In a supervised mode, the input and output parameters and the data fed into them help a machine generate the answers required of them. In an unsupervised mode, the machine unlocks the patterns and interpretation from all the data fed into it. In reinforcement learning, we tap the ability of machines to learn from even bad decisions and past actions. These lessons help them to churn out insights. Here are a few real-life examples of machine intelligence:

1. Voice assistants
2. Fraud detection in insurance
3. Algorithmic trading
4. Anti-money laundering apps
5. Robo advisors in BFSI
6. RPA in operations
7. Smart mining
8. Predictive maintenance and Cobots in factories
9. Geo-analytics for agriculture
10. Personalized, real-time, and contextual marketing
11. Self-driving vehicles
12. Law and Order support – with image and speech recognition
13. Smart City applications
14. Traffic monitoring
15. Personalized medication
16. Smart drug discovery, triage, and diagnosis
17. Robotic surgeons
18. Predictive advertising
19. Product personalization
20. Climate modeling
21. Cyber-security
22. Natural language processing (NLP)

Why do we need to know?

Of course, machines are helping us solve many questions in our lives. But there is a flip side to this power. The quintessential ‘Black Box’ problem of AI is a real challenge when applying machine intelligence. Even if a machine is spinning out beautiful and thunder-fast answers – one has to know what is going on inside the box that is leading to these answers. It is significant to see the process of helping a machine learn anything. Otherwise, these insights can easily be prone to costly errors, bias, discrimination, false positives, and ethical problems. 

According to McKinsey’s ‘The State of AI in 2021’ report, 57% cited cybersecurity as a relevant AI risk. Some companies also report personal and individual privacy as a relevant artificial intelligence risk more often. Explainability continues to be an important risk area – whether it is for emerging economies or developed ones. Both these economies also found fairness and equity as significant risk factors with AI. What is notable here is that high performers put in effort in managing these risks. For example, data professionals actively check for skewed or biased data during data ingestion (47%), data professionals actively check for skewed or biased data at several stages of model development, and high performers have a dedicated governance committee that includes risk and legal professionals.

Machines emerge as intelligent edifices that complement and elevate human efficiency, decision-making, and action. But they are learning based on data that is fed into them or which they pick on their own. The quantity, and context, of this data, can change the outcomes of a model. So humans cannot wash off their responsibility by just making data available to machines. They need to go further and be cognizant of this data’s quality, use, and application. They should know what is happening inside the machine as it processes this data, learns from it, and uses it as per the algorithm or experience it works on. Spending good time in programming, setting boundaries, and controlling measures are essential parts of machine learning. They matter a lot – even if the machine is excellent in speed.

It is advisable and critical to know how your machines learn what they are learning. In the real world out there, it can make a lot of difference – not just between a high-performer and an average runner. But between a responsible human and a clueless one.

The post How Does Machine Intelligence Work? appeared first on TalentSprint.

At first, it was like a meteor –coming from far away somewhere and sparking off a lot of curiosity. Then it became like water – slowly pouring into many aspects of business and everyday life. And pretty soon, it will be like air- all around us, essential and still invisible.

The sharp rise, and consistent maturity, of AI, are truly remarkable. AI and its subsets like machine learning and deep learning have permeated almost every industry and application today. Just pause and check around your room or inbox- I am sure that AI is now driving a significant chunk of your day.

From a handshake to a bear hug

AI, put simply, is the ability of a computer or a robot or any form of technology to perform task/s that are usually done by humans – and done through simulating human capabilities and human intelligence. For example, machine learning is a specific type of AI program which enables a system to learn and improve from experience without any explicit programming automatically.

Let’s take a quick walk through some reports to get a snapshot of how fast AI has started redefining our work and lives. 

As per a recent O’Reilly annual survey on AI adoption, 26 percent of organizations had AI projects in production, 43 percent said they are evaluating it, and 31 percent are not using it. 

Stanford’s AI Index Report 2022 shows that private investment almost doubled between 2020 and 2021- touching around $93.5 billion. In addition, AI is becoming easy to deploy and quite affordable as time goes by. In 2018, the cost to train an image classification system dipped by 63.6 percent, while training times have jumped by 94.4 percent. Lower training cost but faster training time is a pattern that has echoed in other MLPerf task categories such as recommendation, object detection, and language processing as well.

As per 451 Research’s ‘Voice of the Enterprise: AI & Machine Learning, Use Cases 2021’ survey, 95 percent of respondents say AI/ML was very or somewhat important to their digital transformation efforts. Almost three in four enterprises have either invested in MLOps tools or plan to do so in the next 12 months. It is expected that the adoption of MLOps will go on unabated in 2022. 

According to S&P Global Market Intelligence, AI and its machine learning subset are adopted across enterprises to optimize, automate and augment digital transformation strategies. The analysts here expect take-up of more advanced use cases and the long-standing use cases, such as fraud detection in financial services and churn analysis in telecom. It is also signified that more use-cases will emerge for AI for employee safety in manufacturing, clinical trial analysis in healthcare/life sciences, and vision analytics for infrastructure inspection in telecom.

We have already started feeling AI as a standard ingredient in our lives when we use chatbots in banking, or AI-apps for food ordering, or AI-based algorithms to decide what to watch on OTT. However, many more applications will emerge and gain a stronghold as AI gets more mature, easy to execute, and ROI-friendly.

All these applications are translating into good numbers. The global artificial intelligence services market is slated to rise from $8.24 billion in 2021 to $12.39 billion in 2022, as per data from ReportLinker. It has been explained that a lot of this growth is due to companies resuming their operations and adapting to the new normal while recovering from the COVID-19 impact. The market is expected to reach $58.93 billion in 2026. A significant portion of the artificial intelligence (AI) services market would entail sales of AI services in telecommunications, government, retail, defense, and healthcare. We will also see the strengthening of ‘AI as a service’ as businesses start to use AI for different purposes, without significant initial investment, and with lower risk. However, fuelling this growth would need a good strength on the AI workforce side.

More AI means need for more human capabilities

As seen in the O’Reilly survey, respondents with AI in production, and respondents who were evaluating AI, said that the most significant bottlenecks were lack of skilled people and lack of data or data quality issues (both at 20 percent), apart from problems like difficulty in finding appropriate use cases (16 percent). In addition, the lack of people with relevant cp was shared as a critical challenge by organizations with AI in production and those evaluating AI (20 to 25 percent).

The next set of risks was in unexpected outcomes (68 percent), model interpretability, and model degradation (both 61 percent). Then there are issues like Interpretability, privacy (54 percent), fairness (51 percent), and safety (46 percent). Interestingly, all these are some form of human issues.

