I am going to tell you the Difference between AIOps and Artificial intelligence (AI) on the basis of their Definition and how they work and what are the components of them. So let’s start.

What is AIOps?

AIOps stands for artificial intelligence for operations team promises to improve the events correlation, speed root cause analysis, and drive automation.

In other words, the ability to drive the automated process by using automation, whether the process is around incident management, remediation.

Let’s take an example- If you are getting so much alerts noise at the time of monitoring you could either ignore them or put lots of effort to solve that, but the AIOps is driven to drive the resolution to that issue with the help of automation, that means not much effort, work done in less time or say in a smarter way.

 AIOps is all about delivering a better customer experience, that’s why much more customers are adopting AI machine learning. With AIOps you can predict and fix most common IT problems before they impact customer experience and free up the IT teams to innovate.

AIOps leverages big data and collects data from different platforms like ops tools and devices to automatically spot and react to the issue in real-time.

The goal is to increase the speed of delivery of the services to improve the efficiency of IT services and in other words to provide a superior user experience.

It’s clear that AIOps break down the siloed operations and enable the generation of insights that can be communicated to stakeholders and it can help in driving automation and collaboration.

Need of AIOps

AIOPs offer clarity to performance data and dependencies throughout all environments, examine the data to take out the important events which are associated with outages or slow down, and automatically alert members to problems, the root causes, and recommended solutions.

Components of AIOps

1) Extensive and diverse IT Data

2) Aggregated big data platform

3) Machine learning

4) Observe

5) Engage

6) ACT

7) Automation

AIOPs bridges three different IT disciplines –

1) Service management

2) Performace management

3) Automation

What is artificial intelligence (AI)?

AI refers to the automation of tasks by feeding data or by taking the help of machine learning to learn new things by getting data from the internet, locally saved data, or from the instruction that has been installed to work like as instructed.

Machine learning is a kind of brain to AI that helps it to think or decide like a human brain but not completely because humans are creative. We can do anything by using our brains that can’t do machines.

AI had been thought of in 1955 and introduced in 1956 in a seminar by John McCarthy, that’s why we call him the father of AI as well.

It is said AI is our future but it’s not true AI is present as well as future.

Some examples that we are using currently are Alexa, Siri on iPhone, Google Assistant, Tesla car, Cortana on windows. All these are some examples of present AI that we are using and Google maps are also one of them and many more.

Artificial Intelligence (AI) in the field of computer science.

Stages of AI

1. General AI
2. Narrow AI
3. Artificial super intelligence

General AI means lots of works and activities can be done. Humans can dance, eat and do many more activities, in the same way, AI can also do multiple tasks. But unfortunately, we don’t have that much evolved AI right now. We can make it do any particular task that we want to make it done. In other words, we have only narrow AI’s right now.

Narrow AI means it is focused on any particular task that is assigned to it such as an application is designed to take a photo but a human can do anything with that photo. So this is the difference between AI and humans. (General and narrow AI).

Artificial super-intelligence means the machine which will surpass humans in thinking, behaving, etc, and can do much more which we can’t imagine. But we don’t have such kind of super-intelligence right now but. It is like hypothetical robots that have been shown in movies.

Advantages and Disadvantages of AI

Advantages

• Workloads can be decreased.
• Time can be saved.
• Errors can be reduced
• Automation
• To remember things easily
• We can use robots instead of humans as cops
• Designing and construction without hard work
• Can work without breaks
• Collection of data and many more.
• Solve problems and perform complicated tasks

Disadvantages

• Humans will become lazy.
• If somehow anyone can succeed in manipulating the AI then it can be dangerous to human’s kinds.
• Machines can keep an eye on us all the time by using cameras and many more, which means no privacy.
• It can give unemployment to people
• High cost of maintenance
• Can’t sense like humans
• Lack of creativity

Types of AI

• Reactive machine AI
• Limited memory AI
• Theory of mind AI
• Self-aware AI

Training Place

I would like to tell you about one of the best places to get trained and certification in DevOps, DevSecOps, SRE, AIOps, MLOps, GitOps, AI, and Machine learning courses is DevOpsSchool. This Platform offers the best trainers who have good experience in DevOps and also they provide a friendly eco-environment where you can learn comfortably and free to ask anything regarding your course and they are always ready to help you out whenever you need, that’s why they provide pdf’s, video, etc. to help you.

