Data visualization using Matplotlib (Plotting a Simple Line Graph)

Data visualization is closely associated with data analysis, which uses code to explore the patterns and connections in a data set. A data set can be made up of a small list of numbers that fits in one line of code or it can be many gigabytes of data.

Data visualization involves exploring data through visual representations. When a representation of a data set is simple and visually appealing, its meaning becomes clear to viewers. People will see patterns and significance in your data sets that they never knew existed. With Python’s efficiency, we can quickly explore data sets made of millions of individual data points on just a laptop. Also, the data points don’t have to be numbers. We can analyze nonnumerical data as well. Generally we use Python for data-intensive work in genetics, climate research, political and economic analysis, and much more.

Data scientists have written an impressive array of visualization and analysis tools in Python, one of the most popular tools is Matplotlib, a mathematical plotting library. We’ve used Matplotlib in previous post and will continue to use it to make simple plots, such as line graphs and scatter plots.

Plotting a Simple Line Graph

Our objective is to plot a simple line graph using Matplotlib, and then customize it to create a more informative data visualization. We’ll use the cubic number sequence 1,8,27,64,125 as the data for the graph. See the code below:

import matplotlib.pyplot as plt

cubic_values = [1,8,27,64,125]
fig,ax = plt.subplots()
ax.plot(cubic_values)
plt.show()

Now let's understand our code, the first line imports the pyplot module using the alias plt so we don’t have to type pyplot repeatedly. The pyplot module is required as it contains a number of functions that generate charts and plots.

Next we create a list cubic_values which contains the data to be plotted. As per Matplotlib convention we call the subplots() function which can generate one or more plots in the same figure. The variable fig represents the entire figure or collection of plots that are generated. The variable ax represents a single plot in the figure and is the variable we’ll use most of the time to plot the data using the plot() method. Finally the function plt.show() opens Matplotlib’s viewer and displays the plot as shown in the figure below:

The output shows the cubic values in the increasing order. If you notice the label type is too small and the line is a little thin to read easily.Let's adjust every feature of this visualization. See the code below:

import matplotlib.pyplot as plt

cubic_values = [1,8,27,64,125]
fig,ax = plt.subplots()
ax.plot(cubic_values, linewidth=3)

# Setting chart title and label axes
ax.set_title("Cube Numbers", fontsize=24)
ax.set_xlabel("Value", fontsize=14)
ax.set_ylabel("Cube of Value", fontsize=14)
plt.show()

The linewidth parameter controls the thickness of the line that plot() generates. The set_title() method sets a title for the chart. The fontsize parameters, which appear repeatedly throughout the code, control the size of the text in various elements on the chart.

The set_xlabel() and set_ylabel() methods allow you to set a title for each of the axes as shown in the figure below:

The following code style the tick marks on both the axes:

# Set size of tick labels.
ax.tick_params(axis='both', labelsize=14)
plt.show()

The method tick_params() styles the tick marks depending on the argument for axis variable which in our case is both. Hence it affect the tick marks on both the x- and y-axes (axis='both') and also we set the font size of the tick mark labels to 14 using labelsize variable (labelsize=14). The final output is shown below:

Did you notice from the above plot that Cube of 4 is 125, Phew! Let's fix this. When you give plot() a sequence of numbers, it assumes the first data point corresponds to an x-coordinate value of 0, but our first point corresponds to an x-value of 1. We can override the default behavior by giving plot() the input and output values used to calculate the cubes. See the code below:

import matplotlib.pyplot as plt

input_values = [1, 2, 3, 4, 5]
cubic_values = [1,8,27,64,125]
fig,ax = plt.subplots()
ax.plot(input_values,cubic_values, linewidth=3)

# Setting chart title and label axes
ax.set_title("Cube Numbers", fontsize=24)
ax.set_xlabel("Value", fontsize=14)
ax.set_ylabel("Cube of Value", fontsize=14)

# Set size of tick labels.
ax.tick_params(axis='both', labelsize=14)
plt.show()

Now plot() will graph the data correctly because we’ve provided the input and output values, so it doesn’t have to assume how the output numbers were generated. The resulting plot, shown in Figure below, is correct.

