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Banking has existed in some form for millennia. And for many centuries, the core principles and activities of the financial industry essentially remained the same, even as the scale of global financial activities grew exponentially. 

For companies and leaders, this shift has opened up some big questions. What does the future of fintech hold for organizations inside and outside the financial services industry? And what can companies do now to prepare their workforces? 

What Leaders Need to Know About the Future of Fintech

As a refresher, fintech broadly refers to the application of emerging technology to the financial services industry, generally to streamline and improve the delivery of core services. Today, fintech startups and legacy companies that embrace emerging technology are challenging existing financial services business models. How? By disrupting intermediaries, reducing fees, offering an improved customer experience, and democratizing financial services products. 

The promise of fintech has made it the leading sector for venture investment in recent years—even amid the challenging financial landscape of 2023. But fintech isn’t just for startups. It’s relevant to companies of all sizes in banking, investing, real estate, insurance, risk management, regulation, and other fields in the financial industry. 

The venture firm Coatue predicts the fintech market cap will grow by 50% in the next three years, with nearly unlimited potential for companies that can tap into this market while maintaining sound business practices. Legacy companies that don’t adapt, on the other hand, face a serious risk of disruption. To stay ahead in this fast-changing landscape, corporations and their employees need to develop the new skills and capabilities required to adapt to—and innovate—new financial technologies. 

Fintech Focus Areas in 2023

Microfinancing and crowdfunding

: Providing financial services to those who have traditionally lacked access to tools such as banking and lending. 

Payments and remittances:

Offering new tools for making payments and transferring money, including across currencies, with increased speed and decreased costs. 

Cryptocurrencies, blockchain, and other distributed ledger technologies:

Creating decentralized, highly secure platforms for storing data and conducting transactions and offering alternatives to traditional fiat currencies. 

Fintech startups have emerged in each of these areas to challenge legacy companies with new products and services that address unmet needs in both the B2B and consumer spaces.

Top Capabilities Needed for Fintech Success

To make the most of the opportunities the fintech revolution offers, major corporations and scrappy startups alike need teams equipped with not only broad industry knowledge but also emerging technical skills. 

The top three capabilities companies need to develop to prepare for the future of fintech are:

1. Artificial Intelligence and Machine Learning

Fraud detection and prevention 

Regulatory compliance

Credit risk assessment

Institutional trading 

Customer service

2. Data Analytics 


Customer service

Product development and refinement

Risk management

Investment decision-making

Regulatory compliance

Data analysts in the fintech industry need a strong grounding in core data science principles to succeed, as well as specific knowledge of the specific challenges and opportunities that high-frequency fintech data presents. 

3. Blockchain and Distributed Ledger Technology

Despite recent setbacks in the cryptocurrency space, blockchain and distributed ledger technology remain one of the most promising areas of financial innovation. The top use cases for blockchain in financial services include:

Smart contracts

Fraud prevention

Decentralized payments

Tokenization of assets


To make smart investments in this space, companies need teams that understand the complexities of blockchain systems and can evaluate their application to various financial products and services. They also need to understand the complex risks associated with decentralized finance. 

In addition to these core capabilities, companies also need employees with knowledge and skills in a variety of related areas, including embedded finance, global fintech regulation, open banking, banking-as-a-service, strategic finance, and financial governance.

How to Build Teams for the Future of Fintech

Globally, fintech talent is in short supply. Companies face increasing challenges in recruiting and retaining qualified employees for key roles—and even highly-qualified talent may lack important context and knowledge specific to the intersection of finance and technology. 

Berkeley Fintech: Frameworks, Applications, and Strategies, a University of California, Berkeley Executive Education course delivered in partnership with Emeritus, prepares teams to make the most of opportunities in the fintech space—and to meet emerging challenges head-on. 

This course, designed for teams in banking, investing, real estate, insurance, risk management, regulation, and other fields in the financial industry, as well as fintech startups, dives deep into both the financial and technological considerations to offer a holistic view of the future of fintech.

This two-month online course pairs theoretical knowledge with real-world applications and is structured around three primary outcomes. 

Section One: Building Foundational Knowledge and Understanding ‘Why Now?’

The first section of the program provides teams with a comprehensive overview of the fintech revolution history to date, the broader fintech landscape and the economic foundations of fintech. It ensures teams have a shared understanding of the foundations of fintech and the overall opportunity, answering the question of “why now”?

Section Two: Developing Technical Knowledge 

The program provides an in-depth overview of the skills and core capabilities that are driving the fintech revolution: AI/ML, data science, blockchain/distributed ledger technology, financial literacy, and a deep understanding of the ecosystem. Teams will learn the core principles of each area, understand how the technology is applied, and identify opportunities to use new tools to add value. Employees in both technical and non-technical roles will understand where the competition is headed and how to stay one step ahead. 

Section Three: Real-World Application

The final section of the program applies fintech theory and principles to real-life business challenges. Using case studies from both startup disruptors like Venmo and legacy companies like Fidelity, participants explore applications of fintech business models and learn from notable Silicon Valley VCs and others (e.g., a World’s Private Equity leader) about what makes a great investment. Finally, the focus is on applying new frameworks to assess a fintech valuation as well as identify fintech ideas or investment opportunities for your organization.

