capgemini Research Institute, 61% of businesses, which depend on digital media will fail to recognise threats without the help of Artificial Intelligence. However, 69% of businesses acknowledge that AI is inevitable to counter cyber threats. Moreover, it is believed that the market for this technology is estimated to reach $46.3 billion by 2027. 

How AI & ML Help Businesses To Counter Data Breaches?

Identifying Deviations:

Artificial Intelligence & Machine Learning utilize behavioural records that enable profiles for people, networks, and assets to detect deviations that may be indicative of a potential cyber attack.

Foreseeing probable cyber threats: 

These disruptive technologies make it feasible to exercise huge chunks of data of various types to forecast probable data breaches before they take place.

Countering cyber attacks in real-time: 

Artificial Intelligence & Machine Learning methodologies can alarm when a data breach is detected or counter automatically with no human interruption.

Advantages of Artificial Intelligence & Machine Learning 

Businesses that adopt AI & ML into their data security strategies are gaining massive advantages. 

Quick Detection of Data threat & Counter Attack

AI & ML can easily detect millions of pieces of data. In addition, not only respond to threats but also autonomously improvise response times. Cyber threats can infiltrate any organization’s digital space & cause harm. However, this disruptive technology’s quick detection & response time is the key.

Reduced IT costs: 

AI & ML together lower the effort and time needed to predict and counter data breaches, making them reliable tools depend on. As per the Capgemini reports, it lowers by 12% of IT costs. However, there are also some examples where businesses lowered IT costs by 15%. 

Improving cyber analyst productivity: 

With these groundbreaking technologies, cyber analysts can work with reduced pressure saving time to manually shift to data logs. AI & ML can alarm cyber analysts regarding potential cyber threats highlighting the type of attack. 

Collectively, with increasing cyber threats, the need for more efficient technology has been on the rise. However, Artificial Intelligence & Machine Learning have become saviours in countering cyber threats by being more effective. It may become inevitable for future Cyber analysts to acquire AI & ML-relevant skills. 

The platform for such aspirants, who wants to be a part of this technological revolution shortly, NearLearn is the best institute. With effective classroom training, you get the opportunity to experience live projects. If you’re looking out to own these skillsets then NearLearn at Bangalore is happy to assist you.

The post Artificial Intelligence & Machine Learning: The Future Superstars of Cybersecurity!  appeared first on .

Machine Learning AI is hailed as an unbeaten mastermind in various fields, hopefully,, futuristic upgrades may completely change the aura of the the gaming sector. Hence, there is no doubt that technology like ML and AI will be the inevitable future of gaming. It will be a great deal once these emerging technologies can be incorporated and refined into a game. 

Read: What is AI? Here’s everything you need to know about artificial intelligence

Machine Learning AI Game On!

Appealing Visuals

Essentially, Machine Learning AI could enhance the gaming experience by advancing visual quality. With ever-growing amounts of data at our disposal, users can experience unique game environments and characters that make the play more realistic and natural.

The technology has the potential to incorporate more advanced and modern forms of AI into our game processes. These groundbreaking technologies help achieve more human emotion thereby gaining larger traction of the user base.

Real-world Ramfications

Emerging AI with voice assistants is being concatenated into our smartphones and smartphones. For instance, Alexa, Google Assistant, Siri, and Cortana are already diversifying the way we play games.

The voice assistant apps have already become industry standard enabling great change in the user interface and user experience. As there is very much work in progress, well-integrated AI assistants have the potential to leave their speakers and once these technologies reach their peak, mainstream gaming could end up with digital gaming and storytelling experiences.

Customized Play

The idea of customized service via Machine Learning AI could also disrupt our gaming experience, certainly bringing them more directly into our lives. Advanced AI can access data about individual players in many ways.

Sophisticated Design Tools

AI does just not change the game content but enhances the overall game design. With enough development under progress, we could see Machine Learning AI and data collection work hand in hand, to support designers to create the best possible systems.

Necessarily, Machine Learning approaches have a wide range of connotation in the majority of all sectors and the way the technology intersect across gaming has potentially some of the broadest implications.

Several problems are becoming increasingly complex to fund, manage, and make, as they blow up in graphical fidelity and exponential complexity.

Machine Learning with AI helps build the models of their own rules within parameteres. Whereas, Video games are non-players that are created by a programmer.

Machine Learning AI model has the potential to create practically more quest dialogues, making from an update about the world of warcraft’s on websites and wiki entry searches to create scarily realistic and practical objectives that comprise real place names and enemy types.

Machine Learning AI codes or programs will take a major role play in building the animation and construction of creatures, characters, and level assets at a reliable cost. However, these tools will not replace the manpower but complicates the work. If AI is incorporated with Machine learning then AI can provide an animation that could purely save developers time and ease the tasks that will have a significant impact on the player’s experience.

Read: Artificial Intelligence and Machine Learning Will Be the Most Important Technologies in 2022

The post How Machine Learning AI Is Going to Revolutionise The Gaming Sector Forever. appeared first on .

In order to thrive in excellence, businesses around the globe have had to diversify the way they work. The diversification has fostered retraining of their massive workforce to facilitate them with in-demand skills. 