Technology comes with plenty of its own challenges — from deploying at scale to overcoming bottlenecks and skill shortages during development – as outlined by S&P Global Market Intelligence too. 

AI may be moving the needle towards a mint-fresh future – but organizations would need the right talent and the support of relevant capabilities and tools to leverage this potential optimally.

AI will be as pervasive and imperative as air for many enterprises – straddling many industries. But keeping this air breathable and clean would be a job that would need humans – and at a different level of expertise and excellence than ever.

Let’s enter this age of AI – armed with clarity, confidence, and excitement. Having AI by our side would be like having the best breed of dogs – but to make these pets loyal, well-trained and fast – now that would still be a job for humans. Won’t it?

The post Real Life Applications of Research Areas appeared first on TalentSprint.

“It’s not about the shoes. It’s what you do in them”.

This punch-line of a trendy sneaker brand- could not have been more relevant than today. Contrary to what many sneaker-heads would opine, the actual value of a good shoe lies in its on-ground sturdiness. It’s all about being rugged in all weathers – whether AI or Leather.

Of all the breakthroughs, AI is not supposed to gather dust in a closet. And its unlimited potential hinges a lot on the user of artificial intelligence (AI) – how and where that user applies it. So has AI been advancing well on the street? Has it gotten off the kerb and explored real-life applications and research areas? And if so, what kind of tasks and adoption curves have we seen so far? Let’s test these shoes.

1. Real life applications of research areas  
2. Task classification of artificial intelligence (AI) 
3. Artificial intelligence (AI) and neural networks
4. Conclusion

1. Good shoes take you to good places! Real-life applications

Artificial intelligence (AI) is expanding in many dimensions of powerful research and applications – especially in the last few years. Enterprises- across almost every vertical type- are experimenting with artificial intelligence (AI) to accelerate decisions, improve precision, achieve customer-centricity, and gain new efficiencies.

Deloitte’s State of AI report 2020 outlines how AI benefits are multi-faceted and hard to ignore. In a survey of 1900 early adopters across seven countries here, it was seen that:

• 43 percent find it useful to enhance products and services
• 41 percent for optimizing internal business operations
• 34 percent for making better business decisions
• 31 percent for optimizing external business processes
• 28 percent find it helpful to create new products and services
• 27 percent use it to pursue new markets
• 26 percent capture and apply insufficient knowledge

AI’s Acceleration

• Expert levels of tasks
• Hi-end research work
• Applications in many verticals
• Wider and faster adoption
• New investments and bold traction

As for research, it is hard to deny the increasing attraction which AI manifests today. In 2019, 65 percent of graduating North American PhDs in AI went into the industry—this was considerably up from 44.4 percent in 2010. It reiterates the more significant role the industry has begun to play in AI development (AI Index Report 2021 by Stanford University and HAI). We also saw that the number of AI journal publications went up by 34.5 percent from 2019 to 2020—this was relatively better than what we saw from 2018 to 2019 (19.6 percent). In just the last six years, the number of AI-related publications on arXiv has blossomed over six-fold, from 5,478 in 2015 to 34,736 in 2020. It is also causing researchers to invest in technologies for the detection of generative models. The DeepFake Detection Challenge data, for instance, indicates how well computers can distinguish between different outputs.

Plus, An AI Index survey conducted in 2020 suggests that the world’s top universities have pumped their investment in AI education over the past four years. In the European Union, the vast majority of specialized AI academic offerings are taught at the master’s level; we see that robotics and automation is the most frequently taught course in the specialized bachelor’s and master’s programs, while machine learning (ML) is emerging as a predominant one in the specialized short courses. If this is the scenario on the research side, the business side translation is quite strong too. The advent of AI is attaining higher degrees of scale, sophistication, and impact, as visible already in industries of enormous complexity, stakes, and difficulty levels.

According to the AI Index Report 2021 by Stanford University and HAI, AI investment in drug design and discovery increased significantly: “Drugs, Cancer, Molecular, Drug Discovery” received the most significant level of private AI investment in 2020, with more than $13.8 billion – this was 4.5 times higher than 2019. Also, AI systems can now compose text, audio, and images to a sufficiently high standard. In fact, humans have a hard time telling the difference between synthetic and non-synthetic outputs for some constrained technology applications. Scientists have started to use ML models to learn representations of chemical molecules for more effective chemical synthesis planning.

2. Don’t change the foot, if the shoe doesn’t fit! Task classification

Let’s just take the tasks that AI does- today. Based on this characteristic, we can slice AI into Mundane, formal, and Expert tasks. The Mundane ones incorporate areas like perception, vision, speech, natural languages, understanding, reasoning, translation, and robot control. The Formal ones comprise complex mathematical, logical, and geometrical areas. Finally, the Expert ones are where we can bring in financial analysis, diagnosis, engineering, simulation, and scientific experiments.

3. Artificial intelligence (AI) and neural networks

An interesting pillar of AI’s explosive rise is the progress made in the area of neural networks. Put simply- these networks run the algorithms that form the core of any AI model. They are loosely constructed on the map of human biological networks in the brain. They work by drawing reference from the collection of units or nodes called neurons – which model the neurons in the brain. They use this to form a system of hardware and software which is shaped on these highly interconnected processing elements (neurons). Further, an artificial neural network (ANN) can evolve from the fundamental – and it can be used to comprehend the relationship between datasets to generate a desired output. To arrive at that, the system simulates a human brain via some deep learning technologies to solve complex pattern recognition or signal processing problems.

Here are some applications of neural networks:

• Weather prediction
• Handwriting recognition
• Fraud detection
• Risk analysis
• Oil-exploration data analysis
• Facial recognition
• Speech-to-text transcription
• Banking- credit and loan application evaluation
• Predictive analytics for loan delinquencies
• Transport- power routing systems, truck brake diagnosis systems, and vehicle scheduling
• Healthcare- cancer cell analysis, advanced diagnostics and design
• Customer support 

We can notice that as AI models and applications explode in growth, there is a corresponding rise in the use of neural networks. In the estimates of Allied Market Research, the global neural network market stood at $14.35 billion in 2020 and can climb to $152.61 billion by 2030. Furthermore, as per MarketsandMarkets, the global artificial neural network market can show a spurt from $117 million in 2019 to $296 million by 2024. 

The fast state of adoption can be understood better by getting closer to the breakdown of tasks that AI handles. Of course, they differ in their complexity and tech maturity. But as we will reckon ahead, AI is evolving quickly here.