Learn more about group enrollment options in the Berkeley FinTech course to ensure your team is prepared for the future of fintech.

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The Future Of Machine Learning: Automl

Do you ever wonder how companies develop and train machine learning models without experts? Well, the secret is in the field of Automated Machine Learning (AutoML). AutoML simplifies the process of building and tuning machine learning models for organizations to harness the power of these technologies. Figure 1 gives a visual AutoML. In this blog, we’ll explore a look at some of its key benefits and limitations. Get ready to be amazed by the power of AutoML.

Learning Objectives

Understand the basics of AutoML and its methods

Explore the key benefits of using AutoML

Understand the limitations of AutoML

Understand the practical impact of AutoML

This article was published as a part of the Data Science Blogathon.

Table of Contents

What is AutoML?

Methods of AutoML: A Comprehensive Overview

Effortless ML: The Merits of AutoML

AutoML: A Closer Look at the Drawbacks

AutoML in Practice: How Companies are Automating Machine Learning?


What is AutoML? The Future of Machine Learning

AutoML is a game-changer in the field of machine learning. It is a training of machine learning models to automate the process of selecting and tuning algorithms. This includes everything from data preprocessing to selecting the most suitable model for the given task. AutoML tools handle hyperparameter tuning and model selection tasks, which typically require time and expertise. With AutoML, users without experience in machine learning can train high-performing models with minimal effort. Whether you’re a small business owner, a researcher, or a data scientist, AutoML helps to achieve your goals with less time and effort. Examples of popular AutoML platforms include Google Cloud AutoML, chúng tôi and DataRobot.

AutoML provides explainable AI to improve the interpretability of the model. This allows data scientists to understand how the model makes predictions, which is particularly helpful in healthcare, finance, and autonomous systems. This can be used to identify bias in data and prevent wrong predictions. For example, AutoML can be used in healthcare fo gnosis by analyzing medical images, in finance for fraud detection, in retail for product recommendations, and in transportation for self-driving cars. Figure 2 shows the AutoML process.

ethods: A Comprehensive Overview

AutoML automates the use of machine learning for real-world problems. This includes tasks such as algorithm selection, hyperparameter optimization, and f rent methods are being developed to tackle the various aspects of the problem. Some popular approaches are given below

Neural Architecture Search (NAS):

This method uses a search algorithm to automatically find the best neural network architecture for a given task and dataset.

Bayesian Optimization: This method uses a probabilistic model to guide the search for the best set of hyperparameters for a given model and dataset.

Evolutionary Algorithms: This method uses evolutionary algorithms such as genetic algorithms or particle swarm optimization to search for the best set of model hyperparameters.

Gradient-based methods: This method uses gradient-based optimization techniques like gradient descent, Adam, etc., to optimize the model hyperparameters.

Transfer Learning: This method uses a pre-trained model on a similar task or dataset as a starting point and then fine-tunes it on the target task and dataset.

Ensemble methods: This method combines multiple models to create a more robust and accurate final model.

Multi-modal methods: This method uses multiple data modalities such as image, text, and audio to train models and improve performance.

Meta-learning: This method uses a model to learn how to learn from data, which can improve the efficiency of the model selection process.

One-shot or few-shot learning: This method can learn to recognize new classes from only one or a few examples.

AutoML is broadly classified into a model selection and hyperparameter tuning, as shown in Fig 3. Many differen integrated into existing workflows.

Effortless Machine Learning: The Merits of AutoML in Machine Learning

AutoML simplifies the machine learning process and brings many benefits, some of which are given below:

Time-saving: Automating the process of model selection and hyperparameter tuning can save a significant amount of time for data scientists and machine learning engineers.

Accessibility: AutoML allows users with little or no experience with machine learning to train high-performing models.

Improved performance: AutoML methods can often find better model architectures and hyperparameter settings than manual methods, resulting in improved model performance.

Handling large amounts of data: AutoML can handle large amounts of data and find the best model even with more features.

Scalability: AutoML can scale to large datasets and complex models, making it well-suited to big data and high-performance computing environments.

Versatility: AutoML can be used in various industries and applications, including healthcare, finance, retail, and transportation.

Cost-effective: AutoML can save resources and money in the long run by reducing the need for manual labor and expertise.

Reduced risk of human error: Automating the model selection and hyperparameter tuning process can reduce the risk of human error and improve the reproducibility of results.

Increased Efficiency: AutoML can be integrated with other tools and processes to increase efficiency in the data pipeline.

Handling multiple data modalities: AutoML can handle multiple data modalities such as image, text, and audio to train models and improve performance.

AutoML offers several benefits for data scientists and engineers that save time and resources by automating tedious and time-consuming tasks. This also improves the interpretability of the model by providing explainable AI. These combined benefits make AutoML a valuable tool in many industries and applications.