Evansadata, in its recent data, anticipated that 5 million developers will be needed to handle the challenging projects over the next five years. Further, as per the survey of Coursera on industry-level job skills, the majority of the industries are in desperate need of technological skills. 

So we are delighted to explain to you the top 10 skills to learn in 2022 and also how to monetize an existing skill. 

Learn and Monetize These Top 10 Skills in 2022! 

1. Artificial Intelligence and Machine Learning: 

In the age of highly advanced technological breakthroughs, some innovations such as Artificial Intelligence and Machine Learning are promising future. With groundbreaking revolutions like driverless cars, facial recognition software, and surgical robots are evolving, with a broad range of applications for the Fintech, Healthcare, and Education sectors. 

As per the data of Semrush, between 2018 and 2025, the Asia-Pacific region will witness a huge compound annual growth rate. In addition, the predicted AI annual growth rate would be 33.2% between 2020 and 2027.

Artificial Intelligence facilitates a distinct range of career paths.

• Machine Learning Developer. 
• Data Scientist.
• Software Engineer. 
• Data Engineer.  

2.. Full Stack Development: 

The need for highly skilled developers is augmenting at a brisk pace compared to other areas of IT. The respective skills are in massive demand across a wide range of industries, giving rise to an extensive range of opportunities. Being a professional full-stack developer you are allowed to create highly functional and attractive websites with basic knowledge of coding. 

Full-stack development is simple with low-code platforms like WordPress that ease the job to build a professional website in a very short time. The average salary for a full stack developer in India ranges is 6.7 lakh rupees. 

3. Data Science: 

Data Science is evolving as one of the must-have skills for an IT professional. A diverse range of businesses is already using it. Currently, it is one of the indispensable skills in the job market. Moreover, popular social media platforms like Instagram and others rely on data for their functionality. The average annual salary of a data scientist in India is 6.98 lakh rupees.  

4. Cloud Computing Enginneer: 

As per the report of Global Knowledge mastering cloud computing skills is predicted to be a highly in-demand skill in 2022. The job role includes creating a functional cloud platform to maintain it. These skills can lead to job positions like auditor, cloud developer, and architect.

5. UX/UI Design: 

The entire world is undergoing a huge digital transformation, the user experience has become increasingly important to captivate the traction of wide range of audience. User interface is a design that evaluates the customer’s views towards a specific product aiming on efficiency.  

6. Blockchain Development:

The Blockchain technology has been one of the recent sensation in the tech savvy world. Despite being one of the nascent technology it has created a multi billion-dollar industry. As per the recent report by Statista, overall global expenditure in developing blockchain solutions would reach by $19 billion by 2024. 

The technology thrives to create a career  paths so that skillful person could work as: 

• Blockchain Developer. 
• Blockchain Solution Architect.
• Smart Contracts Engineer.
• Blockchain Quality Engineer.  

7. Cybersecurity: 

Digitalisation is happening at a breakneck speed. It is also equally important to secure all the platforms from potential hacks. Hence, choosing career in cybersecurity could also be a promising choice for good career growth. Currently it is one of the top trending jobs, mastering these skills will help you to build your career as a cybersecurity engineer.

8. Big Data. 

Big data signifies the harvesting and executing a huge chunk of data to exploit authentic information that enhances decision-making. Most precise decision making could save money and time, and this explains why mastering these relevant skills  would get you a job with good salary package. 

9. Augmented Reality (AR) and Virtual Reality (VR). 

 This is also one of the trending jobs with Facebook’s recent rebranding to Meta. AR and VR together referred to as Extended Reality (XR), the European market for the skill is anticipated to evolve 5x in the near future. Many businesses and industries already coming forward to adopt XR technology. Hence the demand for skillful professionals is high.

10. The Internet of Things (IOTs): 

The Internet of Things (IoT) has been the connecting brick that bridges digitally enabled gadgets to communicate, share data across the internet. Getting trained or mastering the relevant skills get you a job in diverse range of fields such as: 

• Data Analytics.
• Network Specialist.
• Artificial Engineer. 
• Cybersecurity Engineer.

The post The Top 10 High Paying Skills to Expertise in 2022! These Are The Skills Companies Are Looking Out For! appeared first on .

The stats prove that machine learning engineer is the highest paying job in terms of pay and the growth of postings. The number of job postings has also increased which are related to machine learning has increased.

Are you keen on learning automation, data, and algorithms, then machine learning could be the perfect job. You can stand out from the crowd if you can achieve a machine learning certification.

Reasons to seek a career in the field of machine learning course

Machine learning and artificial intelligence allow businesses to develop programs that can update their devices and adapt to various environments hence making things better.

If you are feeling that you have got stuck in your career or feeling bored with your job, then opting for a career in machine language can be a great way to start a career. A career in the top leading technology can be a great choice for you to start your career right away.

Since keeping abreast with the technical advancements offers a lot of exposure to help you resolve real-world problems. Machine learning courses can be utilized in various applications to resolve any issues.