We can specify numerous arguments when using plot() and use a number of functions to customize your plots. We’ll continue to explore these customization functions as we work with more interesting data sets in the coming posts.

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Artificial Intelligence in Security Market

The Artificial Intelligence in Security Market is segmented on the Basis of Application Type, Technology Type, Service Type and Regional Analysis. By Application Type this market is segmented on the basis of Intrusion Detection, Web Filtering, Anomaly Detection, Firewall, Data Loss Prevention and Distributed Denial of Services.

By Technology Type this market is segmented on the basis of Speech Recognition, Machine Learning, Image Processing and Natural Language Processing. By Service Type this market is segmented on the basis of Cloud Security, Data Security, Network Security, Identity & Access Security and Others. By Regional Analysis this market is segmented on the basis of North America, Europe, Asia-Pacific and Rest of the World.

As digital threats and cyber crimes are rising in number, security needs to be enhanced. Security now needs constant supervision and adaption to the current market scenarios. With the rise in connected enterprises, devices and applications, the businesses are becoming more vulnerable as they are connected to a mass of independent endpoints. AI in security provides an enticing proposition with its proactive threat mitigation capabilities which is needed for constant supervision and adaptation to the multifaceted security vulnerabilities faced by modern digitized economy.

With the increasing use of sophisticated techniques for cyber crimes, there is a rising need for AI based techniques to counter the threats from malicious software bots. AI can be used to fight against various cyber threats including spear phishing, watering hole attack, web shell, ransomware, DDoS attacks, and remote exploitation, by identifying and preventing them from proliferating into the systems. AI based security systems can analyze even those threats which have not occurred in the past unlike traditional based security systems.

Market Analysis:

According to Kenneth Research, the AI in security market is expected to reach $11.95 billion by 2024, growing at a CAGR of around 34.9% during the forecast period. The market is expected to witness a surge in the next few years, factors such as growing need of technologies like AI, machine learning and deep learning to tackle the evolving threats and advanced persistent threats which remains undetected within the network which steal the data, increasing number of security breaches and increasing BYOD trends has further pushed the growth of AI in security market during the forecast period.

Companies including IBM Corporation, Palo Alto Networks, Cylance are the key players in the Supply Chain Analytics market. IBM and MIT are investing in MIT-IBM Watson AI Lab which is focusing towards developing AI algorithms and new applications of AI in various fields such as healthcare and cybersecurity. Recently in 2018, Palo Alto Networks announced that it had acquired Israel-based Secdo to add endpoint detection and response (EDR) capabilities for its advanced endpoint protection and the application framework.

North America had the largest market share in 2017 and is expected to dominate the AI in security market during the forecast period. The market will experience a steep rise in this region. Factors driving the growth of the market in North America includes increasing government funding in this region, increasing IT security budgets which increased by more than 2% compared to the previous year, growing number of cyber-attacks, such as ransomware which has increased more than 50% worldwide with the US been the most affected country with ransomwares such as WannaCry and NotPetya, cryptojacking which disturbs the businesses financially and operationally, and growing presence of providers of AI based security solutions in the region.

One of the major advantage of using AI in security is that even the small deviations can be captured which were not possible in traditional signature based approaches. AI and machine learning applications can be used to fight against spear phishing, watering hole, webshell, ransomware, and remote exploitation by identifying the known as well as the unknown threats and preventing those threats before they can execute into the systems. 

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How artificial intelligence is changing the face of banking in India

Artificial intelligence (AI) will empower banking organizations to completely redefine how they operate, establish innovative products and services, and most importantly impact customer experience interventions. In this second machine age, banks will find themselves competing with upstart fintech firms leveraging advanced technologies that augment or even replace human workers with sophisticated algorithms. To maintain a sharp competitive edge,  banking corporations will need to embrace AI and weave it into their business strategy.

