Often the words such as data, information, and insight are used interchangeably, but these words are not similar, they have different meanings. Understanding those differences can help you tailor your program to benefit your business.

What is data? How does that turn into information and what kind of insights does it yield?

These differences are confusing right! But the distinctions are the simple ones you see how they work together. Let’s see what their definitions actually mean.

What is Data?

Data is raw with unprocessed facts that we capture according to some agreed standards. Data can be in the form of numbers, images, audio, transcriptions, etc. While working on analytics projects, the first task is to go through the client’s data structure or even normalize it.

What is Information?

Information is a collection of data points that we can use to understand something that needs to be measured. It is data that is processed, aggregated into a manner humans could read and understand. The common ways to present information are through data visualization, reports, and dashboards.

What is Insight?

Insights are gained by analyzing data and information to understand and draw conclusions that can benefit the organization while decision making.  These are the final outputs of the data in a usable form.

How they work together

The data, information, and insight work together to yield a complete analytics package. In an organization the data is collected in a raw manner, later it is converted into a readable format that is the information, this information is later processed to insights which are highly valuable in making big decisions of the firm. If one is absent or inconsistent that means it can affect the overall functioning of the company.

Impact of Insight on Business Decisions

Data-driven marketing is the latest word that is heard everywhere, but insights are more powerful than that. Most of the brands already have data but couldn’t find the right insights. Insight can help in talking about the value of your business more than data does.

Any business decisions depend on insights not the raw form of data. These insights can make a wide difference in the strategies and performance of businesses. Great insights can help you overcome any kind of market problems such as competitors, and consumer behavior.  All kinds of evolutionary changes can occur only with the insights that can help decide better in the market.

According to Global Web Index, insights are four kinds, they are Human, Universal, True to Brand, and Targeted. In other words, a great insight reveals a story that is unique to the brands. Insights help the company and customers by coming up with new and fresh ideas.

The road to getting out valuable insights is through adequate research. Here are a few steps that can help you put information for the benefit of your business.

Goal: Set the right goal and objectives for the organization to achieve through the research. This can ensure you understand better what the company actually needs.

Collect: Once you’re done researching, the next step comes a collection of the data, whether through quantitative or qualitative.

Analyze: Here the collected data is analyzed to get valued insights. It can be about your customers, employees, products, and satisfaction.

Action: The last and final step comes where the insights are gained from analysis and these are put into action by the businesses which can help in a better decision-making process.

Now, hope you got what is the difference between data vs information vs insights and also understood how insights can impact business decisions.

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Understanding how to differentiate between data, analytics, and insights and how they work.

Data. Analytics. Insights. These three are the most important food for the soul of business today. The current scenario is more transformational and technology-dependent, where data is known as the digital currency. Data is driving business intelligence through advanced analytics and by deriving intelligent insights. These three aspects are interconnected and they build off the process of generating insights from data. Without data, analytics cannot be performed and hence, insights cannot be interpreted. However, several times people confuse these terms, using them interchangeably. This might dissolve the actual significance of the terms and what they stand for. Hence, here is an effort to distinguish them and understand their meanings interdependent of each other.

Data: The Cornerstone of Business Intelligence

Business intelligence and digital transformation sound snazzy and stylish since it involves cutting-edge technologies like Artificial Intelligence and Data Science. In order to start a transformation, you need to lay the groundwork, and data does that job. Today, there is an abundance of data that are obtained from a wide variety of sources. A singular data point might seem not useful, but when it is presented collectively, it is possible to define patterns and meaning from them. Big data is the exponentially growing huge amount of data that has become the foundation of businesses across industries. This includes data from consumers, users, clients, general audience, internet, media, and many other data points.

Businesses collect data differently according to their niche and needs. Hence, data should have a context so that meanings can be derived. Business organizations should deploy smart infrastructures and experts to collect and process the unstructured, raw data. Maintaining a data flow and storing the huge loads of data have become great challenges these days.

Analytics: Discovering the Meaning

We discussed how singular data without a context can be senseless. Analytics steps in here. It enables us to find patterns and meanings from the huge datasets and makes it sensible. Disruptive technologies have made analytics more advanced to gain better business decisions. Unlocking the real value from data is not possible without analytics. Analytics experts help in processing the collected data and translating it into a comprehensible form. With analytics, an organization can understand how it is performing over time and deliver patterns of consumer behavior and market trends.  Arriving at important marketing and business decisions is impossible without analytics.