AutoML: A the Drawbacks

AutoML has become a popular tool for data scientists and analysts. However, it has limitations. There are following limitations are given below

Limited control over the model selection and hyperparameter tuning process: AutoML methods operate based on predefined algorithms and settings, and users may have limited control over the final model.

Limited interpretability of the resulting model: AutoML methods can be opaque, making it difficult to understand how the model makes its predictions.

Higher costs than manually designing and training a model: AutoML tools and infrastructure can be costly to implement and maintain.

Difficulty in incorporating domain-specific knowledge into the model: AutoML relies on data and pre-defined algorithms, which can be less effective when incorporating domain-specific knowledge.

Potential for poor performance on edge cases or unusual data distributions: AutoML methods may not perform well on data that is significantly different from the training data.

Limited support for certain models or tasks: AutoML methods may not be well-suited to all models or tasks.

Dependence on large amounts of labeled data: AutoML methods typically require large amounts of labeled data to train models effectively.

Limited ability to handle data with missing values or errors: AutoML methods may not perform well on data with missing values or errors.

Limited ability to explain the model’s predictions and decisions: AutoML methods can be opaque, making it difficult to understand how the model makes its predictions, which can be an issue for certain applications and industries.

Overfitting: AutoML methods may lead to overfitting on the training data if not properly monitored, which can result in poor performance on new unseen data.

AutoML is a powerful tool for automating the machine-learning process, but it is with its limitations. It is important to consider these limitations in the presence of expert supervision to validate the results.

AutoML in Practice: How Companies are Automating Machine Learning?

A few practical examples of AutoML are given below:

Google’s AutoML Vision allows users to train custom machine-learning models for image recognition using th mage datasets’s AutoML enables data scientists and analysts to automatically train and optimize machine learning models without having to write code

DataRobot provides an AutoML platform that can automatically build, evaluate and deploy machine learning models for a wide range of use cases, including fraud detection, customer churn prediction, and predictive maintenance

Amazon SageMaker is a fully managed service that enables data scientists and developers to quickly and , train, and deploy machine learning models at scale

IBM Watson AutoAI is a platform that automates the process of building, training, and deploying machine learning models and provides interpretability and explainability features that help users understand the models’ decision-making processes

Microsoft Azure ML is a cloud-based platform that provides a wide range of tools and services for building, deploying, and managing machine learning models, including AutoML capabilities.

These are a few examples of how companies leverage AutoML in different industries to automate model building and hyperparameter tuning, allowing data scientists to focus on model selection and evaluation.


AutoML automates the process of building and tuning machine-learning models. This method uses algorithms to search the best model and hyperparameters rather than relying on human expertise. AutoML includes increased efficiency and the ability to handle large amounts of data. It can be useful in the shortage of experienced machine learning practitioners. However, there are also limitations to AutoML. It can be computationally expensive and difficult to interpret the results of the automated search process. Additionally, the practical use of AutoML is limited by the data’s quality and computational resources’ availability. In practice, AutoML is mainly used in an indus prove productivity and model performance in scenarios like image, speech, text, and other forms of data.

Key Takeaways:

Simplify the process of building and training models.

AutoML suffers limitations such as a lack of control over the model selection process, huge data requirements, computationally expensive, and overfitting issues.

Expert supervision is important to validate the results of AutoML to counter available limitations.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.


The Best Of Ces 2012: Popsci’s Products Of The Future

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It’s often tough to find a clean signal in all the noise of CES, but in putting this list together, we realized how excited we are about a lot of the new gear we saw this year. CES 2012 brought us the best TV we’ve ever seen, two killer new cameras, a fridge that can chill beer cans in five minutes, high-speed in-car mobile wireless, awesome new smartphones, and a lot more. Check out our picks for the Products of the Future in the gallery below.

Nokia Lumia 900

Modular Robotics Cubelets

The simplest way to wrap your head around what exactly the Cubelets are is to think of them as robotic Legos. Sold as a six-block starter kit, the Cubelets are pre-programmed 1.5-inch blocks, each with its own action—to move, sense nearby objects, display light, whathaveyou—and the way you stack them determines what your final robot will do. Snapping a battery block on top of a motion-sensing block and a roller block, for instance, will great a ‘bot that automatically moves when the lights go on (or off). Modular Robotics sells add-on blocks with other traits (sound sensitivity, loudspeakers, etc.) and will add a Bluetooth block this year, allowing users to re-program their bricks over a simple Web interface.

Samsung Super OLED TV

OLED televisions are super thin, ultra-contrasty, and have colors that are saturated to the point of near surreality. But other than Sony’s dimunitive XEL-1, which has been on sale since 2008, they’re usually trotted out at CES only as dreamy concept showpieces. Samsung’s 55″ Super OLED, though, marks an important moment in TVs: large-panel OLEDs are now practical to manufacture and sell. No word on price (it will be exorbitant, surely), but Samsung stated plainly that the set would be available for purchase this year. And good thing: after you’ve stood in front of this OLED beauty, it’s hard to look at normal TVs the same way again.