Let's examine the dynamics of AI ecosystems in the banking industry and how it is fast becoming a major disruptor by looking at some of the critical unsolved problems in this area of business. AI’s potential can be looked at through multiple lenses in this sector, particularly its implications and applications across the operating landscape of banking. Let us focus on some of the key artificial intelligence technology systems: robotics, computer vision, language, virtual agents, and machine learning (including deep learning) that underlines many recent advances made in this sector.

Industry Changes

Banks entering the intelligence age are under intense pressure on multiple fronts. Rapid advances in AI are coming at a time of widespread technological and digital disruption. To manage this impact, many changes are being triggered.

1. Leading banks are aggressively hiring Chief AI Officers while investing in AI labs and incubators.
2. AI-powered banking bots are being used on the customer experience front.
3. Intelligent personal investment products are available at scale
4. Multiple banks are moving towards custom in-house solutions that leverage sophisticated ontologies, natural language processing, machine learning, pattern recognition, and probabilistic reasoning algorithms to aid skilled employees and robots with complex decisions.

Some of the key characteristics shaping this industry include:

• Decision support and advanced algorithms allow the automation of processes that are more cognitive in nature. 
• Solutions incorporate advanced self-learning capabilities 
• Sophisticated cognitive hypothesis generation/advanced predictive analytics

Surge of AI in Banking

Banks today are struggling to reduce costs, meet margins, and exceed customer expectations through personal experience. To enable this, implementing AI is particularly important. And banks have started embracing AI and related technologies worldwide. According to a  survey by the National Business Research Institute, over 32 percent of financial institutions use AI through voice recognition and predictive analysis. The dawn of mobile technology, data availability and the explosion of open-source software provides artificial intelligence  huge playing field in the banking sector. The changing dynamics of an app-driven world is enabling the banking sector to leverage AI and integrate it tightly with the business imperatives.

Digital personal assistants and chatbots have transformed customer experience and communication. They are powerful enablers in freeing routine daily tasks and ensuring a personalised experience for customers. Virtual assistants and chatbots have many applications. 

AI in Banking Customer Services

Automated AI-powered customer service is gaining strong traction. Using data gathered from user devices, AI-based relay information using machine learning by redirecting users to the source. AI-related features also enable services, offers, and insights in line with the user’s behavior and requirements. The cognitive machine is trained to advise and communicate by analyzing user data. Online wealth management services and other services are powered by integrating AI advancements to the app by capturing relevant data.

Personalized wealth planning is revolutionized using AI. For example, if a customer is looking to buy a new car, the app will provide guidance for the proposed outlay and loan approval limits based on current expenditure and income. Chatbots too can be “employed” to act as customer service agents and serve customers continuously throughout a day.

The tested example of answering simple questions that the users have and redirecting them to the relevant resource has proven successful. Routine and basic operations i.e. opening or closing the account, transfer of funds, can be enabled with the help of chatbots.

Fraud and risk management

Online fraud is an area of massive concern for businesses as they digitise at scale. Risk management at internet scale cannot be managed manually or by using legacy information systems. Most banks are looking to deploy machine or deep learning and predictive analytics to examine all transactions in real-time. Machine learning can play an extremely critical role in the bank’s middle office.

The primary uses include mitigating fraud by scanning transactions for suspicious patterns in real-time, measuring clients for creditworthiness, and enabling risk analysts with right recommendations for curbing risk.

Trading and Securities

Robotic Process Automation (RPA) plays a key role in security settlement through reconciliation and validation of information in the back office with trades enabled in the front office. Artificial intelligence facilitates the overall process of trade enrichment, confirmation and settlement.

Banks have also actively started to leverage AI for reconciliations in the case of over-the-counter derivatives and Forex transactions, and for target balancing and notional pooling in cash and liquidity management.