They also provide real-time projects to increase your knowledge and to make you tackle the real face of the working environment. It will increase the value of yours as well as your resume. So do check this platform if you guys are looking for any kind of training in any particular course and tools.

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Big data used to be, with hindsight, pedestrian. Conferences on the subject (in those days ‘data warehousing’) used to be about companies showing off. Big data seemed to have gone away, or at least gone very quiet. That scale of data (vast, huge, overwhelming, exciting) certainly concentrates the mind. Get it wrong and, well, you know the old handle that data is the new oil.

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In today’s business environment, data is key to success. With over 2.5 quintillion bytes of data created each day, data-driven insights are the main driver in every major business decision and are essential to discovering more efficient processes, reduction in risk or new sources of revenue.

However, harnessing the power of data continues to be a challenge, due to the on-going shortage of data science skills in the labour market, as demand for digital skills still far outstrips the supply. A recent UK government report found that nearly half of businesses (46%) have struggled to recruit for roles requiring hard data and analytics skills.

IDC estimates that by 2025 we’ll have created more than 175 zettabytes globally. As the world of business continues evolving, companies are moving fast and need fast solutions – they can no longer tolerate knowledge workers, delivering low strategic output from legacy tools for the enterprise. The sheer abundance of data and its growing complexity means data skilled workers able to harness it for fast and sound decisions will be at the forefront of the job market throughout the next decade.

While not every worker needs to become a data scientist, many businesses are turning to upskilling their employees to overcome this shortage. Building their own internal pool of talented data workers with the skills, desire, knowledge, and analytical expertise to be successful and thrive in an increasingly ‘data-rich’ environment.

Organisations have already started to recognize data literacy as an important skill for their workforce. A recent McKinsey study found that 84% of executive leaders – when increasing their talent pool of data specialists – experienced more success from upskilling their existing workforce, compared to just 16% who succeeded when hiring externally.  By providing analytics solutions that upskill information workers into data-literate knowledge workers, these knowledge workers – individually and collectively – can help drive organisational transformation.  Employees have the context of the business questions to solve as well as the knowledge of the data assets available that can drive answers through analytics.

Creating a culture of upskilling is by no means an easy feat. Getting employees engaged can be half the battle. It requires building a new culture where data is accessible to workers throughout the organisation, as well as significant investment in new tools and platforms that do not require users to know complex coding languages. Low code and no code solutions provide space for employees who want to upskill, learn and practice to become skilled data workers themselves.

By implementing formal upskilling programmes that focus on key skills and technologies, in addition to providing a learning curriculum that can result in valuable and credible certifications, companies can set themselves and their employees up for success. However, these programmes should not be dry and academic. In fact, the upskilling journey can be a social experience.

For instance, businesses can host “lunch and learn” activities and company-wide “data challenges” that bring people together from across the organisation, introduce staff to data science and make it appealing and accessible. Gamification strategies can also encourage staff to use online learning resources and develop their data skills by using leader boards, points scoring and creating personal challenges and achievements.

The aim is to create an open culture of learning where staff communicate and work together to solve data problems. A company’s existing data scientists should act as coaches to colleagues, encouraging them to think analytically and ask the right questions of datasets. This will help build data skills into every team, so that data analytics becomes an enterprise-wide initiative, rather than siloed into one team of analytics professionals.

The other benefit of this more social approach to data science is how it can impact diversity. Simply put, data science has a diversity problem: as few as 15% of data scientists are women. This lack of diversity is a huge concern, because with a diverse range of approaches and points of view to tackle data challenges and ensure data models and algorithms are free from biases, businesses will see improvement in results. It’s no secret that the more diverse the workforce the richer the business outcomes will be, research by McKinsey has shown that organisations with more ethnic and gender diversity are more likely to outperform.  When we value our varied experiences, they impact how we solve problems to get to better answers.