Basis Band

LG’s French Door Refrigerator With Blast Chiller

This fridge wants us to never deal with warm beer ever again, and for that we are eternally thankful. Its special blast chiller compartment can take your beverage of choice from room temperature to a delicious 42º F in no time—5 minutes for a single can, or 8 minutes for two cans or a bottle of wine. A gentle rocking motion exposes the liquids to the cold evenly, without leaving you with a fizzy carbonation bomb upon opening. See a video of the chiller in action here.

MakerBot Replicator

The 3-D printer you build yourself is new and improved for 2012, now sporting a larger building area and twin extruders for printing with two colors (or entirely different materials) simultaneously. Your very own personal assembly line for under $2,000.

MicroVision PicoP Gen2 HD Laser Projector

If there’s one thing we love at PopSci, it’s a pico projector. And, well, we’ve seen a lot of them over the last three years. But MicroVision’s new PicoP Gen2 is sorta the one we’ve been looking for. Not only is it the first pico to display 720p HD video, it’s also the first one we’ve seen that’s small and efficient enough to be built into more things that just standalone projectors–we’re talking projector phones that aren’t bricks and even portable gaming systems that can be standalone mobile entertainment centers.

Fujifilm X-Pro1

Parrot ZIK Headphones

There are always tons of headphones at CES. So why’d we pick out these wireless ones from Parrot, a company known for fun flying drones rather than audio equipment? A few reasons: Designed by Philippe Starck, a well-known industrial designer, the headphones look awesome, all black leather and curved silver metal. The way you use them is really, really cool: they have controls built in, but not in any boring way like an inline remote or (and here we utter a bad-design shudder) some play/pause/forward/back buttons on the outside of the ear cups. Instead, it uses a proximity sensor to figure out when you’re wearing and when you’ve taken the headphones off, and it pauses automatically when you remove them. To change the volume, you gently stroke the ear cups up and down, and to go to the next or previous track, you stroke left and right. (You can see in this picture that our own John Mahoney got pretty into the stroking part of this.) The ZIK has a bunch of other features too: it’s got self-contained noise cancellation (Parrot says the batteries last about five hours with the battery-draining cancellation turned on), Bluetooth to connect, and even NFC, which to my knowledge has never been implanted into a pair of headphones before. And they’re super comfortable. Audio nerds: we only tested them in the midst of a raucous western-themed press event, so we can’t vouch for audio quality in any respectable way. They sounded pretty good but we can’t comfortably say much more than that. They’ll be available sometime this year for an undisclosed (but undoubtedly steep) price.

Sennheiser RS220

Thanks to a new wireless streaming standard, the RS220 home-theater headphones may well be the best-sounding wireless pair you can get. The pair transmit uncompressed audio over the 2.4GHz range—yes, the same as Kleer and other high-end transmission standards—but this new DSSS trick modulates the signal across several clustered frequencies, and the headphones recompose the signal at the other end. The result: better dynamic range and super low-latency, which might not matter as much when listening to music, but makes a world of difference when you need to sync with a TV screen.

Lenovo IdeaPad Yoga

It’s become clear this week that 2012 is the Year of the UltraBook, but in reality there’s not a lot to distinguish one super-trim laptop from another. Unless we’re talking about the IdeaPad Yoga, which in this case we happen to be. The Windows-running clamshell can morph into any of four form factors. From standard laptop clamshell, rotate the keyboard behind the screen (like the cover of a spiral notebook) to enter tablet-style mode with the keyboard deactivated. Or, use the keyboard as a stand in either a sandwich-board-style orientation or a right-angle hinge.

Mobile High-Definition Link

Mobile High-Definition Link, or MHL, wasn’t announced at CES 2012, but it was during CES that we at PopSci really started to get excited about it. It’s a new kind of technology that can be applied to just about any connector, like HDMI, USB, or any kind of proprietary port (including Apple’s iPod/iPhone/iPad port), and it essentially gives those dumb old connectors a whole bunch of new powers. Some of those, like HDMI, for example, can’t deliver power. But an MHL-supporting HDMI port sure can. That’s how we can get things like the Roku Streaming Stick, which is an entire Roku the size of a USB thumb drive that plugs right into your TV’s HDMI port. Other cool features include the ability to control anything plugged into an MHL-enabled HDMI port with your TV’s remote–no need to have several remotes scattered around anymore.

Able Planet Personal Sound Amp PS2500AMP

Unlike the majority of personal hearing aids, Able’s Planet’s Personal Sound Amp tucks almost entirely inside the ear canal—nearly invisibly so. Like a noise-canceling headphone pair, the Amp senses what noise it’s up against—wind, music, the din of a loud room—and automatically tunes itself to cancel out those noises. FI the wearer is still having trouble hearing (ie: if the earbud has yet to re-tune to the room), he can cup his hand over his ear; the change in pressure from that action tells the Amp to re-tune itself.

OnStar & Verizon Wireless Chevy Volt with LTE

The promise of Verizon’s 4G LTE network has long—well, since 2010—been the ability to stream audio and video consistently from anywhere, even if you’re moving. Until now there are have been demos involving telepresence robots and LTE-equipped broadcast cameras, but the new OnStar shows LTE the way a real person would use it: in a car. LTE connectivity allows the system to constantly connect to road-trip-friendly cloud services like Skype and Pandora. Netflix? Maybe not the best idea.