Credit Assessment

Lending is a critical business for banks, which directly and indirectly touches almost all parts of the economy. At its core, lending can be seen as a big data problem. This makes it an effective case for machine learning. One of the critical aspects is the validation of creditworthiness of individuals or businesses seeking such loans. The more data available about the borrower, the better you can assess their creditworthiness.

Usually, the amount of a loan is tied to assessments based on the value of the collateral and taking future inflation into consideration. The potential of AI is that it can analyse all of these data sources together to generate a coherent decision. In fact, banks today look at creditworthiness as one of their everyday applications of AI.

Portfolio Management

Banks are increasingly relying on machine learning to make smarter, real-time investment decisions on behalf of their investors and clients.

These algorithms can progress across distinct ways. Data becomes an integral part of their decision-making tree, this enables them to experiment with different strategies on the fly to broaden their focus to consider a more diverse range of assets.

Banks are focused to leverage an AI and machine learning-based technology platforms that make customized portfolio profiles of customers based on their investment limits, patterns and preferences.

Banking and artificial intelligence are at a vantage position to unleash the next wave of digital disruption. A user-friendly AI ecosystem has the potential for creating value for the banking industry, but the desire to adopt such solutions across all spectrum can become roadblocks. Some of the issues can be long implementation timelines, limitations in the budgeting process, reliance on legacy platforms, and the overall complexity of a bank’s technology environment.

To overcome the above challenges of introducing and building an AI-enabled environment. Banks need to enable incremental adoption methods and technologies. The critical part is ensuring that the transition allows them to overcome the change management/behavioral issues. The secret sauce of successful deployment is to ensure a seamless fit into the existing technology architecture landscape, making an effective AI enterprise environment.

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Top 10 real-life examples of Machine Learning

Machine learning is one modern innovation that has helped man enhance not only many industrial and professional processes but also advances everyday living. But what is machine learning? It is a subset of artificial intelligence, which focuses on using statistical techniques to build intelligent computer systems in order to learn from databases available to it. Currently, machine learning has been used in multiple fields and industries. For example, medical diagnosis, image processing, prediction, classification, learning association, regression etc.

The intelligent systems built on machine learning algorithms have the capability to learn from past experience or historical data. Machine learning applications provide results on the basis of past experience. In this article, we will discuss 10 real-life examples of how machine learning is helping in creating better technology to power today’s ideas.

Image Recognition

Image recognition is one of the most common uses of machine learning. There are many situations where you can classify the object as a digital image. For example, in the case of a black and white image, the intensity of each pixel is served as one of the measurements. In colored images, each pixel provides 3 measurements of intensities in three different colors – red, green and blue (RGB).

Machine learning can be used for face detection in an image as well. There is a separate category for each person in a database of several people. Machine learning is also used for character recognition to discern handwritten as well as printed letters.  We can segment a piece of writing into smaller images, each containing a single character.

Speech Recognition

Speech recognition is the translation of spoken words into the text. It is also known as computer speech recognition or automatic speech recognition. Here, a software application can recognize the words spoken in an audio clip or file, and then subsequently convert the audio into a text file. The measurement in this application can be a set of numbers that represent the speech signal. We can also segment the speech signal by intensities in different time-frequency bands.

Speech recognition is used in the applications like voice user interface, voice searches and more. Voice user interfaces include voice dialing, call routing, and appliance control. It can also be used a simple data entry and the preparation of structured documents.

Medical diagnosis

Machine learning can be used in the techniques and tools that can help in the diagnosis of diseases. It is used for the analysis of the clinical parameters and their combination for the prognosis example prediction of disease progression for the extraction of medical knowledge for the outcome research, for therapy planning and patient monitoring. These are the successful implementations of the machine learning methods. It can help in the integration of computer-based systems in the healthcare sector.

