Tag: ML

2022 Rewind: Tech that made a big splash this year

2022 was surely a monumental year in the world of tech. While the tech industry like others is still recovering from the grappling hooks of the pandemic, it did manage to make major headlines besides the negative ones. As the year comes to a close, we wanted to look back at all the technological advancements and trends that made it mainstream in 2022. So without any further ado, let’s jump right in –

Artificial Intelligence (AI) and Machine Learning (ML) 

It was only in November that we dedicated an entire edition to the emerging technology of artificial intelligence with our ‘AI Special’ The technology took a front foot in 2022 and is expected to grow at a compound annual growth rate of over 20% till 2029. There were many ways AI took over the world with its most prominent example being the healthcare industry during the times of COVID. AI’s demand in the healthcare space increased exponentially due to its models and tools capable of improving healthcare analytics and prediction tools. On the other hand, big companies are now using a big chunk of their commuting power to provide enhanced AI capabilities to the changing world. 

As per Forbes, the biggest trend in the world of AI in 2022 was ‘machine vision’ where computers can now see and recognize objects in a video or photograph with much ease. Expect your ‘Are You a Human’ tests on the internet to get a little bit difficult as machines are now catching up quickly! Language processing was another big advancement in the field of AI where machines can now understand our voice, lingo, and accents much better and speak back to us. Another big trend in the field of AI was ‘AI Art’ which quickly became a rage on the internet and sparked a conversation amongst the artist community. Whether AI Art can be considered genuine art is a debate still ongoing. 

Cyber Security 

The world is becoming increasingly digital and with that cybersecurity will only become more and more prominent. Businesses and individuals both need to protect themselves from major cyber attacks. If the software on your mobile or computer device is compromised it could lead to great catastrophes like the leak of personal data and banking details. As of 2022, most of my data is stored on a cloud drive similar to a lot of people today. We like to have the convenience of being able to store data on the cloud and not on physical drives to preserve storage but it’s a double-edged sword as cloud drives could get struck by a cyber-attack anytime. 2022 saw an increase in public awareness about the topic of cyber security with an increased number of people using VPNs and other services to ensure their data is secured on the internet. Pro tip – Do use a VPN while surfing on the internet as you never know which site leads to a cyber attack! 

Virtual Reality (VR) and Augmented Reality (AR)

2022 saw a surge in interest in VR and AR technologies thanks to the metaverse boom.  Meta’s Mark Zuckerberg has kept his stand solid on the metaverse being the next big thing in the world of tech even though current numbers paint a different picture. This year, we saw the launch of the Meta Quest Pro which is priced at a whopping $1,499. However, the VR headset has introduced the concept of mixed reality which looks like the perfect way ahead in the world of VR. Thanks to mixed reality technology, users can experience high-resolution engagement with the virtual world while maintaining a presence in the real world in high definition. A truly immersive and remake upgrade to the experience of VR was brought upon by Meta which can only be expected to be the gold standard of VR/AR in the near future. Do you expect to attend your work meetings via a VR headset as early as 2023? It doesn’t look like a stretch thanks to the advancements in VR technology. 

5G Connectivity 

2022 was the year India stepped into the 5G game with all guns blazing. Though it was in the last quarter of 2022, I am glad now I can surf the internet at lightning speeds on a cellular network thanks to 5G making its way to India. The fifth-generation wireless is currently the most advanced mobile broadband technology that already has giants like Samsung, Apple, Qualcomm, and other mobile network providers working on their toes to deploy the technology at a faster rate. It is reported that approximately 4 billion 5G subscriptions will be active around the world by 2025. 5G has the potential to change the way people perceive cellular networks and turn to them for their primary data needs. While 5G is still finding its footing in a country like India, 6G is already on the horizon which directly translates to more power in our mobile devices, cars, and wearables! 

Quantum Computing

No, we certainly are not at the stage where we can travel through the ‘Quantum Realm’ and make big changes to the fate of the universe. That is only reserved for the fascinating world of Marvel films. However, the trend of quantum computing surely took over the world in 2022. So what actually is quantum computing? In simple terms, it is the processing of information that is represented by special quantum states that enable machines to handle data in a much different way from the traditional ways. Quantum computing has the potential to give humanity computing power that is a trillion times faster than what we’ve got today’s advanced supercomputers. Having such fast computing speeds allows humanity to unlock a different era of technological advancement. It could change the way we approach aspects like drug innovation, space exploration, and much more.

Depths of ML – What is Machine Learning?