Canon PowerShot G1 X

Continuing the theme of exceptional image quality in ever-smaller packages, the PowerShot G1 X is an entirely new beast for Canon—a camera system within itself. Forgoing a buy-in to an interchangeable lens system to keep costs down, the G1 X offers a fixed 4x zoom feeding light into a brand new CMOS sensor that’s just a hair smaller than those found in most DSLRs.

Corning Gorilla Glass 2

If you have a modern smartphone, chances are you have some Gorilla Glass in your pocket. At CES, Corning announced a new formula for their chemically-strengthened glass that’s 20 percent tougher, which means tablet touchscreens and notebook LCDs can go 20 percent thinner without sacrificing strength and durability. The profusion of slim, MacBook-Air-like ultrabooks this year is no coincidence; Gorilla Glass is one of the enabling technologies pushing our gadgets ever-sleeker.

What’s In The Future For Esports

When it comes to esports it seems like there is no end to the potential for expansions. What started as different and individual groups of gamers coming together to enjoy their hobby in an organized and competitive manner has grown over the years into a bona fide billion-dollar industry. With hordes of gamers joining in on streams and live esports events from around the world to make up audiences that traditional sports would be jealous of.

That being said, I’m only describing esports at the moment.

One key difference between esports and traditional sports (aside from the obvious physical athleticism requirements) is the fact that esports are subject to dramatic change.

I don’t just mean within whatever game the esports stars are playing either. Sure, one official league might use a set, specific version of CS:GO for their tournaments, whilst another welcomes updates and patches into the game itself and forces its players to stay on their toes as far as compatible strategy goes – but there are much bigger changes that could sweep the world of esports.

The Future Of Competitive Games

One such change is the rise and fall in popularity that lots of games go through, and how this translates to esports.

Sure, there are always going to be the mainstays of the esports scene; CS:GO, LoL, Dota 2, and so on, but what about seasonal games? Specifically, Fortnite.

Fortnite is a game that quickly spread its influence throughout the world, particularly amongst younger gamers. It’s free to play, microtransaction based business model combined with its ease of access for streamers made it a smash hit, which translated its popularity into the esports crowd. Just imagine the horde of devoted Fortnite fans who would want to attend the Fortnite world cup, or even just tune in to Twitch to watch their favorite players compete.

Now consider that in January this year Fortnite saw its lowest-income since November 2023. A sign of things to come? Possibly, but its more likely that at some point sooner or later a new game will come along that leeches Fortnites popularity, and encourages a drastic shift in the player base that again, would translate to the esports world.

My point (simplified) is this: popularity in some games comes and goes, and there is every chance that in a few years Fortnite could be a dead game whilst another, unreleased or even unthought of game enjoys the same level of fame.

But where does this leave the Fortnite esports players?

It seems that many esports players are trying to follow this career path as well, with pro players like Robson Merrit (TeaGuvner), going from pro player to coach without missing a beat, and doing well in their chosen path – but if the game no longer exists to play, then what good is coaching it?

In this situation, the player is left out in the cold once the game stops being profitable. What we could see within the world of esports is an overarching body or governing philosophy being developed, so that players can take their already established skills and tactics and apply them to other games. Is it possible? Perhaps, but there are a ton of variables to overcome when you are dealing with esports, and it may not be possible for a pro-Fortnite player to take their skills to Dota 2 and enjoy the same level of success.

Only time will tell.

The Next Step In Esports Training

So, I’ve touched on retraining esports players in different games should they decide a shift in career trajectory is necessary, but what could be in the future for the training programs of esports players? There is one thing on everyone’s mind within the industry in answer to that question, and that thing is AI.

We already know that esports players spend around 8 hours a day (in general) training. This means working as part of a team, as well as on their own to enhance and develop their skills in-game. But, what happens when the top player in any game reaches their peak, and the game lobbies aren’t filling up with internet strangers capable of providing a challenge?

Bots use AI written by the game devs to behave in a way that is consistent with the multiplayer in a game, with computer-controlled enemies and teammates simulating the actions of a real-life player in the multiplayer game, with no one controlling them.

And, if you don’t have familiarity with the concept of bots, then I can tell you that the option is often available for players to tweak the difficulty level of the bots themselves. You can play against simulated noobs, or seasoned veterans – it’s up to the player.

Now, let’s apply that technology to the esports world, and a player’s training regimen, and you can see why AI might be at the forefront of the esports training world.

Potentially, a player could spend limitless hours training against a plethora of opposites, with the AI in their game being built to match the habits of players at their skill level. Not only that, but an AI opponent could also potentially emulate the movements of other professional players, allowing for pre-match training to be conducted against a fairly realistic depiction of the opposite player to the esports player training.

AI could really help in players’ in-game performance, but what about coaching?