The evolving landscape of the data science and analytics market creates an inherent need for organisations to foster and grow data analytics cultures fuelled by collaboration and diversity, presenting an opportunity for all demographics traditionally underrepresented in the technology workforce, to accelerate their careers by embracing analytic roles. For business leaders, this represents an opportunity to look within for specialists with the right attitude to problem solving, not just technical aptitude, to support and upskill in both data literacy and analytics.

By investing in upskilling, people from any age, gender and background can learn vital data skills and progress their careers. It also enables companies to recruit new individuals who don’t necessarily have an academic background or specific coding skills, which may encourage a more diverse range of applicants. This was the experience of the sports and fitness apparel company Gymshark, which uses Alteryx to empower and upskill its employees.

“We’ve been able to expand faster because we are able to find these individuals easier, rather than having to find people with very specific skillsets,” says Gemma Hulbert, CDO at Gymshark. “New hires are now able to come in and hit the ground running right away with Alteryx, even though they aren’t data analysts. We are able to create apps that empower our employees to be able to learn new skills using the platform.”

Data science doesn’t have to be the preserve of the elite few. Anyone in the workforce with a passion for solving data puzzles is now able to do it, not just a handful of specialists. In the past, employees with vast expertise in their own fields were locked out of data analytics due to the technical knowledge it required.

With the right tools and investment, anyone can learn data skills, and when people are encouraged to be creative and think critically, they are able to ask the right questions and solve all sorts of problems. Thanks to self-service platforms and automation, the power of analytics is no longer restricted to a few gatekeepers, but rather it is available to all. By enabling employees to scale their passion for data science, businesses will accelerate the knowledge workers’ journey to become data-driven, be better able to unlock data-driven insights and tackle the world’s biggest problems with a successful digital transformation journey.

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In 2019, Google Cloud identified six sectors as vital components of its growth: public, healthcare, financial services, retail, media, and manufacturing. Within manufacturing, the cost of quality control and inspection continues to be among the highest. The American Society for Quality estimates that the price of quality may be as high as 15 to 20 percent of annual sales revenues for many organisations. For larger manufacturers, this translates into billions of dollars every year. Additionally, the rapid increase in production volumes makes it difficult for humans to manually inspect defects in computer chips and other products. To combat this, Google Cloud has recently announced an approach, backed by artificial intelligence (AI), for visual inspection. 

The newly launched Visual Inspection AI is a purpose-built tool to help manufacturers and related workers and businesses to inspect and reduce product defects and decrease quality control costs. Powered by Google Cloud Platform’s computer vision technology, Visual Inspection AI goes beyond the traditional methods of supporting manufacturing quality control through its general-purpose AI product, AutoML. 

According to Kevin Prouty, Group Vice President of Energy and Manufacturing at IDC, “Google Cloud’s approach to visual inspection is the roadmap most manufacturing companies are looking for.”

Visual Inspection AI aims to automate quality assurance workflows, thus allowing companies to identify and correct defects before shipping products. Through this, the new AI tool automates visual inspection using a set of AI and computer vision to improve production by increasing yields, reducing re-work, and cutting back on return-and-repair costs.  

Previous methods

COVID-19 has increasingly driven manufacturers to adopt AI into their production processes. According to a Google Cloud survey, 76 percent of executives say they have embraced digital enablers such as AI, data analytics and cloud computing. Additionally, 66 percent of manufacturers who use AI in their daily operations have stated that their reliance on the technology is increasing.

With this advancement, traditional methods to quality control inspections fall short. Traditionally, manufacturers include one or more steps to inspect products for defects visually. The visual inspection process is typically highly manual, making it vulnerable to human error and highly time-consuming. Moreover, traditional machinery used in machines are not flexible enough to adapt to product changes and can only detect a handful of defects at any time.