A machine learning expert can work in real-time situations to help you resolve and analyze the best possible issues. Gaining a career beyond the mechanical routine jobs in the field of machine learning can be the most optimum choice.

There are multiple opportunities for experts in machine learning who can take them to the forefront, ahead of their competition. Let’s dig in some of the factors before deciding to opt for a career in machine learning:

Great scope in the future- Machine learning is the new skill of the future so that the chances of gaining employment increases with the requirement of this skill. Since there is a shortage of experts in the field of machine language, there are multiple vacant ML jobs. Henceforth it is a great opportunity to start your career in the field of machine learning. When you know, you can start your career in the great field of machine learning with maximum scope.

Real-world issues- The individuals who want to tackle real-world issues can take up a career in the field of machine learning. After acquiring the course in machine language, if you decide to work as an ML engineer, you can work on real problems having significant implications. You can learn to resolve issues and develop solutions that impact both individuals and businesses in a great way. A career in machine learning is a great option if you’re seeking an opportunity that gives you the most satisfaction.

Promising career growth-  Future is secured, since the professionals who want to pursue a career in machine learning, then they are free to make this choice with the median salary. It is an attractive career choice for anyone.

Keep learning and advancing your knowledge, hence an MLM professional needs to wait for a good amount of salary over time. With gradual development in machine language, a professional has the chance to see an exponential increase in their career.

Multiple opportunities ahead- The experts in machine language have many options in their careers. hence if they possess the right capabilities, then there are several options lying ahead. You can acquire the skills and become a practical resource for many career options. When you have finished a course in machine language, then you can be part of a journey that can be filled with learning, challenges, and possibilities.

There are many career paths you can select within the industry. you can get a high-paying job as a machine learning engineer, data scientist, business intelligence developer, NLP scientist, or human-centered machine learning designer.

Job opportunities

Since machine language is a vast and complex subject, hence it has got an advantage to select it as a career. It offers you a variety of various career opportunities in various fields. Are you thinking about, “how to start a career in machine learning”, then the following top career choices in the field of machine learning can help you:

1. Machine learning engineer– An machine learning engineer is it professional job that requires studying, developing, and creating automating predictive models. you can work on artificial intelligence in the job.

2. Human-centered systems designers- Human-centered systems designers can design intelligent software with the end-user to meet the requirements of the client requirements. Over time, they analyze the market trends and implement them accordingly.

3. Aiops engineer

It is crucial in a business to oversee the performance of the data. You can learn machine learning and ai to accomplish this job. The job role is to understand the root causes of the problem within the company and offer the solution to it.

4.Computational lingust

Computational linguistics involves focusing on comprehending written and spoken language and deals with scientific and technical subjects. The job role deals with designing, implementing, and coming up with innovative ideas about applications.

5. Data Scientist

Data Scientist is the best career choice which you have earned with proficiency in Machine language. It is one of the jobs which has the maximum demand, hence learning machine learning can help you secure a job in a Data Science career. A data scientist’s job role is to do data analysis ., processing, and modeling to produce the strategies for your business. You can entail a mix of computer science, statistics, and arithmetics in this field of Data Science.

Earn a certificate in Machine language and earn a decent amount of salary with it. Let’s understand more about the machine learning career salary:

Machine Learning Engineer – $150336

Data Scientist –  $119444

Computational Linguist – $109434

AIOps Engineer – $117,101

Human-centered AI Professional – $60,000

Read: Machine Learning Engineer vs-Data Scientist a Career Comparison

The Conclusion

A career in Machine learning helps you to be a part of the digital revolution. Since it has various technologies, tools, and concepts, machine learning helps to improve the skills required to work with those technologies which helps to earn a growth in career. professionals who are skilled in machine language can have a variety of possibilities. You can resolve real-world problems henceforth, the more you work, the more you can experience real-world experiences and earn machine learning salary in India. With the increase in machine learning, you can earn a chance to stick around in the future.

The post Is Machine Learning a Good Career? appeared first on .

Let’s dig into the top 10 technology trends in 2022:

Artificial Intelligence and Machine learning

Artificial Intelligence(AI) is now initial to see its implementation is various sectors of life. It is basically known for its superiority in image, speech recognition, ride sharing apps, smartphone personal assistants and many more.

AI is also utilized in analysing interactions to determine underlying connections and insights to help you predict the demands in various hospitals. It helps to enable authorities to make better decisions about the resource utilization and detect the patterns of customer behaviour by analysing data in real time and personal experiences.

Since AI is getting utilised in various sectors, hence new jobs are created in development, programming, support and testing. Stats prove that AI, machine learning and automation will create many jobs by 2025.

AI and machine learning will help you secure jobs:

1. AI research scientist
2. AI engineer
3. AI architect
4. Machine learning engineer.

Blockchain

Blockchain, one of the best technical courses after graduation can be described as the data you can only add to, not take aways from or change. The COVID-19 pandemic has accelerated the digital transformation in various areas especially in blockchain or distributed ledger technology.

Many businesses have started adopting blockchain technology for enhancing their business processes. Stats prove that worldwide spending on blockchain solutions is going to reach USD 11.7 billion by the year 2022. Banking is one of the areas where the high-level security, real-time processing and quicker cross-border transcations take place.