When it comes to coaching, esports teams already enjoy the expertise of players who have decided to stop their competitive playing due to whatever reason (aging out is a [popular one), and now teach the newer pro players how to win with all of their tactics and in-game knowledge.

But, esports are traditionally fast-paced, and it can be hard for a coach to single out an individual player for feedback during a fast-paced match, especially in games that have blink-and-you-miss-them opportunities cropping up every few seconds.

So, what if the esports teams could implement their coaching into the game itself, allowing AI to take over and deliver specific criticisms when they become relevant, as a form of instant feedback?

That’s exactly what is being planned on top of the line CPUs at the moment, and being made possible in future processors. These types of AI programs can analyze a player’s environment, play style, opponents and much more to deliver immediate feedback to a player in the moment, leading to a smoother and quicker progression in skill.

Bringing In New Talent

With the last section in mind, let’s look to the esports players who are only at the beginning of their career – or even those who haven’t begun to play yet.

With AI becoming such a prominent force in the world of esports, its entirely possible that in the future these types of AI will become available to casual players to download in exchange for a fee so that they can improve their in-game performance.

So, the potential pipeline for player progression and in-game engagement could be completely upheaved by AI integration, especially when you consider that the types of CPUs available for en masse gaming are becoming more powerful by the year.

This means that a player could potentially buy (for the sake of argument) the latest Call of Duty. Then, after becoming involved with the multiplayer and invested in progressing through the multiplayer skillset, they purchase the in-game esports training AI. Suddenly, not only do you have a player who is potentially a new esports player (depending on their skill level), but you have an additional in-game transaction that could lead to more (ie passes).

So, we have a way for esports to directly affect the lifespan of both new and established games with the development of esports training AI, but there is another way that the esports world could directly influence casual gaming with the implementation of this AI, and that’s with player support.

I mentioned before about how in-game AI could be possible of replicating professional players for the pro gamers to train against. This same technology could be collected together and distributed to a mass audience, allowing gamers to play against a virtual representation of their favorite esports players.

You can imagine how this could be appealing to both marketing teams and the players themselves; Players sign up to have their virtual presence distributed to the gaming audience in exchange for what we can essentially call an appearance fee, and in return, the players get the chance to play with the biggest stars in the esports world and see how they fare.

You can imagine how this can be spun out as well – imagine different amateur leagues playing not only against each other but pitting their skills against professional teams as well with the assistance of this AI. Game developers could maximize the impact of this AI too, offering in-game prizes and rewards for teams or players who can beat the professional AI.

That opens up a big question though: will the AI make it easier for new players to enter the esports world, and will the future of esports be easier to access?

The Accessibility Of Future Esports

One big question that comes up in regards to the future of esports is accessibility for new players, and how easy it will be for amateur players to become involved in the industry. So, to answer that question we need to look to the fastest growing platform that esports takes place on Mobile devices.

Right now mobile gaming makes up 51% of the whole world’s total gaming revenue, with its biggest audience being in China (though recent restrictions to Chinese policy regarding video games could soon change this), making up 620 million total players in China alone.

Consider that games like Hearthstone, Gwent, and other fantasy online card games are immensely popular in the esports scene and are easily playable on a phone.  The ease of access granted to these games alone on a mobile device means that we could see a huge influx of professional gamers in these different games, even though the average person on the street might not recognize them as a traditional game.

So let’s talk about a ‘traditional’ game, a shooter like Fortnite. Fortnite is also available to download onto mobile devices, and with the assistance of a gamepad players can easily dominate the leader boards should they want to invest the time into the game itself. China is already the biggest consumer of mobile gaming – so imagine how many potential professional gamers there are sat waiting for their chance to explore the esports world.

This isn’t lost on China either. The revenue generated by esports must be lucrative enough that different businesses and sections of the government have noticed, and are now opening ‘Gaming Hotels’, where younger gamers can go to develop their skills over an extended period of time in an environment similar to the gaming houses that lots of pro gamers live in around the world.

That is a trend set to expand across the globe. Gaming hotels, gaming camps – even esports scholarships at universities have begun, and as a result, the industry is seeing more support than ever in getting brand new players into the esports scene.

The Technology Of Future Esports

I mentioned towards the beginning of the article that there are going to be a lot of major changes to the way that esports are changed, and we can talk about a few of them here. For example, 5G is set to rock the mobile gaming world.

Whereas before the average mobile gamer might have had to settle on WiFi to make sure of a stable internet connection to enjoy online gaming on whatever mobile device they were using – be it tablet or phone.

With 5G, it’s going to be a lot easier for a gamer to jump into any sort of mobile game at any opportunity, and practice their skills wherever they might be. That level of accessibility isn’t just going to draw in more gamers to the platform, but it could potentially also draw in a larger developer base for mobile games overall, with wider accessibility leading to larger overall profits.

Plus, mobile gaming and devices may become a big part of one technology that as of yet, isn’t fully integrated into the world of esports: V.R.

Virtual Reality is, as of yet, entirely underutilized within the world of esports – and that goes for both spectating and the games themselves.