In the AI-Index report from Stanford, what also emerges is a trend of AI gaining traction and confidence in the expert segment of tasks. Computer vision is now industrializing rapidly. Companies are investing increasingly large amounts of computational resources in training computer vision systems- this is happening faster than ever before. Then technologies for use in deployed systems—like object-detection frameworks for analysis of still frames from videos—also show AI deployment and maturity. We can also observe notable progress in natural language processing (NLP). That has yielded AI systems with significantly-improved language capabilities to have a meaningful economic impact on the world. 

The McKinsey State of AI 2021 survey shows that AI adoption continues to grow and that the benefits remain significant— though, in the COVID-19 pandemic’s first year, they were felt more strongly on the cost-savings front than on the top line. Moreover, as AI’s use in business becomes more common, the tools and best practices to make the most out of AI have also become more sophisticated. Fifty-six percent of all respondents report AI adoption in at least one function, up from 50 percent in 2020. Notably, AI adoption increased most at companies headquartered in emerging economies, including China, the Middle East, and North Africa: 57 percent of respondents signified strong adoption, up from 45 percent in 2020. And across regions, the adoption rate is quite huge at Indian companies, followed closely by those in Asia–Pacific. 

AI adoption is becoming common in areas like service operations, product and service development, and marketing and sales. Popular use-cases are service-operations optimization, AI-based enhancement of products, and contact-center automation – do note that the most significant percentage-point jump has been in the use of AI in marketing-budget allocation and spending effectiveness.

AI’s Hiccups

• Lack of quality AI talent
• Inadequate upskilling and reskilling
• High demand for new AI roles
• Need for AI builders as well as translators

Leveraging the ultimate potential of AI needs a lot of work in many areas. If we look at some obstacles enterprises face before/with AI adoption, we reckon that some struggles need particular focus and interventions.

There are a lot of challenges that envelope AI adoption. For example, in Deloitte’s State of AI report 2020, it can be seen that enterprises face several challenges – from integrating AI in the company’s roles and functions (38 percent), to data issues (38 percent), to implementation issues (37 percent), measuring and providing business value (33 percent).

But to top it all, a lot of shoe-horning remains to be done with AI on the aspect of talent. Unless AI is supported with the right skill sets, unless professionals keep reskilling and up-skilling in AI, it would be a futile exercise to have hope in AI. The power and possibilities of AI are pointless if adequate human energy, proficiency, support, and dexterity are not around to steer it.

As per a Global AI Talent Report 2020, the growth rate of specifically AI devs (-68.8 percent) is inverse to that of data scientists (102.7 percent). On the other hand, median growth rates for data analyst, data scientist, and ML engineer roles attained specific stability in 2019, between 1.24 percent and 3.28 percent. ML researcher roles, meanwhile, have a much higher median growth rate of 6.28 percent. Add to this that researchers make up 1.77 percent of the demand measured here, while they only comparatively make up 1.02 percent of the talent supply available to the industry. We can expect that competition will get even tighter for research talent. As a result, more researchers may be drawn out of academia.

The report argues that AI devs need to understand the underlying engineering to fit novel AI tools into the software. The challenging problems to solve in AI software development are why AI has been largely inaccessible to smaller teams who can’t afford both ML engineers and specialized AI devs. The aim to democratize AI with standard, out-of-the-box tools could allow the average developer with some online AI training to implement AI in their software. This is good for smaller teams as it will enable them to put more of their budget towards implementation rather than engineering and still be able to tweak out-of-the-box methods with some customization.

The report also highlights that as AI matures, it will become more pervasive. We will see new specialized roles emerge for managing the new dynamics of AI, but eventually, everyone will need to update their digital skills to collaborate with this new technology. Already, we have seen that most people can grasp the concept of an AI-powered recommendation algorithm and adjust their behavior to affect the algorithm’s output. However, people have the minimal choice and only blunt tools for manipulating an algorithm to their needs. When the different tooling and skill sets standardize along the value chain, it will vastly increase the choice and access to AI technology and engender far more innovation than we have seen with AI software.

To get there, we challenge bridging the gap between proof-of-concept in the lab and real-world deployment. Researchers and engineers play an essential role in helping close that gap, but they cannot do it alone. The institutions that train them have to sharpen their focus on standardizing their tools and processes so that others can more easily strike collaborations with others down the value chain.

The proportionate mix of specialized engineering, technical implementation, and research roles in demand is closely matched with the combination of supply: about 61 percent for implementation roles building the software around AI capabilities, 38 percent for AI engineering roles building the core AI capabilities, one percent for researchers. We do not know the aggregate amounts, but the monthly flows grew steadily in 2019, around 2-6 percent for different job titles. Unsurprisingly, we saw 20-30 percent drops in demand for the relevant job titles during 2020, but both 2019 and 2020 show significant outliers for those entering the scene and persisting through the pandemic. India’s capacity to attract and retain talent in 2018-19 showed -0.183 that were invited and -0.315 that stayed.   

A survey by The European Commission recently found businesses identified access to the right skill sets as being the number one impediment to adopting AI. Also -as per a Nasscom report, the demand for highly-qualified, experienced, and technically adept talent in the field of AI and BDA outstrips the current supply. Therefore, there is a severe need to narrow the demand-supply gap by upskilling talent across the AI and BDA occupations. While total job openings stood at about 140K in 2018, they touched about 230K in 2021. But university talent was about 78K in 2018 and projected to hit only some 90K in 2021 – showing a talent-supply gap of approx—140K in 2021. India had an employed talent pool of about 370,000 skilled in AI and BDA in 2018, and if we look at the projected total demand for AI and BDA for 2021, it was around 800,000.

In the Deloitte’s State of AI report 2020, we can see that 68 percent of executives report a moderate-to-extreme skill gap, and 27 percent find the skill gap an extreme one. The top four most needed AI roles are AI builders – this segment entails the need for AI researchers (30 percent), software developers (28 percent), data scientists (25 percent), and project managers (23 percent). Companies also need AI translators to bridge the divide between business and technical staff. This is required at both the front-end and back-end of building AI solutions.

Here the most sought-after professionals are:

• Business leaders (22 percent).
• Change management experts (22 percent).
• User experience designers (21 percent).
• Subject matter experts (20 percent).

4. Conclusion

AI’s potential cannot be realized without adequate support from the talented side. With AI, it looks like the world is at your feet, so it’s all the more vital that you should wear the right shoes. AI is a big turning point for this world – from compelling research areas and unprecedented applications to staggering levels of human augmentation. Let’s make the most of this turn. 