Statistical Arbitrage

In finance, arbitrage refers to the automated trading strategies that are of a short-term and involve a large number of securities. In these strategies, the user focuses on implementing the trading algorithm for a set of securities on the basis of quantities like historical correlations and the general economic variables. Machine learning methods are applied to obtain an index arbitrage strategy. We apply linear regression and the Support Vector Machine to the prices of a stream of stocks.

Learning associations

Learning associations is the process of developing insights into the various associations between the products. A good example is how the unrelated products can be associated with one another. One of the applications of machine learning is studying the associations between the products that people buy. If a person buys a product, he will be shown similar products because there is a relation between the two products. When any new products are launched in the market, they are associated with the old ones to increase their sales.

Classification

A classification is a process of placing each individual under study in many classes. Classification helps to analyze the measurements of an object to identify the category to which that object belongs. To establish an efficient relation, analysts use data. For example, before a bank decides to distribute loans, it assesses the customers on their ability to pay loans. By considering the factors like customer’s earnings, savings, and financial history, we can do it. This information is taken from the past data on the loan.

Prediction

Machine learning can also be used in the prediction systems. Considering the loan example, to compute the probability of a fault, the system will need to classify the available data in groups. It is defined by a set of rules prescribed by the analysts. Once the classification is done, we can calculate the probability of the fault. These computations can compute across all the sectors for varied purposes. Making predictions is one of the best machine learning applications.

Extraction

Extraction of information is one of the best applications of machine learning. It is the process of extracting structured information from the unstructured data. For example, the web pages, articles, blogs, business reports, and emails. The relational database maintains the output produced by the information extraction. The process of extraction takes a set of documents as input and outputs the structured data.

Regression

We can also implement machine learning in the regression as well. In regression, we can use the principle of machine learning to optimize the parameters. It can also be used to decrease the approximation error and calculate the closest possible outcome. We can also use the machine learning for the function optimization. We can also choose to alter the inputs in order to get the closest possible outcome.

Financial Services

Machine learning has a lot of potential in the financial and banking sector. It is the driving force behind the popularity of the financial services. Machine learning can help the banks, financial institutions to make smarter decisions. Machine learning can help the financial services to spot an account closure before it occurs. It can also track the spending pattern of the customers. Machine learning can also perform the market analysis. Smart machines can be trained to track the spending patterns. The algorithms can identify the tends easily and can react in real time.

Conclusion

In a nutshell, we can say that machine learning is an incredible breakthrough in the field of artificial intelligence. And while machine learning has some frightening implications, these machine learning applications are one of the ways through which technology can improve our lives.

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How artificial intelligence and machine learning will impact our everyday life

Machine Learning and Artificial intelligence (AI) is now considered to be one of the biggest innovations since the microchip. AI used to be a fanciful concept from science fiction, but now it's becoming a daily reality. Neural networks (imitating the process of real neurons in the brain) are paving the way toward breakthroughs in machine learning, called "deep learning."

Machine learning can help us live happier, healthier, and more productive lives... if we know how to harness its power.


Some say that AI is ushering in another "industrial revolution." Whereas the previous Industrial Revolution harnessed physical and mechanical strength, this new revolution will harness mental and cognitive ability. One day, computers will not only replace manual labor, but also mental labor. But how exactly will this happen? And is it already happening?

Here are 15 ways artificial intelligence and machine learning will impact your everyday life.

1. Intelligent Gaming

Some of you may remember 1997 when IBM’s Deep Blue defeated Gary Kasparov in chess. But if you weren’t old enough then, you might remember when another computer program, Google DeepMind’s AlphaGo, defeated Lee Sedol, the Go world champion, in 2016.

Go is an ancient Chinese game, much more difficult for computers to master than chess. But AlphaGo was specifically trained to play Go, not by simply analyzing the moves of the very best players, but by learning how to play the game better from practicing against itself millions of times.

2. Self-Driving Cars and Automated Transportation

Have you flown on an airplane lately? If so, then you’ve already experienced transportation automation at work. These modern commercial aircraft use FMS (Flight Management System), a combination of GPS, motion sensors, and computer systems to track its position during flight. So an average Boeing 777 pilot spends just seven minutes actually flying the plane manually, and many of those minutes are spent during takeoff and landing.