Starting with the games, lots of industry insiders and game developers themselves have called V.R the future of gaming. That being said, the gaming world is yet to see a viable and enjoyable V.R game that enjoys the same level of success as Fortnite or CS:GO for example. Steps have been made with the likes of Pavlov, but realistically, the availability of V.R for the mass public is yet to get on the same level of mobile gaming, so it may be a while before we see the V.R equivalent of the LoL World Championships.

That being said though, through mobile devices there is a potential for V.R to become a big part of the audience’s experience. It wouldn’t be too hard for an esports tournament host to set up a type of ‘virtual arena’, and sell tickets or allow access to this arena as an alternative for those who have access to anything like Google Cardboard – a simpler way for gamers to get involved or closer to the esports action whilst being totally immersed in the experience.

Right now, there isn’t much in the way of V.R integration when it comes to esports. But we are certain that in five, ten or twenty years we are going to see V.R a lot more in our video games, and out of that will come to a thriving esports scene – just as it did for traditional games.

But, these are just a few ways that esports could change in the future. We are yet to see the full impact of coronavirus on the industry, and there could be an esports industry world-rocking technological development just around the corner that might force me to rewrite this whole article.

Right now, all we can say for certain is that the long term prospects for professional gamers are looking better thanks to the integration of AI and that different teams rosters should be set to grow exponentially as the popularity of gaming and esports expands, alongside the ease of access into the hobby.

The Future Of Artificial Intelligence In Manufacturing

Industrial Internet of Things (IIoT) systems and applications are improving at a rapid pace. According to Business Insider Intelligence, the IoT market is expected to grow to over $2.4 trillion annually by 2027, with more than 41 billion IoT devices projected.

Providers are working to meet the growing needs of companies and consumers. New technologies, such as Artificial Intelligence (AI), and machine learning make it possible to realize massive gains in process efficiency. 

With the growing use of AI and its integration into IoT solutions, business owners are getting the tools to improve and enhance their manufacturing. The AI systems are being used to: 

Detect defects

Predict failures

Optimize processes

Make devices smarter

Using the correct data, companies will become more creative with their solutions. This sets them apart from the competition and improves their work processes.

Detect Defects

AI integration into manufacturing improves the quality of the products, reducing the probability of errors and defects.

Defect detection factors into the improvement of overall product quality. For instance, the BMW group is employing AI to inspect part images in their production lines, which enables them to detect deviations from the standard in real time. This massively improves their production quality.

Nokia started using an AI-driven video application to inform the operator at the assembly plant about inconsistencies in the production process. This means issues can be corrected in real time. 

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Predict Failures

Predicting when a production line will need maintenance is also simple with machine learning. This is useful in the sense that, instead of fixing failures when they happen, you get to predict them before they occur.

Using time-series data, machine learning models enhance the maintenance prediction system to analyze patterns likely to cause failure. Predictive maintenance is accurate using regression, classification, and anomaly detection models. It optimizes performance before failure can happen in manufacturing systems.

General Motors uses AI predictive maintenance systems across its production sites globally. Analyzing images from cameras mounted on assembly robots, these systems are identifying the problems before they can result in unplanned outages.

High speed rail lines by Thales are being maintained by machine learning that predicts when the rail system needs maintenance checks.

Optimize Processes

The growth of IIoT allows for automation of most production processes by optimizing energy consumption and predictions for the production line. The supply chain is also improving with deep learning models, ensuring that companies can deal with greater volumes of data. It makes the supply chain management system cognitive, and helps in defining optimal solutions. 

Make Devices Smarter

By employing machine learning algorithms to process the data generated by hardware devices at the local level, there is no longer a need to connect to the internet to process data or make real-time decisions. Edge AI does away with the limitation of networks.

The information doesn’t have to be uploaded to the cloud for the machine learning models to work on it. Instead, the data is processed locally and used within the system. It also works for the improvement of the algorithms and systems used to process information.

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What’s Next?

The manufacturing market is seeing a huge boost thanks to the IIoT and AI progress. Machine learning models are being used to optimize work processes. 

The quality of products is getting improved by reducing the number of defects that are likely to occur. This is expected to improve over time, and it also will heavily improve the production process to reduce errors and defects in products.

There is still a huge potential of AI that has yet to be utilized. Generative Adversarial Networks (GAN) can be used for product design, choosing the best combination of parameters for a future product and putting it into production.

The workflow becomes cheaper and more manageable. Companies realize this benefit in the form of a faster time to market. New product cycles also ensure that the company stays relevant in terms of production.

Networks are set to upgrade to 5G, which will witness greater capacities and provide an avenue for artificial intelligence to utilize this resource better. It will also be a connection for the industrial internet of things and see a boost in production processes. Connected self-aware systems will also be useful for the manufacturing systems of the future.

Salesforce Lightning: The Future Of Crm Design


Customer relationship management (CRM) is a crucial aspect of modern business operations. With customers at the centre of every business, managing their interactions and experiences is vital to ensuring long-term success. Salesforce, a leading provider of cloud-based CRM software, has been at the forefront of CRM technology for over two decades.