As they say- Cinderella is proof that a new pair of shoes can change your life. 

The post Research Areas of Artificial Intelligence (AI) appeared first on TalentSprint.

Technology is not an extra container but the engine in the ship of any supply-chain enterprise. A world-class, diverse and engaged technology organization would be the most significant competitive advantage in the current landscape. As Gartner has predicted already, by 2023, 50 percent of global product-centric enterprises will have invested in real-time transportation visibility platforms. Also, by 2025, over 50 percent of supply chain organizations will have a technology leadership role. This could report directly to the chief supply chain officer.

A significant shift is being witnessed in the overall capabilities, new investments, people engagement, and talent recruitment at many levels in the Logistics and Supply Chain Industry. This can be seen in the journey of logistics majors like Maersk.

Take a new turn
The company has covered this transformation splendidly and is quite a template for how to embrace disruption. What is behind the technology transformation at Maersk, which we all know as a shipping company?”

It is an enterprise that has been into shipping for 100 years. But it realized that to be relevant for the next 100 years, it has to do something different. It cannot do so without building its technology capabilities. In 2016, to survive and be successful in another century – it needed to look at the industry differently. it needed to be an integrated supplier and simplify things for our customers. Technology had to be a competitive advantage. It went towards an in-sourcing model. It started building new strengths and is now witnessing how the value is being translated in how its customers view it.

All this was unlocked in #WhatIndustryWants, an interactive fireside chat.  #WhatIndustryWants is a TalentSprint initiative that brings DeepTech aspirants face to face with industry professionals to answer “What Industry Wants” from the talent pool of this country. The event provides necessary exposure to participants on the emerging tech trends, opportunities prevailing in the tech industry, how to build the right expertise, and much more. The event brings in senior industry professionals to discuss and share their unique perspectives and insights into the evolving tech landscape. In this edition, several aspects were unraveled that explained the role of technology in logistics. 

All hands on deck
For instance, in the last year, the company has been investing heavily into technology. People from the central part of the transformation – their competencies matter. It is driving for the whole of Maersk to become a technology organization. There are many areas to it – developing people, offering them development opportunities, industry best practices, agile organization, leadership engagement, and more. Enterprise Platform, Engineering Architecture, Data Engineering, Data Science, Cyber Security are in focus now. The same is true for services and operations. The top transformation team does not expect everyone to tick all boxes. It helps them grow into world-class people it aspires for. It is what they are exposed to, it is what they build, it is what they see the colleagues working on – that drives it all.

Logistics and Supply Chain Industry – #WhatIndustryWants, an interactive fireside chat with Jaafar Al Ahmar, Global Head of HR, Technology and Finance, Maersk.

This company is disrupting an industry, and that is exciting. It is a good cue for aspirants on how to make it big with a suitable kit of skills and attitude. That’s why having focus, patience, a sense of direction, and alignment with the company’s broader goals is crucial for any professional.

Pack your bags well
Those who can keep up today will have exciting jobs in the future. The fundamentals of technology are important. Motion Graphics, Blockchain, and Drone Technology are exciting areas. Become world class in technology and go beyond the normal. You need to understand what is beyond the normal. Curiosity is important here. Having an open mind matters. Be patient. The money will come. The opportunities will come. But they can speed up. When you are working in an industry, that’s growing so fast, and if you are learning fast, you are also growing fast. You can develop your career in the right direction. But movement can also be side-ways and not necessarily in the upward direction. This can be done by specializing in other areas.

The role of technology was best addressed in this episode by answering a question about the state of India in terms of technology adoption in the supply chain.  The advice to professionals was pithy. Be patient about what you do and do it best. Work for companies that you can connect to. 

Like helping countries transport goods during a pandemic is such a great purpose to be a part of, for a top executive in this company.

Incidentally, Gartner has augured that through 2023, less than 5 percent of control-tower-like deployments will fulfill their end-to-end potential, and this could be because of mindset and cultural obstacles. But we also know that there is a strong chance that through 2024, 50 percent of supply chain organizations will invest in applications that support Artificial Intelligence (AI) and Advanced Analytics capabilities.

There is no room for mindset barriers now. It is a good wind, and it is better to swim in the right direction with these new currents. So if you are working or aiming to work in the exciting logistics space, you know that this industry has become a lot more critical and lucrative after everything the world has seen in the pandemic. So be ready for a new future, with its new role, and yours too.

The post Impact of DeepTech on Supply Chain Revolution appeared first on TalentSprint.

TechWise is an 18-month program designed to prepare students from underrepresented groups in the US for high-growth tech careers. Google offers a 100% tuition scholarship and an additional $5000 scholarship that takes care of students’ basic expenses and ensures better focus on learning. 

In the first cohort launched in March 2022, the program received over 700 applications from 5 partner colleges, and only 126 students were selected, making it a 1:6 selection ratio. These students are undergoing 600 hours of immersive learning, mentoring by Google, and networking with peers passionate about tech. The success of the pilot cohort led to Cohort 2, which recently started with 8 partner colleges on board.

To celebrate the success of the pilot cohort, the TechWise Tour recently took place across three participating colleges in the US. The event was graced by distinguished dignitaries, including Congressman Ro Khanna, representative of California’s 17th Congressional District,  Shiv Venkataraman, VP/GM at Google, and Dr. Santanu Paul, MD and CEO of TalentSprint. The events have been held at the Community College of Allegheny County, Des Moines Area Community College in Iowa, and Benedict College in South California.

Congressman Ro Khanna, the representative of California’s 17th Congressional District, said: “What TechWise and Google and this partnership is really about is bringing this country together and saying that for us to succeed, for America to remain the manufacturing superpower, for America to remain the technology leader, ahead of any other country, we can’t write a single community off.” In a conversation with The Washington Post, he also said “The essence of the program is to get tech jobs to communities that haven’t had access to them before.” 

Shiv Venkataraman, VP/GM, Google stated that through TechWise, Google is creating not just more software engineers, but also promoting racial and gender equity by bringing technology to underserved communities. Dr. Santanu Paul, MD and CEO, TalentSprint was moved by the energy of the participants. He shared his vision for TechWise and emphasized the three pillars it was built on – learning to learn, learning by doing, and learning without the fear of failure.