There is no debate about the fact that humans and computers are different entities. One of the main differentiating factors between humans and computers is that the former is capable of learning from past experiences. Well, to some extent. At the same time, computers need to be told what to do specifically. Computers are code-based strictly logic machines that do not possess common sense. This means that if we want them to do something, we have to tell them what to do precisely. This is done by providing them with step-by-step instructions on what to do exactly. Humans write scripts and program computers to follow instructions. This is where Machine Learning comes in! In simple terms, Machine Learning (ML) is a concept that consists of teaching computers to learn from experiences beyond data. 

What is Machine Learning? 

Machine Learning (ML) is a form of Artificial Intelligence (AI) that allows the software to predict more accurate outcomes without being programmed to do so exclusively. ML algorithms draw from historical data as input in order to predict new output values. Some of the ways ML is used are through recommendation engines, fraud detection, spam filtering, malware threat detection, and much more. So, what’s the big deal? Why is ML being used around as a trendy keyword in the world of AI? 

ML is important as it allows enterprises to observe the changing trends in customer behavior. Business operational patterns can also be observed through ML, whereas the technology also helps in the development of new products. Tech giants around the world like Google, Facebook, Uber, and many others use ML as a central part of their operations. Similar to AI, ML also has different categories. While classical ML is usually classified by how an algorithm learns to output accurate predictions, there are four different approaches to how it is done. The approaches to ML are listed below – 

Supervised Learning

In this type of ML, data scientists supply algorithms with specifically labeled training data. The variables of the data here are defined to the minutest details and both the input and output of the algorithm are specified. Supervised learning algorithms are good at binary classification, multi-class classification, regression modeling, and ensembling. 

Unsupervised Learning

Unsupervised ML algorithms do not require the data to be labeled. Most types of deep learning used are unsupervised algorithms. These algorithms discover hidden patterns and groupings without the need for human input. Due to its ability to discover similarities and differences in information, unsupervised learning is the best solution for customer segmentation, image recognition, exploratory data analysis, and more. 

Semi-supervised learning

As one would expect, this is the middle ground between Supervised and unsupervised learning. While training this type of algorithm, data scientists use smaller labeled data sets to guide classification. A small amount of labeled training data is fed to an algorithm which allows it to learn the dimensions of the data set. 

Reinforcement learning

This type of learning is used to teach a machine to complete a multi-step process for which the rules are clearly defined. An algorithm is programmed with a distinct goal and a prescribed set of rules to accomplish that goal. One of the main implementations of reinforcement learning is robotics. Robots can learn to perform physical tasks with the help of reinforcement learning. Whereas, reinforcement learning can also be used to teach bots to play a number of different video games. Resource management is another way where RL can be used as finite resources and a defined goal can allow enterprises to plan how to allocate resources. 

There are a lot of ways where machine learning is being used in a wide range of applications today. One of the best examples here is your Facebook news feed. The news feed uses ML to personalize every member’s feed. If you as a user frequently go on Kim Kardashian’s Facebook page then your News Feed is likely to show you more of her activity on the feed. We often start seeing advertisements for a certain product right after we search for it on Google or Amazon, that is due to the machine learning algorithm working in the background. Behind the scenes, the software is simply using statistical analysis and predictive analysis in order to identify patterns in your user data and use the same data to populate your news feed.

Depths of ML – What is Machine Learning?

There is no debate about the fact that humans and computers are different entities. One of the main differentiating factors between humans and computers is that the former is capable of learning from past experiences. Well, to some extent. At the same time, computers need to be told what to do specifically. Computers are code-based strictly logic machines that do not possess common sense. This means that if we want them to do something, we have to tell them what to do precisely. This is done by providing them with step-by-step instructions on what to do exactly. Humans write scripts and program computers to follow instructions. This is where Machine Learning comes in! In simple terms, Machine Learning (ML) is a concept that consists of teaching computers to learn from experiences beyond data. 

What is Machine Learning? 

Machine Learning (ML) is a form of Artificial Intelligence (AI) that allows the software to predict more accurate outcomes without being programmed to do so exclusively. ML algorithms draw from historical data as input in order to predict new output values. Some of the ways ML is used are through recommendation engines, fraud detection, spam filtering, malware threat detection, and much more. So, what’s the big deal? Why is ML being used around as a trendy keyword in the world of AI? 

ML is important as it allows enterprises to observe the changing trends in customer behavior. Business operational patterns can also be observed through ML, whereas the technology also helps in the development of new products. Tech giants around the world like Google, Facebook, Uber, and many others use ML as a central part of their operations. Similar to AI, ML also has different categories. While classical ML is usually classified by how an algorithm learns to output accurate predictions, there are four different approaches to how it is done. The approaches to ML are listed below – 

Supervised Learning 

In this type of ML, data scientists supply algorithms with specifically labeled training data. The variables of the data here are defined to the minutest details and both the input and output of the algorithm are specified. Supervised learning algorithms are good at binary classification, multi-class classification, regression modeling, and ensembling. 