In 2014, they launched Salesforce Lightning, a new user interface for their CRM platform. Lightning was designed to provide a more modern and intuitive user experience while also enhancing the platform’s capabilities. Since then, Lightning has become the future of CRM design, and businesses that adopt it are reaping the benefits of a more streamlined and efficient workflow.

History of Salesforce

Salesforce was founded in 1999 by Marc Benioff, Parker Harris, Dave Moellenhoff, and Frank Dominguez. At the time, Benioff had left his job at Oracle and wanted to create a new kind of CRM software that would be entirely cloud-based. The company’s mission was to “democratize enterprise software” by making it more accessible and affordable for businesses of all sizes.

Salesforce quickly gained traction and became known for its innovative approach to CRM. In 2003, they launched the AppExchange, a marketplace for third-party applications that integrated with Salesforce. This allowed businesses to customize their CRM software to meet their specific needs, without having to build everything from scratch.

Over the years, Salesforce has continued to expand its offerings, adding new products and features to its platform. They’ve also acquired several companies, including ExactTarget, Demandware, and Tableau, to broaden their capabilities and improve their offerings.

Evolution of CRM Design

CRM design has come a long way since the early days of Salesforce. When the company first launched, their interface was relatively basic and utilitarian. It was designed to provide users with access to customer data and help them manage their interactions with clients. While this was a significant improvement over traditional on-premises CRM software, it was still far from perfect.

Over time, Salesforce began to add more features and functionality to their platform. They also started to invest more in design, recognizing that a more intuitive and user-friendly interface would be crucial to their long-term success. In 2013, they hired John Maeda, a renowned designer and technologist, to lead their design team. Maeda’s influence can be seen in the company’s renewed focus on design and user experience.

Features of Salesforce Lightning

Salesforce Lightning is a complete overhaul of the Salesforce platform, providing users with a more modern and intuitive interface.

Some of the key features of Lightning include −

Customizable Home Page

The Lightning home page is fully customizable, allowing users to create a personalized dashboard that provides quick access to the information they need most. Users can add and remove components, such as charts, tables, and lists, to create a dashboard that meets their specific needs.

Kanban View

The Kanban view is a new way of visualizing data in Salesforce. It provides users with a card-based interface that allows them to drag and drop records between columns. This is particularly useful for managing tasks or projects, as it provides a more visual way of tracking progress.

Enhanced Search

Enhanced Search is another feature of Salesforce Lightning that provides improved search capabilities within the platform. It allows users to search for records, files, and other information using natural language, making it easier to find what they need. Additionally, Enhanced Search provides results that are personalized to the user based on their past behaviour and preferences, making it even more efficient and intuitive.

Lightning App Builder

The Lightning App Builder allows users to create custom applications without needing to write code. It provides a drag-and-drop interface that allows users to add and configure components, such as charts, tables, and forms, to create a custom application that meets their specific needs.

Lightning Experience

Lightning Experience is the new user interface for Salesforce, designed to provide a more modern and intuitive experience. It includes features such as contextual hovers, where users can see additional information about a record by hovering over it, and split view, which allows users to view two records side-by-side.

Lightning Components

Lightning Components are reusable building blocks that can be used to create custom applications and pages. They provide a consistent look and feel across the platform and can be used to create custom functionality that integrates seamlessly with the Salesforce platform.

Salesforce Einstein

Salesforce Einstein is the company’s artificial intelligence (AI) platform, which is integrated into Lightning. It provides users with insights and recommendations based on their data, helping them to make more informed decisions.

Benefits of Salesforce Lightning

There are many benefits to using Salesforce Lightning for your business.

Here are some of the most significant −

Improved User Experience

The Lightning user interface is designed to be more intuitive and user-friendly than the previous interface. This makes it easier for users to navigate the platform and find the information they need quickly.

Increased Efficiency

Lightning’s customizable home page, Kanban view, and other features allow users to work more efficiently, reducing the time it takes to complete tasks and improving productivity.

Better Data Insights

Salesforce Einstein provides users with insights and recommendations based on their data, helping them to make more informed decisions. This can lead to better business outcomes and increased revenue.

Increased Customization

The Lightning App Builder and Lightning Components allow users to create custom applications and pages without needing to write code. This increases the platform’s flexibility and allows businesses to tailor the platform to their specific needs.

Better Integration

Lightning’s modern architecture and open APIs make it easier to integrate with other systems and platforms, such as marketing automation tools and ERP systems. This improves data visibility across the organization and streamlines workflows.

Real-World Examples

Many businesses have already adopted Salesforce Lightning and are seeing the benefits of the new interface.

Here are some real-world examples of how businesses are using Lightning to improve their operations −


Groupon is a global e-commerce marketplace that connects customers with local businesses. They use Salesforce Lightning to manage their sales and marketing operations, allowing them to track customer interactions, analyze data, and improve their marketing efforts.

The American Red Cross

The American Red Cross is a non-profit organization that provides disaster relief and other humanitarian services. They use Salesforce Lightning to manage their donor relationships, allowing them to track donations, communicate with donors, and analyze data to improve their fundraising efforts.

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