Here are some testimonials that are a testament to the pilot cohort’s success and transformation:

TechWise offers a transformational learning experience through its live, instructor-led classes, hands-on experiential learning, peer collaboration, high-touch Google mentoring, among others. TechWise’s pedagogy of learning to learn, learning by doing, and learning without the fear of failure was greatly appreciated by the college partners during the tour. These prestigious partner colleges have started to recognize the significant value that the program pedagogy offers and, thus, are looking forward to integrating the same into their educational offerings. The curriculum also compliments the participants’ existing degree programs, thus, making them industry-ready. 

In addition, many community college dignitaries discussed the program’s shifting and long-lasting impact. The TechWise Tour’s real power lies in the participants’ transformational journeys. These stories are inspiring and powerful. 

TechWise is about creating a new narrative for the tech industry – that empowers all voices from minority backgrounds and celebrates the limitless potential of a genuinely inclusive tech world. It breaks down barriers and promotes diversity of thought and perspective by providing opportunities for underrepresented groups to succeed in the tech industry. The TechWise Tour has proven to be a rich platform for networking amongst global leaders and aspiring tech professionals. We look forward to seeing the continued success of the TechWise tour as we work towards a more inclusive future.

The post The Chronicles of the TechWise Roadshow appeared first on TalentSprint.

In the wise and penetrative words of Alan Perlis, “A year spent in artificial intelligence is enough to make one believe in God.” Let’s raise a toast to Alan and chat more about artificial intelligence (AI) in the same vein. AI looks like a miracle. And it’s a miracle created by humans – to a large extent. That’s why AI is becoming as powerful and mysterious as a Batman. It has so many shades, and it can fly to unimaginable heights. If we could look at some initial encounters, that should be enough to make us utter a big ‘Wow’.

AI- sliced by tasks

Now, while AI is getting faster and more profound in all kinds of tasks, what’s interesting to note is its swift maturity curve in expert systems. The adoption curve here is quite fascinating and explosive. 

As per the State of AI Report 2021, AI is stepping up in areas as complex as mission-critical infrastructure like national electric grids and automated supermarket warehousing optimization during pandemics. In addition, it is getting its roots deep into faster simulations of humans’ cellular machinery (proteins and RNA).

The McKinsey State of AI 2021 survey shows that AI adoption continues to grow and that the benefits remain significant— though, in the COVID-19 pandemic’s first year, they were felt more intensely on the cost-savings front than on the top line. Moreover, as AI’s use in business becomes more common, the tools and best practices to make the most out of AI have also become more sophisticated. Fifty-six percent of all respondents report AI adoption in at least one function, up from 50 percent in 2020. Notably, AI adoption increased most at companies headquartered in emerging economies, including China, the Middle East, and North Africa: 57 percent of respondents signified strong adoption, up from 45 percent in 2020. And across regions, the adoption rate is quite huge at Indian companies, followed closely by those in Asia–Pacific.

AI adoption is becoming common in areas like service operations, product and service development, and marketing and sales. Popular use cases are service-operations optimization, AI-based enhancement of products, and contact-center automation – do note that the most significant percentage-point jump has been in the use of AI in marketing-budget allocation and spending effectiveness.

AI- not so mundane anymore

AI is being used in service operations optimization (27 percent), contact-center automation (22 percent), AI-based product enhancements (22 percent), and product feature optimization (20 percent). In marketing areas, we see the use of AI in customer service analytics. It is also being used in customer segmentation (16 percent). In supply chain areas, too, AI is expanding its impact on logistics network optimization and sales-demand forecasting (11 percent). Not just that, AI is now being used confidently in risk modeling (16 percent) and fraud and debt analysis (14 percent). In factories, we see AI being deployed for predictive maintenance (12 percent) and for yield/throughput optimization (11 percent). AI is also becoming handy for top strategic tasks like capital allocation (seven percent) and treasury management (six percent). And all of this translates into bottom-line impact for sure. As much as 27 percent were seen reporting at least 5 percent of earnings before interest and taxes (EBIT) that’s attributable to AI. Many organizations also reported higher levels of cost cuts from AI adoption in the pandemic’s first year, while revenue spikes held steady.

It is evident that as we gain maturity and clarity on AI, we will see more and more expert tasks getting into the core adoption bucket of AI. This would, of course, need support on facets like skill-sets, human-in-the-loop expertise, and the adequacy of tools for AI.

Any enterprise that invests well in AI talent, AI models, and AI-ready data – is bound to unlock many more miracles of AI in the near future. So this superhero is growing brilliantly in various shades of tasks – as we move ahead.

AI is getting better and better at expert tasks. From a caveman to a Batman- and now it will need a good Alfred all the more. Let’s start getting those Bat-mobiles out.

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Humans need inanimate friends to help them get rid of labor, time, and cognitive effort that can be put in elsewhere or accentuated. Whether a farmer who uses a bullock or a Henry Ford who taps the assembly line- the quest for human augmentation is the same. We want someone to help us amplify the effort we put in. We want someone to help us focus on more important tasks – where creativity, cognitive input, and disruptive ideas matter more. So be it a cow, a piston, or a robot- they are all designed and nurtured for the same purpose- human augmentation. This journey has been accelerated with the onset of intelligent systems.

Let’s face it – nothing augments us as artificial intelligence (AI) does or is capable of doing. An intelligent system is everything packed into one wonder- the convenience of the fire, the wheel’s speed, the scale of the printing press, the loyalty of a farm dog, the precision of an assembly line- and much more.

Intelligent systems transform human lives beyond imagination. Because they are not just about velocity, volume, accuracy, exponential effort, intuitive action, or reliability. They are all of that- and a lot more.

1. What is machine intelligence? What are the types of machine intelligence?

Humans have always been able to tap the agility and horsepower of many other species and tools. But what is different with machine intelligence is that humans can now befriend something that reflects their brain. They have created intelligence beyond their bodies. And poured in smart models, programmable solutions, and clever algorithms. Making their lives easier, faster, deeper, and smarter.

Intelligence comprises many abilities and aspects – comprehension, visualization, processing, memory, reasoning, application, and learning. There are many kinds of intelligence. Here are some broad types: 

• Visual / Spatial intelligence
• Linguistic intelligence
• Logical or mathematical intelligence
• Kinesthetic intelligence (aware of body parts and good at physical coordination)
• Naturalistic intelligence (good with environmental areas)
• Intrapersonal intelligence
• Interpersonal intelligence
• Musical intelligence
• Existential intelligence (good at deep questions and philosophy)

When we look at intelligence from the lens of artificial intelligence (AI), there are two main categories- supervised and unsupervised. 