Unsupervised Learning

Unsupervised ML algorithms do not require the data to be labeled. Most types of deep learning used are unsupervised algorithms. These algorithms discover hidden patterns and groupings without the need for human input. Due to its ability to discover similarities and differences in information, unsupervised learning is the best solution for customer segmentation, image recognition, exploratory data analysis, and more. 

Semi-supervised learning

As one would expect, this is the middle ground between Supervised and unsupervised learning. While training this type of algorithm, data scientists use smaller labeled data sets to guide classification. A small amount of labeled training data is fed to an algorithm which allows it to learn the dimensions of the data set. 

Reinforcement learning

This type of learning is used to teach a machine to complete a multi-step process for which the rules are clearly defined. An algorithm is programmed with a distinct goal and a prescribed set of rules to accomplish that goal. One of the main implementations of reinforcement learning is robotics. Robots can learn to perform physical tasks with the help of reinforcement learning. Whereas, reinforcement learning can also be used to teach bots to play a number of different video games. Resource management is another way where RL can be used as finite resources and a defined goal can allow enterprises to plan how to allocate resources. 

There are a lot of ways where machine learning is being used in a wide range of applications today. One of the best examples here is your Facebook news feed. The news feed uses ML to personalize every member’s feed. If you as a user frequently go on Kim Kardashian’s Facebook page then your News Feed is likely to show you more of her activity on the feed. We often start seeing advertisements for a certain product right after we search for it on Google or Amazon, that is due to the machine learning algorithm working in the background. Behind the scenes, the software is simply using statistical analysis and predictive analysis in order to identify patterns in your user data and use the same data to populate your news feed.

AI in the Fight Against COVID-19

2020 has been a disaster. The first quarter of the year is almost over and people have no other option but to sit inside their homes, while a China originated human-killing virus wipes a considerable amount of the world population. While ‘Thanos’ was an imaginary being in the Marvel Universe, his aim was no different. He wanted to relieve the world of half its population and restore balance. The COVID-19 is no different situation apart from the fact that killing innocent humans is not going to restore the balance on earth, not after it has been subjected to all the malicious atrocities that it has been subjected since the arrival of mankind.

The virus which is also known as coronavirus is said to be originated in the city of Wuhan, China and suspected to be contracted from animals to humans. As people kept mingling and travelling all over the world, the virus too was spread. The current situation is so bad that most advanced countries like the USA and healthcare dominant countries like Italy are on the verge of giving up. All the global leaders, dignitaries and citizens of most of the infected countries are just asking the same question. When will this end?

In the fight against coronavirus, artificial intelligence (AI) and Machine learning (ML) have all of a sudden great role to play. While doctors, physicians, pharma companies and scientists are directly engaged in this duel of humanity against the virus, AI and ML are now being used as human allies for the answer to we all looking to hear. 

How will AI and ML fight the disease?

For most of us, AI and ML are just boxed in computers. Apart from seeing AI and ML suggest products from our last google search, none of us has actually seen AI work in real-time. What most people would know AI as just a bunch of codes which work behind the scenes to make things easier in front of the screen. However, if you think so, you are wrong. You can not physically see AI and ML do its job, but AI and ML are contributing to this fight, a whole lot more than you can imagine. 

AI and ML can effectively track the disease spread and the trends which points out where the medical help is most needed and where the virus is most likely to spread. This helps medical professionals to quickly act and helps to curb the spread of the disease. AI or as we know artificial intelligence is beyond normal human learning and use its past learning patterns to quickly identify where the things could go wrong. Based on its learning and combining its previous knowledge with insights, it can predict where the disease could go next based on the population flow and density. 

Artificial Intelligence can also match the symptoms of COVID-19 and the required treatment or any other therapy that is required. AI is also streamlining the best treatments for the disease which we still await an effective vaccine. Not only this, but AI and ML are also being used to analyse millions of drugs, elements and formulas with high speed to develop a vaccine. AI might seem to be working at its full speed, but a vaccine is still a year or more away from being developed. 

Now coming to the other side, AI and ML must also contribute to risk management. AI is as good as 95% correct most of the times. With more and more learning, AI continuously improves and thus corrects itself. However, it is quite dicey at this point to depend on AI alone, especially when the data samples (available data on COVID-19) is very less. Will AI still be that effective when it comes to less available data and also predict the failures? This is still to be understood at large unless there is a huge amount of data available for artificial intelligence to learn from. 

For any organisation or company using AI and ML as a tool in the current crisis, it is clear that AI and ML are no more proprietary or experimental technology anymore. The current situation has help demonstrate AI and ML have far more usage and applications, especially when human knowledge and efforts seem to have seen a dead end.

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