Artificial intelligence then is an agent that manufacturers are hopeful will bring in a more significant wave of innovation. Google Cloud listed multiple benefits of utilising AI, ranging from the reduced cognitive load for operators, fewer missed defects, no programming required (making it more flexible than previous machines), and the ability to detect hundreds of areas of interest on a product in seconds. 

Google’s new solution

As per Kyocera Communications Systems, a major manufacturer of mobile phones for wireless service providers, Visual Inspection AI is an innovative service that non-AI engineers can use. Google Cloud says that its new Visual Inspection AI meets the needs of quality, testing, manufacturing, and process engineers who might not be well-versed in AI despite being experts in their respective fields. Thus, the new tool paves the way to many substantial benefits compared to general-purpose machine learning (ML) models, such as superior computer vision technology, shorter time-to-value and high scalability. Through this, customers can deploy solutions within weeks, and an interactive user interface guides them through the steps. 

Visual Inspection AI has also improved accuracy by up to 10 times from general ML approaches. Finally, Visual Inspection AI deep goes beyond simple anomaly detection. Instead, it allows customers to train models that detect, classify and locate multiple defect types in a single image—doing so provides follow-up tasks on production lines to be automated. 

There are multitudes of ways in which businesses can use Google Cloud’s Visual Inspection AI in manufacturing. Automotive manufacturing, for one, can use it for paint shop surface inspection or press shop inspection—to look for scratches, dents, cracks or staining. On the other hand, electronics manufacturing could employ the tool for defects in printed circuit board components, and general-purpose manufacturing could improve upon procedures like packaging and label inspection, fabric inspection, metal welding seam inspections—to name a few. 

As per the above mentioned Google Cloud survey on manufacturing trends, the most common roadblock to AI integration is the lack of talent to leverage AI properly. Given this, Google Cloud’s new Visual Inspection AI appears as a brilliant step towards the proper deployment of artificial intelligence in the manufacturing industry.

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With humungous data being the reason why organizations function, the importance given to data cannot be merely put into words. Over the years, data has enjoyed prominence in every field that one can possibly think of. This is why everyone dreams of landing a job in this field. However, getting a little confused as to what is data science, big data and data analytics and how are they different from each other is natural. These three terms have utmost importance in the magical world of data. They are similar in certain aspects and different in other areas. That said, having a clear picture in mind regarding all of them would ultimately result in you making a better career choice. Here is everything you need to know about data science, big data and data analytics.

Data science

Data science revolves around filtering the data in a manner that it is possible to extract information and draw meaningful insights from it. This field takes into account both structured as well as unstructured data.

Skills required to become a data scientist

1. Coding languages like R, Python, Java, C/C++, etc.
2. Ability to work with unstructured and structured data.
3. Statistics and mathematics.
4. Understanding the business problem and objective.
5. Problem-solving
6. Critical thinking.
7. Strong communication skills.
8. Fair knowledge about Hadoop and SQL.

Applications of data science

1. One of the biggest applications of data science is in coming up with recommendations to the users based on the history. This is widely used by the E-commerce industry.
2. Digital marketing.

Data analytics

Data analytics is nothing but working on raw data to be able to reach conclusions. This further helps the management in making better decisions. The main objective behind data analytics is to take steps that lead to the growth of the organization. It is solely on the basis of data analytics that the management team decides on new steps to be taken, rejecting certain ideas and even re-working on the decisions already taken. Ultimately, what everything boils down to is – the organization should be in a position to make decisions that address the issues, if any and/or take the organization to a different level altogether.

Skills required to become a data analyst

1. Programming languages are a must to become a data R and Python are the two most sought-after languages by the recruiters.
2. The ability to visualise data.
3. Strong communication skills.
4. Sound knowledge of statistics and mathematics.
5. The ability to convert raw data into a form that it is possible to make better decisions.
6. Machine learning. This is yet another key aspect that one should not neglect when aiming to become a data analyst

Applications of data analytics

Data analytics has a wide range of applications. Some of them are –

1. Gaming
2. Travel and tourism.
3. Healthcare sector, etc.

Big data

The term “big data” evidently throws light on what it could be. Big data refers to huge volumes of data that cannot be processed effectively using traditional methods. The first step starts with processing the raw data that cannot be stored in any of the traditional systems. With data growing manifold, the term big data perfectly fits in. According to Gartner, “Big data is high-volume, and high-velocity or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.”