In machine learning, models and data help machines to learn. This learning, then, allows machines to support humans in their decisions and actions. When this learning happens without human supervision, it is called unsupervised machine learning. When it happens with humans in the loop, it is called supervised machine learning. And when the machine learns from feedback continuously and continuously improves itself- it uses reinforcement learning.

In supervised learning, both the input and output are defined, and the machine has to learn how to get from input to output. As a result, accuracy and control are high, but it takes too much time, labeling effort, and scalability issues.

In unsupervised learning, the machine must work out patterns and inferences on its own. Of course, this method can be more error-prone than supervised learning, but it is less labor-intensive.

In reinforcement learning, humans can get to insights that even they may not be aware of- provided they can allow machines some time and errors to get there.

Two percent of the respondents use supervised learning in a recent O’Reilly survey, and 67 percent use deep learning. 

There are many more divisions possible. For example, suppose a model uses previously-acquired knowledge. It is crystallized, but if it keeps changing and evolving into something better than before- able to apply abstract thinking for new or unfamiliar problems- it becomes fluid intelligence.

Cognitive computing is where sensors and algorithms help machines see and hear to get closer to some human capabilities. When a machine can make decisions based on data and models, this kind of intelligence is called artificial intelligence (AI). Here too, AI can be classified as Narrow (not too many human-like capabilities), General (Matching human capabilities), and Super (capabilities that are better than humans). In deep learning, a machine can mimic the human brain to some extent with its nervous topology and use of neural networks.

So depending on what an organization’s context and needs are, one can go for a specific type of AI model or machine intelligence alternative. For example, narrow and supervised learning would be a good answer for some applications. But for high-stake and high-value applications- one might need reinforcement intelligence and super-AI.

2. How does machine intelligence work?

Now that we know what machine intelligence is composed of let’s say we have decided on one particular course- let’s see if we can get a peek into what happens underneath the lid of these systems. But, of course, that would be wise to do before we unleash the beast in a given direction.

Inside any kind of machine intelligence, a whole new world of modeling and decision trees is brewing. The data and information that goes in or is built up on top of basic inputs get processed and translated in unique ways based on the design and purport of these intelligent machines. 

It is all about how algorithms are structured and run – that makes machines churn out so many decisions and insights- and so swiftly. The input and output parameters and the data fed into them in a supervised mode help a machine generate the answers required. In an unsupervised mode, the machine unlocks the patterns and interpretation from all the data fed into it. Then, these help the machine whip up all the answers and actions expected of it. In reinforcement learning, we tap the ability of machines to learn from even bad decisions and past actions. These lessons help them to churn out insights.

As intelligent human beings, we have never left our horses to go wild or our factories to run in the dark – so the same wisdom should apply again with AI. We need to be cognizant of what’s happening inside these intelligent machines. We must maintain visibility, context, responsibility, and far-sightedness while using these mind-blowing wonders. 

In the reckoning of Forrester analysts, AI systems making inexplicable decisions can be dangerous. As more and more companies adopt AI, business stakeholders will rely on AI for their workflows. Their trust cannot emerge without a general understanding of how they were made. What is also worth considering here is that AI obfuscates the ‘second-order insights’ like non-intuitive correlations that emerge from the inner workings of a machine-learning model. As advised by Forrester analysts, it would also become necessary for many stakeholders and regulators to understand how the entire model operates. They would need global explanations, while your customers may look for local explanations that clarify how the system made the decision that impacted them. 

Also, the quintessential ‘Black Box’ problem of AI is a real challenge when applying machine learning. Even if a machine is spinning out beautiful and thunder-fast answers – one has to know what is going on inside the box that is leading to these answers. It is significant to see the process of helping a machine learn anything. Otherwise, these insights can easily be prone to costly errors, bias, discrimination, false positives, and ethical problems. According to McKinsey’s ‘The State of AI in 2021’ report, 

• 57 percent cited cybersecurity as a relevant AI risk. Some companies also report personal and individual privacy as a relevant AI risk more often
• Explainability continues to be an important risk area – whether it is for emerging economies (34 percent) or developed ones (44 percent)
• With AI, these economies also found fairness and equity as significant risk factors (30 percent). What is notable here is that high performers put in effort in managing these risks
• Data professionals actively check for skewed or biased data during data ingestion (47 percent)
• Data professionals actively check for skewed or biased data at several stages of model development (36 percent)
• High performers have a dedicated governance committee that includes risk and legal professionals (23 percent).

3. Voice and language-driven intelligence

Now that we know what happens under the hood of these wonders, let’s spend a few minutes on one of these wonders. It is called voice recognition. And it is extraordinary because it cannot just hear us but listen to us – quite well. It is a genre of AI that can listen, translate, interpret and act in a real-time voice. It can transcribe conversations as they happen. It uses machine intelligence and Natural Language Processing (NLP) for instant speech recognition and contextual translation. It can also distinguish one voice or speaker from another.

In voice intelligence, the solution or model of AI being deployed for a specific case can listen to the voice, record data, process it, and apply it for insights or decisions in an intelligent way. This solution learns from the data it hears. This ability transpires into many solutions like conversational AI, chat-bots, NLP-powered algorithms, and smart assistants. They are permeating all aspects of our lives and are redefining many industries. Today, voice recognition is getting stronger and stronger every day- and is still evolving in many aspects. However, while 89 percent of users say that voice technology is easy to use, they look for better accuracy as the most desired improvement. And 57 percent of users opined that improvements in accuracy would cause them to use voice technology more often or for more purposes. The privacy and comfort parts of this technology also need to get better. This is apparent when 62 percent feel awkward using voice technology when other people are present. That reminds us that while technology embraces maturity and sophistication at a compelling velocity, much more needs to be considered as we augment human power.

Conclusion

Augmentation is a path that is still on an inception curve. As a result, we have barely scratched the surface of intelligent systems’ vast and boundless potential. 

Machine learning is exploding in its reach and depth. According to Fortune Business Insights, the global Machine Learning market was valued at $15.44 billion in 2021 and is expected to grow to $21.17 billion by 2022 and $209.91 billion by 2029. Today, AI manifests in many gains and revolutionary impacts – and multiple industries.