Blockchain helps you get secure jobs in the field of various fields and industries:

1. Risk analyst
2. Tech architect
3. Front end engineer
4. Crypto Community Manager

Internet of Things(IoT)

The list of technical courses after graduation cannot be complete without IoT, as it has always been a promising trend Now a days there are multiple things which can be built with WiFi connectivity. Hence the internet of things(IoT) has enabled various devices, home appliances to be connected to each other and exchange data over the internet.

IoT can be utilised in various applications like for instance you can switch off lights, fans and even lock the door remotely, while tracking the fitness on our Fitbits. The IoT enable better safety, efficiency and decision making for various businesses where the data can be easily collected and analysed.

Forecasts suggest that by 2030 around 50 billion of these IoT devices will be in utilization around the world. The global spending on the Internet of Things(IoT) is going to reach 1.1 trillion U.S dollars by the year 2022.

Cyber Security

Cyber security is an emerging technology and best technical courses in Indiaas the malevolent hackers are trying to access data illegally and continue to find ways to get through the toughest security measures. This latest technology is adapted to enhance security. Cyber security will remain a trending technology as it constantly evolves defend against hackers.

By 2025, around 60% of organizations utilize cybersecurity as a primary determinant in conducting third-party transactions and enhance business engagements.

You can get the roles:

1. Ethical Hacker
2. Malware Analyst
3. Security Engineer
4. Chief security officer

Quantum Computing

One of the amazing trends is involved in preventing the spread of the coronavirus and to develop potential vaccines is the quantum computing. It has the ability to easily query, monitor , analyse and act on data. Banking and finance is another field where you can manage credit risk for high-frequency trading and fraud detection.

Quantum computers acts much faster than regular computers and huge brands like Honeywell, Microsoft , AWS, Google . By the year 2029, the revenues for global quantum computing market can surpass $2.5 billion.

Virtual Reality and Augmented Reality

Virtual Reality and Augmented reality is one of the great technical training courseswhich have helped the user to immerse in an environment and enhance it also. Besides its utilization in gaming applications, it is used as a simulation software to train U.S. navy, army.

AR and VR has got enormous potential in various applications from training, entertainment, education, marketing and even rehabilitation.By 2022, it is estimated that the global AR and VR is expected to reach upto $209.2 billion.

Employers might look for skill set which requires a lot of specialized knowledge, basic programming skills can land a job.

Robotic Process Automation(RPA)

Robotic Process Automation is the utilization of software to automate business processes like transcation processing, interpreting applications, dealing with data and email reply. The automation of tasks can be easily automated sing RPA.

Stats prove that RPA automation can be harmful for existing jobs as 5 percent of occupations can be totally automated.

If you can learn RPA, then you can gain a number of career opportunities like

 1. RPA developer

2. RPA analyst

3. RPA architect

Edge Computing

Cloud computing has been found difficult to deal with when the quantity of data organizations increases. Edge computing helps to resolve problems to bypass the latency caused by cloud computing and getting data to a data centre for processing. Edge computing can be used to process time-sensitive data in remote locations with limited or no connectivity to a centralized location.

The stats prove that with the increase of Internet of Things(IoT) increases, the edge computing will also increase. By 2022, the global edge computing is expected to reach $6.72 billion.Following are some of the job positions which can be secured if you can master cloud computing and quantum computing:

Cloud reliability engineer

DevOps cloud engineer

Cloud architect and security architect

Cloud Infrastructure engineer

5G

With the growing time, 5G has become the next technology trend and the most in-demand tech skills. It enables services that rely on advanced technologies like AR and VR, cloud based gaming services like Google and lot more.

HD cameras with the implication of 5G helps to improve safety and traffic management, smart grid control and smart retail. Many telecom companies like Apple, Nokia corp, QualComm are really working om mobile traffic data making. It is estimated that by 2024, around 40% of the world will be utilised by 5G networks.

Drones are improving navigation and using the Internet of Things(IoT) to communicate with on-board devices. The development of 5G and 6G continues to improve smart cities around the world and support the drone market.

Telemedicine

Telemedicine has become the talk of the town during this pandemic situation. Many people are avoiding the risk of contracting the coronavirus to their workers and patients. The doctors and patients are communicating via video chat where artificial intelligence conducts diagnostics using photographs.

By early 2022, the number of remote receptions is going to increase a count of billion. It is also expected that machine learning will be gradually utilised in diagnostics, administrative work and creation of robots for healthcare

The Conclusion

Many technological advances in 2022 is going to continue with the impact of COVID-19. These trending technologies are welcoming skilled professionals with nice amount of salary. Master in these courses and get on-board at the early stages of these trending courses.

The post Top 10 Trending Tech Courses for 2022 appeared first on .

AI in investment management has resulted in the reduction of jobs, passive investments, decreasing confidence, and investment fees. On the other hand, it can all be a boon as it has started enabling people to make better decisions quickly and consistently. Since there is a great influence of artificial intelligence to overcome the challenges of asset management has resulted in great efficiency, risk management, and enhance decision making.