The leap into self-driving cars is more complicated. There are more cars on the road, obstacles to avoid, and limitations to account for in terms of traffic patterns and rules. Even so, self-driving cars are already a reality. These AI-powered cars have even surpassed human-driven cars in safety, according to a study with 55 Google vehicles that have driven over 1.3 million miles altogether.

The navigation question has already been solved long ago. Google Maps already sources location data from your smartphone. By comparing the location of a device from one point in time to another, it can determine how fast the device is traveling. Put simply, it can determine how slow traffic is in real time. It can combine that data with incidents reported by users to build a picture of the traffic at any given moment. Maps can recommend the fastest route for you based on traffic jams, construction work or accidents between you and your destination.

But what about the skill of actually driving a car? Well, machine learning allows self-driving cars to instantaneously adapt to changing road conditions, while at the same time learning from new road situations. By continuously parsing through a stream of visual and sensor data, onboard computers can make split-second decisions even faster than well-trained drivers.

It’s not magic. It’s based on the exact same fundamentals of machine learning used in other industries. You have input features (i.e. the real-time visual and sensor data) and an output (i.e. a decision among the universe of possible next “actions” for a car).

So, sure these self-driving cars already exist, but are they ready for prime-time? Perhaps not yet, since the vehicles are currently required to have a driver present for safety. So despite exciting developments in this new field of automated transportation, the technology isn’t perfect yet. But give it a few months or years, and you’ll probably want to have one of these cars yourself.

3. Cyborg Technology

Obviously, our bodies and our brains have built in limitations and weaknesses. According to Oxford C.S. professor Shimon Whiteson, technology will improve to to such an extent that we will be able to augment some of our weaknesses and limitations with computers, thereby enhancing many of our natural abilities.

But wait - before you start picturing dystopian worlds of steel and flesh, consider for a moment that most people walking around are already “cyborgs” in a sense. How many people do you know who could survive the day without their trusty smartphone? We already rely on these handheld computers for communication, navigation, acquiring knowledge, receiving important news, and a host of other activities.

Without your smartphone, how in the world would you post on Instagram?

Yoky Matsuoka of Nest also believes that AI will become useful for people with amputated limbs. One day, the brain will be able to communicate with a robotic limb. This technology will give amputees more control and reduce the daily limitations they deal with.

4. Taking Over Dangerous Jobs

One of the most dangerous jobs is bomb disposal. Today, robots (or more more technically, drones) are taking over these risky jobs, among others. Right now, most of these drones require a human to control them. But as machine learning technology improves in the future, these tasks would be done completely by robots with AI. This technology alone has already saved thousands of lives.

Another job being outsourced to robots is welding. This kind of work produces noise, intense heat, and toxic substances found in the fumes. Without machine learning, these robot welders would need to be pre-programmed to weld in a certain location. However, advancements in computer vision and deep learning have enabled more flexibility and greater accuracy.

5. Environmental Protection

Machines can store and access more data than any one person could—including mind-boggling statistics. Using big data, AI could one day identify trends and use that information to arrive at h solutions to previously untenable problems.

For example, IBM’s Green Horizon Project analyzes environmental data from thousands of sensors and sources to product accurate, evolving weather and pollution forecasts. It allows city planners to run “what-if” scenarios and model ways to mitigate environmental impact.

And that’s just beginning. Exciting environment-oriented innovations are entering the market every day, from self-adjusting smart thermostats to distributed energy grids.

6. Digital Empathy and Robots as Friends

Most robots are still emotionless. But a company in Japan has made the first big steps toward a robot companion—one that can understand and feel emotions. Introduced in 2014, Pepperthe companion robot went on sale in 2015, with all 1,000 initial units selling out within a minute. The robot was programmed to read human emotions, develop its own, and help its human friends stay happy.