As per a recent PwC Global Artificial Intelligence study, there is a $15.7trillion potential contribution to the global economy by 2030 from AI. $6.6 trillion is likely to come from increased productivity, and $9.1 trillion is expected from consumption-side effects. Businesses expect a +26 percent boost in GDP for local economies from AI by 2030. As per this study, Labour productivity improvements would-be drivers of initial GDP gains as firms seek to “augment” their labor force’s productivity with AI technologies and automate some tasks and roles. About 45 percent of total economic gains by 2030 could be coming from product enhancements, stimulating consumer demand. This will translate into a lot of market explosion. Interestingly, worldwide revenues for the AI market, including software, hardware, and services, are forecast to grow 16.4 percent year over year in 2021 to $327.5 billion, as per some estimates from the International Data Corporation (IDC) Worldwide Semi-annual Artificial Intelligence Tracker. By 2024, the market could get past the $500 billion mark with a five-year compound annual growth rate (CAGR) of 17.5 percent and total revenues reaching an impressive $554.3 billion. 

Businesses are expecting a lot from AI. A new report published by information hub The AI Journal, titled ‘AI in a Post-COVID-19 World’, has revealed that 72 percent of leaders feel optimistic about the role that AI will play in the future, with the number one expectation being that it will make business processes more efficient (74 percent). About 55 percent suggest that AI will help create new business models, and 54 percent expect it to enable the creation of new products and services. What is notable is that 60 percent of respondents said their organization currently uses AI; 52 percent were planning and implementation. Machine learning is being used in many companies (70 percent), and 63 percent plan further integrations. 

There are still issues to be confronted – like a lack of understanding or commitment towards investing at the board level – feared by 59 percent of respondents. Other concerns are the legacy processes and technologies within businesses that do not support AI (50 percent) and the lack of relevant skills within the workforce (48 percent).

Yes, we cannot ignore the execution side and the challenges around the adoption of intelligent systems. In a recent O’Reilly survey on AI, it was spotted that the most significant barrier to AI adoption is the lack of skilled people and the difficulty of hiring. There are also challenges to the availability of quality data. Organizations struggle with a lack of skilled people and difficulty hiring (19 percent) and data quality (18 percent). There are huge skills gaps in areas like ML modelers and data scientists (52 percent), understanding business use cases (49 percent), and data engineering (42 percent). People are also dealing with Interpretability, model degradation over time, privacy, and fairness (50 percent), security as a concern (42 percent) – as seen in the O’Reilly survey.

AI-ready talent and skills – are critical precursors before we can start dreaming about reaching unprecedented business goals with machine learning and AI. According to Deloitte’s Tech trends 2021 report, MLOps can help AI teams promote trust by addressing data management challenges such as accountability and transparency, regulation and compliance, and ethics. Also, multi-talented teams of technologists and machine learning professionals can help organizations operationalize and scale AI. To IDC’s estimates, 28 percent of AI/machine learning projects fail due to a lack of necessary expertise, production-ready data, and integrated development environments. In addition, about 47 percent fail to make it out of the experimental phase and into production. 

The question, now, is – how do we keep these intelligent systems as much in control as we want. The worry now is- can we tame them the way we domesticated fire, horses, light bulbs, engines, and even space-crafts? The challenge now is- can we squeeze the most out of them without losing anything from our side? Do we have the right knowledge buttons and skills to drive these augmentative forces? That’s the real test of human augmentation. And the ultimate sign of intelligence. Human intelligence.

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What would it be like to walk in a museum that curates not our past but our future? Hard to say, but one thing is for sure. We would see fascinating copies of our brains sitting cheerfully in some corridor here. 

And commanding a spotlight in this corridor would be the fascinating glimpse of neural networks, too – I can bet. This is why I am so excited and upbeat about the changes that these networks will bring to our work and lives – and very soon.

Neural networks – reflecting the behavior of the human brain

In simple words, these networks run the algorithms that form the core of any AI model. They are loosely constructed on the brain’s map of human biological networks. They work by drawing references from the collection of units or nodes called neurons – which model the neurons in the brain. They use this to form a hardware and software system shaped on these highly interconnected processing elements (neurons). Further, an artificial neural network (ANN) can evolve from the fundamental – and it can be used to comprehend the relationship between datasets to generate the desired output. To arrive at that, the system simulates a human brain via some deep learning technologies to solve complex pattern recognition or signal processing problems. Here are some applications of neural networks:

• Weather prediction
• Handwriting recognition
• Fraud detection
• Risk analysis
• Oil-exploration data analysis
• Facial recognition
• Speech-to-text transcription
• Banking- credit and loan application evaluation
• Predictive analytics for loan delinquencies
• Transport- power routing systems, truck brake diagnosis systems, and vehicle scheduling
• Healthcare- cancer cell analysis, advanced diagnostics, and design
• Customer support 

The rise and rise of neural networks

We can notice that as AI models and applications explode in growth, there is a corresponding surge in the use of neural networks. In the estimates of Allied Market Research, the global neural network market stood at $14.35 billion in 2020 and can climb to $152.61 billion by 2030. Furthermore, as per MarketsandMarkets, the global artificial neural network market can show a spurt from $117 million in 2019 to $296 million by 2024. 

Humans, please enter again

As a 2021 Juniper report pointed out – skills remain a crucial challenge, especially when organizations are struggling with expanding their workforce to integrate with AI systems.

Leveraging the vast potential of AI models based on neural networks would need an intelligent human brain’s creativity and application orientation. Enterprises cannot create algorithms out of thin air. They would have to tap the programming, and design, capabilities of a talented specialist here. They would also need the right quality and data management to make these models churn out the insights that we expect of them.

Anyone who wishes to chart a strong trajectory in the expanding field of AI would be clever to start this journey with areas like neural networks. However, demand is high, and capacity is low – so early runners in this playground would always fetch an edge – by getting lucrative work opportunities and exciting chances to create/run new models.

It would be like playing with the human brain – in a new way. Who could have thought that our brains would be such bundles of creativity when we can play with them as toys!

Anything is possible in this museum of the future. What a time to be alive! What a time to live with AI just around the corner!

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Voice sptodayeaks, and loud

All of this has its roots in voice and language-driven intelligence. It is a genre of artificial intelligence (AI) that can listen, translate, interpret and act in real-time voice. It can transcribe conversations as they happen. It uses machine intelligence and natural language processing (NLP) for instant speech recognition and contextual translation. It can also distinguish one voice or speaker from another.

In voice intelligence, the solution or model of artificial intelligence (AI) being deployed for a specific case can listen to the voice, record data, process it, and apply it for insights or decisions in an intelligent way. This solution learns from the data it hears. This ability transpires into many solutions like conversational AI, chatbots, NLP-powered algorithms, and intelligent assistants. As a result, they permeate all aspects of our lives and redefine many industries. In fact, as per a report by Market Research Future (MRFR), the market valuation of this space stood at $1.68 Billion in 2019 and is projected to reach $7.30 Billion by 2025. Furthermore, in a PwC-FICCI report, as many as 83 percent cited customer experience enhancement as the reason for AI implementation- followed by improving productivity (57 percent) and increasing revenue (56 percent) as the other main goals for adopting AI. And 80 percent of respondents from the banking and financial services industry have deployed chatbots to make customer service easy. Also, 65 percent of them have deployed fraud detection AI engines.