Let’s dive into some crucial areas where artificial intelligence in asset management can be easily leveraged and understand What is artificial intelligence currently used for?:

Data science use cases in asset management

AI in asset management in operational functions includes monitoring, quality maintenance, exception handling of the various amounts of information that is managed by managers alone.

The end customers can rely bank upon the data quality which makes fewer blunders and lessens operational risk.

In certain cases, data can be old, missing, or can contain errors, hence the AI in asset management can be utilized to identify anomalies that are based upon statistical assessments.

Digital advice

AI and ML tools can be easily utilized by investors to take better access to the financial markets and gain digital advice. A financial investment requires the proper asset allocation mix to meet its proper objectives understand “How can artificial intelligence be used in businesses? To meet these objectives, various attributes like a client’s age, risk tolerance, and desired income in retirement and model-based AI digital tools can help you select the proper asset allocation.

Digital advisors can utilize the AI asset management tools and give an approach to people to offer personalized advice at a lower cost.

Operational Efficiency

In the current digital landscape, asset management firms are imposing various cost-sensitive concerning the applications of artificial intelligence in designing new guidelines, fee pressures, and the step towards the lower-cost passive products.

Various organizations are exercising various programs with an emphasis on outsourcing and process automation. AI asset management is putting an impetus for firms to incorporate innovative operational excellence into services.

Early AI asset management always proves advantageous as they have the upside of changing “as a service” abilities into profit centers and making an upper hand. The speed enhancement offered by artificial intelligence asset management services helps to improve and move at a particularly greater speed. The services become both a defensible advantage and a sustained revenue source for firms.

User experiences and interfaces

Gone are the days when an individual investor used to historically contact a stockbroker to gain information about stock transactions. As an additional thing, they need to consult with a tax specialist or accountant to consider tax implications and understand the value of these investments. With the utilization of AI and ML and the implication of machine language in asset management, the customers can easily select the right asset allocation based on a user’s age, income, risks, and desired income retirement.

Digital advisors also incorporate personalized investment at a lower cost to gain personalized investment. They also offer tax-loss harvesting, portfolio allocation, and digital documentation delivery.

The Conclusion

In the coming future, technology continues to play an integral role in various asset management. These innovative tools are more affordable and the more availability of data continues to increase its utilization of machine language in asset management. It can eventually result in mitigating risks, reducing costs, gaining better returns, and delivering products and services for clients.

The post Who Is Using Artificial Intelligence / Machine Learning And For What Assets? appeared first on .

The technology leaders have started utilizing various technologies in our day-to-day life like telemedicine, remote learning and education, remote learning and education, entertainment, sports, and live event streaming, manufacturing and assembly and in various fields. The implementation of these smart building technologies brings up the benefits of sustainability, energy savings to become a major option for their selection.

In addition to the 5G, the technology leaders have started utilizing these technology trends in 2022 to improvise the living standards:

1. Farming and agriculture

2. Manufacturing industries, factories

3. Transportation and traffic control

4. Remote learning and education

5. Personal and professional day-to-day communications’

6. Entertainment, sports, and live streaming of events

7. Remote surgery and health record transmissions

Future Technology in 2022

The shift from close meetings to hybrid workforce

With the impact of COVID-19, the technology leaders agree that their team is working closely with the human resources leaders for the implementation of workplace technologies and apps for office check-ins, employee productivity, engagement, and mental health care. The technology leaders have started maintaining strong cybersecurity for a hybrid workforce of remote and in-office workers.

Cyber security

Cyber security seems to be one of the top emerging trends in 2022 related to the mobile and hybrid workforce by utilizing their own devices and cloud vulnerability. Drones are the latest invention designed for security, threat prevention, and surveillance as part of their business model. Stats prove that Brazil, China, India, and the US are some of the states where the utilization of drones is increasing.

An open-source distributed database utilized cryptography with the help of a distributed ledger. As in addition, blockchain- an upcoming future trend in 2022 enables trust among various individuals and third parties. Let’s dig into some of the uses of blockchain technology:

Machine-to-machine interaction became hassle-free in the Internet of Things.

Shipment tracking and contactless digital transactions

Connecting parties securement within a specified ecosystem.

Rise in robots

The next important technology-related change which has been experienced is the rise of robots and stats prove that around 77% state that robots will be utilized to enhance every business sector including sales, human resources, marketing, and IT. Manufacturing and assembly, hospital and patient care. earth and space exploration are some of the sectors where the utilization of robots is going to be increased.

Utilization of HR collaboration at its best!

With the onset of the pandemic, the future technological innovations made the technology leaders start involving various workplace technologies for human resources collaboration. Various companies are involving workplace technologies and apps for office check-in, space usage data, and analytics, COVIDand health protocols, enhancing employee productivity, mental health, and engagement.

It’s quite challenging to maintain cybersecurity for a hybrid workforce or remote and in-office workers to be viewed upon. The companies have started to decide the various preventive measures in the post-pandemic future.