"I believe practical advancements in artificial intelligence will start to enable a more contextual form of computing with some of our devices, particularly smartphones and smart speakers... part of the way this development will likely occur is by learning more about people and how they think—essentially building a form of digital empathy."

—Bob O'Donnell, President of TECHnalysis Research

As funny as it sounds, the day that one can literally “buy a friend” is not too far away.

7. Improved Elder Care

For many seniors, everyday tasks can be a struggle. Many have to hire outside help or rely on family members. Elder care is a growing concern for many families.

AI is at a stage where replacing this need isn’t too far off, says Matthew Taylor, computer scientist at Washington State University. Elderly relatives who don’t want to leave their homes could be assisted by in-home robots. That solution offers family members more flexibility in managing a loved one’s care. These robots could help seniors with everyday tasks and allow them to stay independent and living in their homes for as long as possible, improving their overall well-being.

Medical and AI researchers have even piloted systems based on infrared cameras that can detect when an elderly person falls. Researchers and medical specialists can also monitor alcohol and food consumption, fevers, restlessness, urinary frequency, chair and bed comfort, fluid intake, eating, sleeping, declining mobility, and more.

8. Enhanced Health Care

Hospitals may soon put your wellbeing in the hands of an AI, and that’s good news. Hospitals that utilize machine learning to aid in treating patients see fewer accidents and fewer cases of hospital-related illnesses, like sepsis. AI is also tackling some of medicine’s most intractable problems, such as allowing researchers to better understand genetic diseases through the use of predictive models.

"2018 will be the year AI becomes real for medicine. ... In 2018, we'll begin the adoption of a technology that may truly transform the way providers work, and the way patients experience healthcare, on a global scale."

— Mark Michalski, executive director, Massachusetts General Hospital

Previously, health professionals must review reams of data manually before they diagnose or treat a patient. Today, high-performance computing GPUs have become key tools for deep learning and AI platforms. Deep learning models quickly provide real-time insights and, combined with the explosion of computing power, are helping healthcare professionals diagnose patients faster and more accurately, develop innovative new drugs and treatments, reduce medical and diagnostic errors, predict adverse reactions, and lower the costs of healthcare for providers and patients.

9. Innovations in Banking

Consider how many people have a bank account. Now, on top of that, consider the number of credit cards that are in circulation. How many man hours would it take for employees to sift through the thousands of transactions that take place every day? By the time they noticed an anomaly, your bank account could be empty or your credit card maxed out.

Using location data and purchase patterns, AI can also help banks and credit issuers identify fraudulent behavior while it is happening. These machine learning based anomaly detection models monitor transaction requests. They can spot patterns in your transactions and alert users to suspicious activity.

They can even confirm with you that the purchase was indeed yours before they process the payment. It may seem inconvenient if it was just you eating at a restaurant while traveling on holiday, but it could end up saving you thousands of dollars someday.

10. Personalized Digital Media

Machine learning has massive potential in the entertainment industry, and the technology has already found a home in streaming services such as Netflix, Amazon Prime, Spotify, and Google Play. Some algorithms are already being used to eliminate buffering and low-quality playback, getting you the best quality from your internet service provider.

ML algorithms are also making use of the almost endless stream of data about consumers’ viewing habits, helping streaming services offer more useful recommendations.

"Given the rapid pace of research, I expect AI to be able to create new personalized media, such as music according to your taste. Imagine a future music service that doesn't just play existing songs you might like, but continually generates new songs just for you."

— Jan Kautz, Senior Director of Visual Computing and Machine Learning Research, NVIDIA

They will help more and more with the production of media too. NLP (Natural Language Processing) algorithms help write trending news stories to decrease production time, and a new MIT-developed AI named Shelley is helps users write horror stories through deep learning algorithms and a bank of user-generated fiction. At this rate, the next great content creators may not be human at all.