Skills required to become a big data specialist

1. The ability to identify which data is relevant.
2. The ability to create new methods to gather, interpret, and analyze a data
3. Statistical and mathematical skills.
4. Number crunching.
5. Understanding the business objectives.
6. The ability to come up with algorithms to be able to process the data.

Applications of big data

There are numerous applications of big data. Some of the key ones are –

1. Fraud analytics.
2. Telecommunication sector.
3. Customer analytics.

No matter which career path you choose, your career would be promising for the sole reason that data is here to stay! It will continue to play a vital role in our lives for the years to come.

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Companies should develop a data science strategy to drive business intelligence.

Business intelligence is not a luxury anymore but a necessity today. The rapid adoption of disruptive technologies has enabled companies to enhance growth and agility. Data fuels businesses in the current scenario and it enables companies to gain intelligent insights. Hence, data science is an important part of regular business processes. Different companies use different methods to optimize data and make better decisions. This is often called a data science strategy. The significance of a data science strategy is immense in driving growth and staying close to the customers.

Why A Strategy?

Creating an effective data science strategy is not as simple as it sounds. Our world is getting smarter every day and businesses need to stay on the competitive edge to achieve success. A data science strategy or data strategy will enable the company to reach the right data, metrics, and data resources with ease and better accessibility. Developing a strategy will need a company to first define its goals, it can be a larger and measurable goal like generating more revenue. The next step is to find the right data resources suitable to the business goal. The executives need to clearly define the questions that they want the data insights to answer so that the company does not end up following unuseful and wrong data. A clearly articulated business strategy can ease the process of developing a data science strategy.

Building A Strategy

As mentioned above, the initial step would be to define measurable goals and find the right data resources. Next is identifying the project infrastructures by understanding which technologies to use, should everything be developed internally or outsourced, etc. The company should also decide the data storage platform and the desired form in which you would like to get insights like visuals, charts, reports, and more. Building a data science strategy also involves deciding the algorithms and technological models that should be used. This includes AI, machine learning, statistical inference, and making clear if these algorithms need to be transparent and explainable.

Another most important step is constituting a data science team. Hiring data scientists can be a bit difficult today as the role is in high demand. The company should analyze if it is going to build an in-house data science team or hire experts from outside. Collecting and storing data will create regulatory obligations that need to be met. Companies should consider data governance as an essential component to avoid data becoming a risk and liability. For this, the data science strategy should include compliance, security measures, and privacy policies in place.

Once all these steps are accomplished, a company can then use data analytics to process the huge amount of data and get actionable insights through disruptive technologies. Data and analytics are the crucial part of business intelligence today and intelligent insights are the only way to understand the audience better and create personalized services.

Data science and machine learning go hand in hand. Machine learning, a subset of AI, is an effective and widely used tool to deliver data analysis and insights. Machine learning models can be fed with data and this will enable the machines to learn from these data and improve from past patterns and risk behaviors. Ensuring data quality often becomes a challenge and integrating machine learning into the data strategy can help overcome it. Machine learning can accurately detect errors with minimal human intervention. Machine learning and data science strategy intersect and this is the current business intelligence scenario. Hence, companies should have a data strategy in place to enhance growth and efficiency.

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You may have heard that the field of data science is comparatively new. You may have also heard that there is a high demand for professionals and the trend is likely to continue for the next decade or so. All these are definitely tempting and but are not enough to make an informed decision. As with any other degree, you cannot randomly become a data scientist without knowing the full story. You must judge whether this is the right career path for you and know what all comes after you have graduated.