The Concluding thoughts

Which next big technology breakthrough is going to last forever? Well, the answer is not definite. The pandemic has accelerated various technologies like as-a-service solutions for artificial intelligence, extended reality(augmented, virtual and mixed reality), robotics, machine learning, and various technologies. The technologies are making a powerful impact most on these marketing applications and make them more engaging.

The post Artificial Intelligence and Machine Learning, Cloud Computing Will Be the Most Important Technologies in 2022 appeared first on .

What is the definition of Artificial Intelligence(AI)?

While there have been numerous definitions of artificial intelligence (AI) throughout the previous few decades, John McCarthy provides the following description in this 2004 study (PDF, 106 KB),

“It is the science and engineering behind the development of intelligent machines, most notably intelligent computer programmers.

It is comparable to the analogous goal of utilizing computers to comprehend human intellect, but AI is not limited to physiologically observable methods.”

However, decades before this description, Alan Turing’s key work, “Computing Machinery and Intelligence” (PDF, 89.8 KB, was published in 1950.

Turing, frequently referred to be the “father of computer science,” poses the following issue in this paper: “Can machines think?”

From there, he proposes a test, now dubbed the “Turing Test,” in which a human interrogator attempts to discern between a computer-generated and a human-generated written response.

While this test has been subjected to considerable examination since its publication, it remains an integral element of the history of artificial intelligence as well as an ongoing philosophical notion due to its use of linguistic concepts.

Stuart Russell and Peter Norvig then published Artificial Intelligence: A Modern Approach, which quickly became one of the main textbooks on the subject.

They go into four distinct AI goals or definitions, distinguishing computer systems based on their logic and ability to think vs. their ability to act:

Humane method:

1.Human-like computer systems

2.Systems that behave similarly to humans

The optimal strategy is as follows:

1.Systems capable of rational thought

2.Systems that make rational decisions

Alan Turing’s notion would have been classified as “systems that behave like people.”

Artificial intelligence, in its simplest form, is a field that combines computer science with large datasets to facilitate problem-solving.

Additionally, it comprises the subfields of machine learning and deep learning, which are typically associated with artificial intelligence.

These fields are comprised of artificial intelligence algorithms aimed at developing expert systems capable of making predictions or classifications based on input data.

Today, there is still a lot of hype surrounding AI development, which is to be anticipated of any new emergent technology.

According to Gartner’s hype cycle, product innovations such as self-driving cars and personal assistants follow “a typical progression of innovation, from initial enthusiasm to disillusionment and finally to an understanding of the innovation’s relevance and role in a market or domain.”

As Lex Fridman observes here in his 2019 MIT speech, we are approaching the zenith of inflated expectations and the trough of disillusionment.

As discussions about the ethics of AI begin to emerge, we can witness the first signs of the trough of disillusionment.

Artificial intelligence classifications—weak AI vs. strong AI

Weak AI, also known as Narrow AI or Artificial Narrow Intelligence (ANI), is artificial intelligence that has been trained and focused on performing specific tasks.

Weak AI is responsible for the majority of the AI that surrounds us today.

‘Narrow’ may be a more true definition for this sort of AI, as it supports some quite strong applications, such as Apple’s Siri, Amazon’s Alexa, IBM Watson, and self-driving cars.

Artificial General Intelligence (AGI) and Artificial Super Intelligence are the two components of strong AI (ASI).

Artificial general intelligence (AGI), or general AI, is a speculative kind of artificial intelligence in which a machine possesses an intelligence equivalent to that of humans; it possesses a self-aware awareness capable of problem solving, learning, and planning for the future.

Artificial Super Intelligence (ASI) — often known as super intelligence — would outperform the human brain’s intelligence and capability.

While strong AI is yet purely theoretical with no practical applications, this does not mean that AI researchers are not investigating its development.

Meanwhile, the best instances of ASI may come from science fiction, such as HAL, 2001: A Space Odyssey’s superhuman, rogue computer aide. 

Machine learning vs. deep learning

Because deep learning and machine learning are frequently used interchangeably, it’s important to understand the distinctions between the two.

As previously stated, both deep learning and machine learning are subfields of artificial intelligence; in fact, deep learning is a subfield of machine learning.

A visual representation of the relationship between AI, machine learning, and deep learning

Deep learning is composed of neural networks.

The term “deep” in deep learning refers to a neural network with more than three layers—which includes the inputs and outputs.

This is often depicted by the diagram below:

The distinction between deep learning and machine learning lies in the manner in which each algorithm learns.

Deep learning automates a major portion of the feature extraction process, removing the need for manual human involvement and enabling the usage of bigger data sets.

Consider deep learning to be “scalable machine learning,” as Lex Fridman highlighted in the same MIT presentation mentioned above.

Machine learning that is more conventional, or “non-deep,” is more reliant on human involvement to learn.

Human specialists establish a hierarchy of features to comprehend the distinctions between data inputs, which typically requires more organised data to learn.

While “deep” machine learning can benefit from labelled datasets, commonly known as supervised learning, it does not require a labelled dataset.

It is capable of ingesting unstructured data in its raw form (e.g., text, photos) and automatically determining the hierarchy of features that differentiate distinct types of data.

Unlike machine learning, it does not require human assistance to interpret data, allowing for more innovative approaches to scale machine learning. 