Let us take an example of a business trying to sell a product. For this, they have to first identify the demographic they are going to serve, then find their interests and behavior patterns, establish a strategy to get the product into the market, and later measure its impacts and effects. All this is possible by deploying analytics on the data collected. For online websites, measuring traffic is a significant step today and analytics completes this demand.

Insights: Taking Actions with Decisions

Let us continue from the same example. The same product that you were planning to launch has now been provided with analytical insights. It now shows what are the unique customer behaviors, the perfect time to launch your product in the market, and highlights the risk elements involved in the process. These are called insights, or analytics insights. They are the value derived from combining data and analytics. These are powerful comprehensions that can enhance business growth, customer traction, and predict risks.

Insights are like the edible form of food you get after you add the ingredients and cook them in specific directed conditions. Here, the ingredients are data and the preparation process is the analytics performed on them.

Data-Driven Business Intelligence

Data and analytics will seem invaluable if they cannot deliver meaningful insights. Disruptive technologies like AI and machine learning play a potential role in enabling data-driven business intelligence. Insights should be comprehensible and actionable, which can improve the business and accelerate its transformation. These data-driven insights are the reason behind banks being able to detect frauds, healthcare providers being able to diagnose from medical images, manufacturers being able to smartly manage the supply chains, etc.

Data intelligence is entering into new horizons with the help of disruptive technologies providing new avenues for businesses. Today’s customer-centric businesses are surviving on the data insights to understand and engage with their customers. Businesses need to find the meaning and decisions from data analytics and the insights they derive. As the great American mathematician, John Wilder Tukey has said, “The greatest value of a picture is when it forces us to notice what we never expected to see.”

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A look at the best machine learning companies that are profitable for your business

Machine learning is an advanced artificial intelligence technology that can learn from data using algorithms and then make predictions. The growth of machine learning in recent years has been significantly driven by a boundless quantity of data, affordable data storage, and the evolution of more powerful processing. As organizations are increasingly stepping to leverage robust ML models, finding the best machine learning companies that deliver all the features is quite difficult.

To avoid this hunt for your business, Analytics Insight has listed the top 10 machine learning companies that are worth looking at in 2021.

Iflexion

Founded in 1999, Iflexion delivers software development and related IT services. The Austin, Texas-based company provides full-cycle services in the areas of content management solutions, portals, e-commerce, web-based enterprise solutions, media content distribution, social software. Iflexion has built a custom online payment solution, a supporting CRM, a payroll system, and a standalone mobile app for a financial tech company.

iTechArt Group

iTechArt is a top-tier, one-stop custom software development company offering software and application development, modernization, migration, QA & testing services. Founded in 2002 and headquartered in New York, the company does not only make software AI-enabled or blockchainable, but makes it work for companies’ business goals and target market.

Talentica

Talentica is a Pune, Maharashtra, India-based innovative outsourced product development company. It helps small and mid-sized technology companies take advantage of global talent to reduce time to market and R&D costs. The company engineers practical data-driven algorithms to power machine intelligence for startups by separating the AI hype from computational realities.

UruIT

UruIT is a nearshore software development company. It is developing a new way of creating and extracting metadata across the enterprise. UruIT delivers machine learning capability for searching, identifying and organizing data in the entertainment industry. The company’s major clients include Disney, Warner, BBC, FOX News, and HBO. Huge players in the market rely on this platform to search across multiple sources of media content faster and better by using Machine Learning.

DeCypher DataLabs LLC

DeCypher DataLabs LLC, is a Chicago-based boutique business practice that specializes in all Advanced Analytics and Machine Learning matters for US government agencies, commercial enterprises, and non-profit organizations. The company provides AI solutions that are hosted on the AWS platform and integrate AWS into custom-developed solutions. DeCypher DataLabs guiding principles are based on Truth and Data Ethics.

MobiDev

MobiDev is a Georgia, United States-based software development company. It creates complex business-driven solutions, with a focus on innovation and transparency of actions, guaranteed product delivery, and ongoing evolution. The company’s main areas of expertise include Industrial IoT and Augmented Reality, Data Science & Machine Learning, Blockchain & distributed databases, Microservices & cloud infrastructure, Native mobile & desktop development, and Cross-platform solutions.