To ease your process of selection and decision making, this post compiles a set of essential questions that you should get answers to before starting your career in Data Science. These answers will clear the picture for you and help you to take the first step right while joining the top data science courses after 12^th.

What do you do as a data scientist?

A common misconception that goes around is that the work of a data scientist is only about coding. The statement, in fact, is only partially true as the role of a data scientist is yet to be properly defined. Companies hire data scientists under different roles but the core job remains the same: deal with and analyse a massive set of data that will provide value to the company’s business goals.

By analysing data, you will have to solve matters such as how to increase the current productivity, improve product quality and customer satisfaction, and reduce the manufacturing time and so on. To summarise, you will have to increase the business’ revenue using data.

How much coding should you learn?

While data science may not only be about coding, it is definitely an integral part. Coding comes in during the mathematical processes of dealing with large quantities of data that you simply cannot solve manually.

For instance, if a course is teaching you Python, try to learn this language to an expert level. Even if you face any other language during your job, the core principles of using them remain the same. So, you may not know five different languages but you can still land a top job.

Who hires data scientists and what is the pay like?

Data scientists are required everywhere. You can get into fields such as technology, marketing, financial services, corporate setting, government services, healthcare, gaming and so on. In fact, tech companies hire most of the data scientists (41 per cent) followed by marketing companies (almost 13 per cent). To give you a clearer idea, Google, Facebook, Amazon and Flipkart hire data scientists on a regular basis.

As the demand is high and the number of professionals available is low, the pay in this field is generally high. You can easily expect a six-figure annual income once you graduate and that can easily go up to seven within a matter of two years. Depending on your skills, you can get any amount you want.

What does the future look like?

Data science has already been named the top job in America for the year 2016. Statistics also suggest that during the decade 2014-2024, the field has an expected growth rate of 11-14 per cent. Also, almost 80 per cent of the data scientists out there suggest that there is indeed a shortage of professionals in this current field. All these establishes the fact that data science is, in fact, the next big career option.

But the field is also prone to automation as most of the task is ultimately done by machines. Algorithms can run bulk quantities of data through tools and produce faster results than any human possibly can. However, this does not mean that machines will replace data scientists totally. The courses will teach you to understand the automatic algorithms, better their technology and use your creativity to invent new techniques. If you learn to find optimal solutions for a problem at hand, you will never be searching for jobs.

To conclude

If you are fresh out of the 12^th standard now, there cannot be any proper time to start your career in data science. Both new and old companies are starting to realise the importance of this field and investing without limits on the right professionals. Even universities have branches who recruit scientists to help firms with their data or develop tools to process things better and faster.

Seize the opportunity, select the right institution and kick start your career. You will be dealing with real-world data sets and the scope for growth is limitless. Data science is indeed the next big thing.

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There is a tremendous demand for voice AI all over the world, especially in the Indian market. In the post-pandemic world, the need for machines with touch-less UI is growing, and the voice AI comes across as the perfect solution, says Tapan Barman, Co-founder and CEO, Mihup

Voice AI technology is no longer a stranger to most of us, especially those with love for technology. Alexa manages devices in many homes today, and every single smartphone

has either Google Assistant or Siri waiting for the user’s commands. The advancement of voice AI has reached a level where we can talk to machines around us just the way we speak to another person. Many car companies are already offering embedded voice Assistants and others are set to follow the lead soon.

Research and market report pegged the current voice AI market at around US$ 100 billion and growing 15 percent YoY. There is a tremendous demand for voice AI all over the world, especially in the Indian market. In the post-pandemic world, the need for machines with touch-less UI is growing, and the voice AI comes across as the perfect solution. Many internet users are already using voice AI Assistants on their phones and devices to search for information, entertainment, music and commerce. As per a study by Adobe, 71 percent of American smart home speaker owners Claimed to use it daily while more than half used the speakers several times a day. According to brightlocal, 46 percent of voice AI users search for local businesses daily. In the digital world, the mantra for any business’s success is to meet the consumer where they want and when they want. That’s where companies that don’t integrate voice Assistants into their processes are likely to fall behind in the future.