Applications of artificial intelligence

Today, AI systems have a plethora of real-world applications.

The following are some of the more frequent examples:

Speech Recognition: is often referred to as automatic voice recognition (ASR), computer speech recognition, or speech-to-text. It is a capability that utilises natural language processing (NLP) to convert human speech to text.

Numerous mobile devices incorporate speech recognition into their systems to enable voice search—for example, Siri—or to increase messaging accessibility.

Customer service: Throughout the customer journey, online chatbots are displacing human agents.

They respond to commonly asked questions (FAQs) regarding issues such as shipping or offer personalised advise, such as cross-selling products or recommending appropriate sizes for users, fundamentally altering how we think about client involvement across websites and social media platforms.

Message bots on e-commerce sites with virtual agents, messaging programmes such as Slack and Facebook Messenger, and duties typically performed by virtual assistants and voice assistants are all examples.

Computer Vision: This artificial intelligence technology enables computers and systems to extract meaningful information from digital photos, videos, and other visual inputs and to take appropriate action based on that information.

This suggestion capability distinguishes it from image recognition tasks.

Computer vision, which is based on convolutional neural networks, has applications in social media photo tagging, radiological imaging in healthcare, and self-driving automobiles in the automotive industry.

Recommendation Engines: By analysing historical data on consumer behaviour, AI algorithms can assist identify data trends that can be leveraged to design more effective cross-selling techniques.

This is utilised by online businesses to give relevant add-on recommendations to customers throughout the checkout process.

Automated stock trading: Designed to optimise stock portfolios, AI-powered high-frequency trading platforms execute hundreds, if not millions, of trades daily without human interaction. 

The History of Artificial Intelligence: Significant Dates and Persons

The concept of a ‘thinking machine’ extends back to ancient Greece.

However, significant events and milestones in the evolution of artificial intelligence since the introduction of electronic computing (and concerning several of the subjects mentioned in this article) include the following:

1950: Computing Machinery and Intelligence is published by Alan Turing in Turing—famous for cracking the Nazis’ ENIGMA code during WWII—proposes in the article to address the topic ‘can machines think?’ and introduces the Turing Test to assess whether a computer can display the same intelligence (or the equivalent intelligence) as a person.

Since then, the Turing test’s utility has been contested.

1956: John McCarthy coined the phrase ‘artificial intelligence’ at Dartmouth College’s inaugural AI conference.

(McCarthy would later design the Lisp programming language.)

Later that year, Allen Newell, J.C. Shaw, and Herbert Simon develop the Logic Theorist, the world’s first functioning artificial intelligence computer programme.

Frank Rosenblatt creates the Mark 1 Perceptron, the world’s first computer built on a neural network that ‘learned’ via trial and error.

Only a year later, Marvin Minsky and Seymour Papert publish Perceptrons, which becomes both a seminal work on neural networks and, for a while, an argument against further neural network research.

1980s: Backpropagation neural networks, which train themselves using a backpropagation algorithm, become widely employed in artificial intelligence applications.

1997: IBM’s Deep Blue defeats Garry Kasparov, the global chess champion at the time, in a chess match (and rematch).

2011: IBM Watson defeats Jeopardy! champions Ken Jennings and Brad Rutter

2015: Baidu’s Minwa supercomputer utilises a type of deep neural network called a convolutional neural network to detect and classify images more accurately than the average person.

2016: DeepMind’s AlphaGo programme defeats Lee Sodol, the world champion Go player, in a five-game match powered by a deep neural network.

The victory is important in light of the game’s enormous number of possible plays (nearly 14.5 trillion after only four moves!).

Google later acquired DeepMind for an estimated $400 million. 

The post What is AI? Here’s everything you need to know about artificial intelligence. appeared first on .

What technology is powering this mode of conversion?

Amazon’s product recommendation technology is powered primarily by artificial intelligence (AI).

Aside from product recommendations, online retailers are using artificial intelligence in the eCommerce industry to provide chatbot services, analyze customer comments, and provide personalized services to online shoppers.

According to a 2019 Ubisend study, one in every five consumers is willing to buy goods or services from a chatbot, and 40 percent of online shoppers are looking for great offers and shopping deals from chatbots.

While global e-commerce sales are expected to reach $4.8 billion by 2021, Gartner predicts that by 2020, around 80% of all customer interactions will be managed by AI technologies (without the use of a human agent).

So, how is artificial intelligence in e-commerce changing the shopping experience in 2019?

Let’s look at some of the most important applications of artificial intelligence in eCommerce, as well as some real-world industry examples, in this article. 

How is Artificial Intelligence changing the shopping experience?

The use of artificial intelligence in online shopping is transforming the E-commerce industry by predicting shopping patterns based on the products purchased and when they are purchased.

For example, if online shoppers frequently buy a specific brand of rice every week, the online retailer could send these customers a personalized offer for this product, or even use a machine learning-enabled recommendation for a supplementary product that goes well with rice dishes.

AI-enabled digital assistants, such as the Google Duplex tool, are developing capabilities such as creating grocery lists (from the shopper’s natural voice) and even placing online shopping orders for them. 