11. Home Security and Smart Homes

For the best tech in home security, many homeowners look toward AI-integrated cameras and alarm systems. These cutting-edge systems use facial recognition software and machine learning to build a catalog of your home’s frequent visitors, allowing these systems to detect uninvited guests in an instant.

AI-powered smart homes also provide many other useful features, like tracking when you last walked the dog or notifying you when your kids come home from school. The newest systems can even call for emergency services autonomously, making it an attractive alternative to subscription-based services that provide similar benefits.

Consumer AI will enable wave after wave of convenient automations in the home. When combined with appliances, AI could make housework and household management seamless.

AI-powered apps which allow the oven to communicate with the refrigerator and the pantry robot would act like home chefs. Instant replenishment of food and supplies would mean never running out of anything again. Cleaning could be schedule through sensor-to-appliance connections, after which robotic cleaners would work almost completely independently of humans.

Another advantage of smart homes would be a reduction of household waste and automated recycling, putting the household in better balance with the ecosystem. Releasing humans from housework could deliver major benefits in terms of improving sustainability, saving time, and reducing stress.

12. Streamlined Logistics and Distribution

Imagine getting a package in just a few hours and at a very low shipping cost. That’s the promise of AI in logistics and distribution, with its promise to tame the massive amounts of data and decisions in the trillion-dollar shipping and logistics industry. Amazon has already started experimenting with autonomous drones that blow their already-quite-fast two-day shipping out of the water.

Currently, shipping costs are still quite expensive. Improving efficiency through AI integration and automation will mean big reductions in shipping costs and increases in delivery speed. Optimization opportunities in supply chain management, vehicle maintenance, and inventory will also make shipping faster, easier, and more environmentally friendly.

13. Digital Personal Assistants

Imagine never needing to worry about preparing dinner, because your personal assistant knows what you like, what you have in your pantry, and which days of the week you like to cook at home. Imagine that when you get back from work, all your groceries are waiting at your doorstep, ready for you to prepare that delicious meal you’ve been craving. You even have a bonus recipe for a new dessert you’ve been meaning to try.

Digital assistants are getting smarter by the year. Companies such as Amazon and Google are pouring billions of dollars into making digital assistants even better at speech recognition and learning about our daily routines, opening the door to more and more complex tasks.

14. Brick and Mortar and AI

Georges Nahon, CEO of Orange Silicon Valley, foresees a time when people will no longer need to wait in line at a store. Observing how tech and retail are merging, like Amazon and Whole Foods, he says: “Thanks to AI, the face will be the new credit card, the new driver's license and the new barcode. Facial recognition is already completely transforming security with biometric capabilities being adopted…”

While some people claim that e-commerce and the Internet will completely eat away the traditional retail market, the more likely scenario is that they will arrive at some sort of equilibrium. However, it’s undeniable that even the biggest traditional retail giants are starting to adopt AI-powered technologies to gain a competitive edge.

15. Customized News and Market Reports

According to Reg Chua, COO of Reuters News, technologies are close to providing customized news and market reports, and newsrooms are starting to embrace the possibilities. Can you imagine getting market reports that were written on demand for you and not just when the market closed?

Instead of a generic recap of market performance, your customized report compares how your portfolio performed against the broader market, citing key reasons why. For example: “It's 3:14 pm. The market is currently up 2%, but your portfolio is down 3%. This is attributed in part to the purchase of XYZ stock last week, which has fallen sharply since …”

While the most obvious application of this technology would be in the finance and investing space, there are plenty of other domains that would benefit as well, including ad tech, agriculture, sports, and more.

The Bottom Line

As many people have wisely observed, the dream of artificial intelligence is not new. It has been around since the very earliest days of computing. Pioneers have always imagined ways to build intelligent learning machines.

Currently, most promising approach of AI is the use of applied machine learning. Rather than trying to encode machines with everything they need to know up front (which is impossible), we want to enable them to learn, and then to learn how to learn.

Machine learning’s time has come, and it is in the process of revolutionizing all of our lives.

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