At the backend, the AI-based conversational tools are software applications developed to automate natural language conversations. A company can use them as chat or voice assistants. A modern conversational AI system receives the user’s query through either written or spoken input. Then it offers the desired information with speed and accuracy similar to a regular conversation between humans. The existing tools are rooted in two major aspects: speech recognition and knowledge modelling.

Any voice AI Assistant’s efficiency, and accuracy will depend on its ability to comprehend the voice input correctly. This is where the first-generation voice AI tools had struggled as the American companies mostly produced them to understand the specific American accent. However, the modern voice assistants are becoming rapidly proficient in natural language processing as start-ups outside the US focus on creating phonetic based voice AI tools instead of those that rely on identifying words. The reliance on natural language processing and AI reduces the inaccuracies and empowers the voice assistants globally to deliver a more incredible experience to users worldwide. The market potential is huge, and with one of the largest populations on the planet, India offers great potential for companies using voice assistants.

There are many reasons why voice AI will be the future for business operations in the post-pandemic world. As we see already with many large companies, voice AI enables them to deploy automated round-the-clock customer service. The visitor engagement and traffic on websites and apps using such tools are usually higher as they can get relevant information in near real-time compared to the cumbersome process of contacting the sales team and seeking a response for their queries.

Voice AI is also playing a significant role in making customer service more efficient. They are taking up regular business communication and everyday customer service operations to free up precious human resources and focus on advanced tasks. When it comes to personalization and intuitive customer experience, the AI tools deliver excellent service in verticals such as sales, marketing, HR and others. Since the voice AI and chatbots store their conversations with the consumers, they also facilitate great insights into consumer behavior and expectations and improve their user experience. They can also generate leads and create products and services that are more likely to appeal to their consumers. While usage of consumer data to analyze and extract insights for the business is nothing new, the voice AI has transformational abilities in this arena. The analysis of speech-based data goes beyond typical data. It provides consumer feedback, but the tone, language patterns, and acoustics enable the systems to pick up consumer sentiments. Whether someone is agitated or happy, complaining or making suggestions or sharing positive feedback, it is acknowledged with greater clarity and impact with interaction analytics.

Thus, it is imperative for any company that envisions itself as a leading brand of the future to plan using AI voice assistant technology in its operations soon!

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CBSE has launched an artificial intelligence community for students across the country in collaboration with Intel India to create an AI-ready generation

CBSE in partnership with Intel India has launched an artificial intelligence community for students in its efforts to help build a digital-first mindset and support an AI-ready generation. The platform aims at encouraging students to build real-life social impact artificial intelligence solutions as well as to collaborate and share their creations with others in the community.

The AI Student Community (AISC) is open for all students of the Central Board of Secondary Education (CBSE) and also for students of non-CBSE schools. The platform has been created in line with the National Education Policy 2020, which emphasizes on the importance of artificial intelligence and highlights on the efforts needed to prepare students of the country for an AI-driven economy.

Benefits of Joining the CBSE Artificial Intelligence Community for Students

Students who join the platform will be access to free videos on artificial intelligence and python, introduction to AI, AI domains, AI ethics, self-directed python notebooks, and so on. Apart from that, the platform also offers various other benefits to students such as:

• Students will be able to learn artificial intelligence as a skill and its application for creating social impact projects through webinars with Intel AI-certified coaches and experts.
• Participants will be given access to AI learning resources curated from across the world.
• Students can also attend webinars and face-to-face boot camps to enhance their artificial intelligence skills.
• Students can participate in online challenges to test their knowledge and up-level themselves, and also share their experiences as blogs.
• The community will serve as a platform for participants to connect with other students from across the country, and discuss and deliberate on relevant AI topics in a moderator curated forum.
• National and international competitions will be organised for participants.