4 Major AI Applications in E-commerce

While there are numerous benefits to using artificial intelligence in eCommerce, here are four major AI applications for eCommerce that are currently dominating the industry. 

Chatbots and other forms of virtual assistance

Chatbots or digital assistants are increasingly being used by e-commerce retailers to provide 24×7 support to their online customers.

Chatbots, which are built with AI technologies, are becoming more intuitive and enabling a better customer experience.

Chatbots, in addition to providing good customer service, is increasing the impact of AI in eCommerce through capabilities such as natural language processing (or NLP), which can interpret voice-based interactions with consumers.

• Providing deeper insights to consumers in order to meet their needs.
• They have self-learning abilities that allow them to improve over time.
• Customers should be given personalised or targeted offers. 

Product Recommendations That Are Intelligent

Personalized product recommendations for online shoppers are increasing conversion rates by 915 percent and average order values by 3 percent, according to one of the major applications of artificial intelligence in eCommerce.

AI in eCommerce is influencing customer choices through the use of big data, thanks to its knowledge of previous purchases, searched products, and online browsing habits.

Product recommendations provide numerous advantages to eCommerce retailers, including:

• a greater number of repeat customers
• Customer retention and sales have improved.
• Online shoppers can enjoy a more personalised shopping experience.
• Allow a personalised business email campaign to run. 

Ecommerce AI Personalization

Personalization, which is regarded as one of the most effective modes, is at the heart of AI in Ecommerce marketing.

AI and machine learning in Ecommerce are deriving important user insights from generated customer data based on specific data gathered from each online user.

For example, the AI-enabled tool Boomtrain can analyze customer data from multiple touchpoints (including mobile apps, email campaigns, and websites) to determine how they interact online.

These insights allow eCommerce retailers to make appropriate product recommendations while also providing a consistent user experience across all devices. 

Inventory Control

Efficient inventory management is all about keeping the right amount of inventory on hand to meet market demand while not adding to idle stock.

While traditional inventory management was limited to current stock levels, AI-enabled inventory management allows for stock maintenance based on data related to:

Sales trends in previous years

Changes in product demand that are projected or anticipated

Possible supply-related issues that could have an impact on inventory levels

Aside from inventory management, AI is enabling warehouse management with the emergence of automated robots, which is predicted to be the future of artificial intelligence in eCommerce.

Unlike human employees, AI robots can be used to store or retrieve stocks 24 hours a day, seven days a week, as well as immediately dispatch ordered items following online orders.

AI in the B2B Ecommerce sector is driving a slew of innovative solutions in addition to transforming the E-commerce industry in a variety of ways.

Let’s take a look at some of the most recent industry case studies on artificial intelligence and how it’s affecting this industry. 

Smart AI-Enabled Solutions for the Ecommerce Industry

AI-powered technologies are introducing online shoppers to a variety of products they had no idea existed on the market.

Sentient Technologies, for example, is developing virtual digital shoppers that can recommend new products to online shoppers based on their personal purchasing patterns and data insights.

With the success of the Amazon Alexa device, this E-commerce behemoth is introducing Alexa Voice Shopping, which allows you to review the best of Amazon’s daily deals and place online shopping orders with just your voice.

And there’s more.

Amazon Alexa can also give you wardrobe advice, such as the best fashion combinations and a comparison of outfits to see which one would look better on you.

AI is reducing the number of returned goods purchased through online sales in the Fashion eCommerce industry.

Zara, for example, is utilizing AI capabilities to suggest the appropriate apparel size (based on the shopper’s measurement) as well as their style preferences (loose or tight clothing).

This can assist the fashion brand in reducing product returns and increasing repeat purchases.

Aside from these advancements, AI-powered solutions are reshaping the E-commerce industry in the following areas:

• AI-powered email marketing that sends out marketing emails for products (or services) that the recipient is interested in. Aside from reading more humanly than automatedly, these email marketing tools conduct intelligent user analysis based on their response and are more tailored to individual customer needs.
• AI-enabled Supply Chain Automation enables effective supply chain management for e-commerce platforms. Other advantages include the ability to make business decisions about vendors, delivery schedules, and market needs.
• AI-powered data analytics tools for the e-commerce sector that offer a variety of advantages such as business intelligence, customer profiles, and online sale analysis.

Omnichannel AI solutions that provide a consistent and seamless customer experience across online and physical retail locations.

For example, Sephora’s AI-powered omnichannel solutions use a combination of AI and machine learning, natural language processing, and computer vision to bridge the gap between in-store and online customer experiences. 

Conclusion

As discussed in this article, artificial intelligence is playing a key role in driving innovative solutions and customer experiences in eCommerce.

Some of the most prominent applications of artificial intelligence in eCommerce are personalized shopping, product recommendations, and inventory management.

Are you thinking about how to implement a working model of artificial intelligence for your business as an online retailer?

Content is a well-established data analytics provider that provides solutions centered on product analytics and E-commerce KPIs for AI in eCommerce startups. 

The post The Impact of Artificial Intelligence on the E-commerce Industry appeared first on .