In an Adobe 2020 survey of 1000 voice technology users, one in three voice users (31 percent) counted sanitation, like not needing to touch high-traffic surfaces, as a benefit of using voice technology. Not surprisingly, 18 percent of users use voice technology for health or fitness applications and 86 percent of users say that voice technology could make visiting businesses or attending events more sanitary. Predominantly, people use this technology like apps for maps or driving (52 percent), texting or chat (51 percent), and music (46 percent). About 37 percent would be open to using it for checking their bank balance, 29 percent to book a medical appointment, and 28 percent for grocery delivery.

There is no end to the possibilities that can be unlocked with voice recognition. Some typical applications of NLP and voice recognition are:

1. Keyword spotting in social media 
2. Audio marketing
3. Sales assistance in precise prospecting
4. Automation of many time-intensive tasks
5. Product improvement
6. Many other marketing scenarios based on voice data
7. Live transcription in litigation and other documentation-heavy areas
8. Compliance monitoring
9. Automotive V2V solutions
10.  Robotic surgeries
11.  Car navigation
12.  Smart contact centres
13.  Intelligent banking
14.  Fraud detection
15.  Customer support and call-center optimization
16.  Robotic process automation (RPA)
17.  Document digitization
18.  Data preservation
19.  Digital assistants for CXOs
20.  Virtual assistants 

As an industry-ready technology, voice recognition is getting stronger and stronger every day- and is still evolving in many aspects. The Adobe survey signified that voice technology would better meet user needs as its design continues to develop – as predicted by 49 percent of users. However, while 89 percent of users say that voice technology is easy to use, they look for better accuracy as the most desired improvement. And 57 percent of users opined that improvements in accuracy would cause them to use voice technology more often or for more purposes. In addition, the privacy and comfort parts of this technology also need to get better. This is apparent when 62 percent feel awkward using voice technology when other people are present.

It will take some time before we sharpen the magic of voice intelligence. Soon it would get so sophisticated and easy that it would be like using the good old genie in the bottle. After all, that genie was also a fairy-tale version of a voice assistant. It worked well because it could identify, understand and act upon a specific voice. Today’s AI-based genies are going a step further. They are constantly hearing and learning from their voice masters. So we need to keep thinking of smart wishes to ask of them. Thankfully, there is no limit to the number of wishes. They just need to be worthy enough of these new genies. Can you think of what to ask next?

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The difference lies in just one word, ‘intelligence’, to the best of our knowledge – accumulated and tested over centuries by scientists and explorers. 

The level and evolution of intelligence that humans have mastered are genuinely distinct. Our ability to comprehend something, think about it, learn something, process information, apply experience, make reasonable decisions, act on a situation, and improve our ability to do it all should explain the term ‘intelligence’. And also highlight its relevance in our work and life – on a typical day.

So, what is intelligence? What are the types of intelligence?

Intelligence is composed of many abilities and aspects – comprehension, visualization, processing, memory, reasoning, application, and learning. And this word comes in a variety of shades. There are many kinds of intelligence. Without digging too deep, we can categorize ‘intelligence’ into some broad types: 

• Visual / Spatial intelligence
• Linguistic intelligence
• Logical or mathematical intelligence
• Kinesthetic intelligence (aware of body parts and good at physical coordination)
• Naturalistic intelligence (good with environmental areas)
• Intrapersonal intelligence
• Interpersonal intelligence
• Musical intelligence
• Existential intelligence (good at deep questions and philosophy)

Most of these are self-explanatory. What is fascinating to note here is every person has a different area of strength when we look at all these types of intelligence. For example, someone may be good at noticing nuances of tone – and hence, have musical intelligence. On the other hand, someone may be good at the ability to grow anything and explore nature – thus, born with naturalistic intelligence). 

Also, intelligence can be crystallized or fluid. If it uses previously-acquired knowledge, it is crystallized. Still, if it keeps changing and evolving into something better than before- able to apply abstract thinking for new or unfamiliar problems- it becomes fluid intelligence.

With that backdrop, let’s try to understand the new species humans have created themselves- Machines. And let’s get to know about the intelligence they have and build- machine Intelligence.

Zooming in on machine intelligence

Now let’s look at some significant types of the computer genre of intelligence. 

Cognitive computing is where sensors and algorithms help machines see and hear to get closer to some human capabilities. When a machine can make decisions based on data and models, this kind of intelligence is called artificial intelligence (AI). Artificial intelligence (AI) can be classified as,

• Narrow (not too many human-like capabilities), 
• General (matching human capabilities), and 
• Super (capabilities that are better than humans)

In machine learning, models and data help machines to learn. This learning, then, allows machines to support humans in their decisions and actions. When this learning happens without human supervision, it is called unsupervised machine learning.

• When it happens with humans in the loop, it is called supervised machine learning. 
• And when the machine learns from feedback continuously and continuously improves itself- it uses reinforcement learning.

In supervised learning, both the input and output are defined, and the machine has to learn how to get from input to output. Here, accuracy and control get high, but it takes too much time, labeling effort, and scalability issues. In unsupervised learning, the machine must work out patterns and inferences on its own. Of course, this method can be more error-prone than supervised learning, but it is less labor-intensive.

In reinforcement learning, humans can get to insights that even they may not be aware of- provided they can allow machines some time and errors to get there.

Finally, in deep learning, a machine can mimic the human brain to some extent with its nervous topology and use of neural networks.

Conclusion

Machine learning is exploding in its reach and depth. According to Fortune Business Insights, the global machine learning market was valued at $15.44 billion in 2021 and is expected to grow to $21.17 billion by 2022 and $209.91 billion by 2029. The global AI market size was valued by Grand View Research – at $62.35 billion in 2020 and is expected to expand at a compound annual growth rate (CAGR) of 40.2 percent between 2021 and 2028. With this, you will witness many ML applications in automotive, healthcare, retail, finance, and manufacturing. 

Many AI applications are helping industries reach new levels of efficiency and revenues. We are getting closer to a world where self-driving vehicles, robot-assisted surgery, risk assessment, investment management, etc., would be everyday reality. And who knows, one day, the thing that separates humans from other species would be these machines that humans have—another proof of constant and smart human evolution.

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