How to Join the CBSE Artificial Intelligence Community for Students

Interested students, who belong to classes 6 to 12, can join the platform by visiting the official page and then carrying out the following steps:

• Click on ‘Sign Up’ and fill in the required details such as name, name of school, gender, etc. and submit.
• A verification email will then be sent on the registered email-ID, using which students will have to verify their email address.
• After that, the student’s profile will be created and their registration confirmed as a member of the AISC.

Students, once registered, can log in anytime using their email address and password to access all the features of the CBSE Artificial Intelligence Community.

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Artificial intelligence (AI) is doing what the tech-world Cassandras have been predicting for some time: It is sending out curve balls, leaving a trail of misadventures and tricky questions around the ethics of using synthetic intelligence. Sometimes, spotting and understanding the dilemmas AI presents is easy, but often it is difficult to pin down the exact nature of the ethical questions it raises.

We need to heighten our awareness around the changes that AI demands in our thinking. If we don’t, AI will trigger embarrassing situations, erode reputations and damage businesses.

Positive and Negative Results From Using AI

Two years ago, Amazon abandoned the AI tool it used to recruit employees. The tool, which the company trained using resumes submitted to the company over a decade, preferred male applicants. Recently, Twitter apologized for deploying an image cropping AI which preferred white faces over black. These are embarrassing (and unforgivable) outcomes of AI, but the ethical implications are clear.  

By contrast, the example of a South Korean national broadcaster, SBS, using AI to render songs in the voice of folk-rock singer Kim Kwang-Seok is delightful but considerably more complex. The popular singer has been dead for 25 years, yet continues to have a large fan following. SBS used 20 songs by Kim Kwang-Seok as a training tool and another 700 Korean folk songs to sharpen the accuracy of the AI. The AI now mimics any song in Kim Kwang-Seok’s style. A song, originally by Kim Bum-soo, rendered in the voice of Kim Kwang-Seok using AI, aired late in January. It was so perfect that it brought tears to the eyes of Kim Kwang-Seok fans. Music executives on the other hand were baffled: Who should the work be attributed to? Who owns the copyright for the work? Who will be paid royalties for the work? Will it be the AI programmer? The producer? For the curious, SBS paid a one-off fee to Kim Kwang-Seok’s family for borrowing his voice in the show. But publishing the song commercially presents perplexing questions. 

Tomorrow’s songs need not necessarily be written by humans either. OpenAI’s text generators, like generative pre-training 3 (GPT-3), could use deep learning/machine learning to write original songs that appear to be penned by Kim Bum-soo or any other song writer. This opens limitless possibility to continue to produce work by an artist long after their death. Could this mean that AI can write and direct “2050: Beyond the Future” to keep alive the cinematic magic created by Arthur C. Clarke and Stanley Kubrick with “2001: A Space Odyssey”?

GPT-3 has the potential to do that. Last June it sent powerful waves across the AI community when Sharif Shameem, the app development head of a startup, used it to construct a program by simply describing a UI in plain English. GPT-3 responded by spitting out JSX code. That code produced a UI matching what Shameem wanted. Shameen said, “I only had to write two samples to give GPT-3 context for what I wanted it to do. It then properly formatted all of the other samples.”

GPT-3 doesn’t only reproduce “stuff” like humans. It is a performer as well. In one instance, it was given code in Python and asked to describe what the code does. The program not only did that, it also offered improvements and suggestions on where to post it after the improvement. GPT-3 can identify paintings from descriptions and recommend books. It can write entire articles for publications. In one instance, GPT-3 managed to express a bunch of popular movies in emoji. The extraordinary part? GPT-3 requires no training. It uses 175 billion parameters (by comparison, the closest anything comes to GPT-3 is Microsoft’s Turing NLG, which uses 17 billion parameters) to generate text that sounds human. You could use it to write your next quarterly report and save some valuable time.

The Danger of Deep Fakes

There are obvious social dangers in deploying AI like this, the most direct being bad training data used by machine learning systems leading to the Amazon recruitment breakdown or the Twitter image cropping fail. But worse lurks around the corner. It is easy to use capabilities of the type used by the Korean broadcaster and those of GPT-3 to produce deep fakes.

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