Neoteric

Neoteric is a Poland-based end-to-end software product development company helping startups and enterprises. The company’s services include Web app development, AI-driven solutions, SaaS app development, Product design, and RPA. Neoteric supports companies with end-to-end software development leading the tech from product idea, through prototyping to launch.

Belitsoft

Outsourcing software development company Belitsoft is committed to providing high-quality software products and services. The company focuses on web portals and applications, e-commerce, and more. Based in Minsk, Belarus, Belitsoft provides a wide variety of services. These include custom software development, automated software testing, maintenance & support, quality assurance and more.

Netguru

Poland-based custom software development company Netguru builds digital products that allow people do things differently. The company provides machine learning solutions to businesses to gain a competitive advantage. Netflix saves US$1 billion each year thanks to machine learning algorithms. Its recommendation engine uses dozens of algorithms to compare viewers’ preferences with similar customers around the world.

Unicsoft

Unicsoft is a full-cycle custom software development delivering AI and Blockchain solutions to drive business outcomes for startups and enterprises. The company has delivered more than 200 projects incorporating Blockchain and AI, IoT and Machine Learning, Web and Mobile Development. Unicsoft also delivers its day-to-day services with transparency, clarity, and most importantly, care for its customers.

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Source – https://www.analyticsinsight.net/

Big data and data science technologies have data as their core content and perform various actions

Ever wondered whether to choose big data or data science? If you are into data and a tech geek, you might have come under such a dilemma at least once. In the digital world we live in, data is increasingly becoming the most valuable asset for organizations. It won’t be a surprise if it crosses the price of gold one day. But to explore every bit of data, we need more than just the basics. Big data and data science technologies have data as their core content and perform various actions.

Even though big data and data science are two different technologies, they are interlinked with each other on the grounds of data. Both technologies play a big role in digital evolution. More and more companies across various domains are adopting big data and data science to enhance the routine. Since data is rapidly transforming the way we live and communicate, big data and data science application help collect, sort and study data to improve organizations’ performance. Data science is an extension of statistics that deals with large datasets with the help of computer science technologies. On the other hand, big data engages with the vast collection of heterogeneous data from different sources. In this article, we’ll undo every knot and reveal the difference between data science and big data.

Definition 

Big data represents a large set of data, both structured and unstructured, that inundates business on a day-to-day basis. The data is very large in size that none of the traditional data management tools can store it or process it efficiently. But the massive amount of data can be used to address business problems that humans find difficult to tackle with simple calculations.

Data science is a domain that deals with vast volumes of data to derive meaningful information and make business decisions. Data science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from raw data. The term ‘data science’ was coined in 2008 when companies realized the need for data professionals who are skilled in organizing and analyzing massive amounts of data.

Concept

Big data holds diverse data types generated from multiple data sources. Henceforth, big data approach can’t be easily achieved using the traditional data analysis method. Instead, unstructured data requires specialized data modelling techniques, tools and systems to extract insights and information as needed by organizations.

Data science is a specialized field filled with intelligent data capture techniques, data cleansing, mining and programming to prepare and align big data for intelligent analysis to extract insights and information. Data science is comparatively a challenging area due to the complexities involved in combining and applying the different methods, algorithms and complex programming techniques to perform intelligent analysis in large volumes of data.

Applications

Big data in financial services: Financial services like credit card companies, retail banks, private wealth management advisories, insurance forms, venture funds and institutional investment banks gather a lot of data every day. In order to make the data valuable, they use big data to address common problems. Unfortunately, the data are multi-structured data living in multiple disparate systems, which only big data can manage. Entities perform customer analytics, compliance analytics, fraud analytics and operational analytics to mitigate financial issues.

Big data in gaming: Online sources are the big generator of data. Especially, the gaming industry is a massive creator of big data. A single frame of an online game can require 100mb of data to render. Think about how much data is generated every day just in the gaming industry. Yes, it goes beyond uncountable.

Big data in healthcare: With the healthcare sector gaining more attention, organizations and executives working in the industry find technology as a solution to accelerate the medical processes. Hospitals and medical service providers store big data to analyze and perform tasks like track and optimize patient influx, track the use of equipment and medicines in the facilities, organize patient information, etc.

Data science in recommendations: Recommendation systems are increasingly becoming common in the modern world. We come across recommendations systems every day and find them amazing. Even before we look for more content, the online recommendation systems suggest what we might like. This is used as a marketing method of promoting products to consumers. Scores of companies are already using recommendation systems to enhance their sales.

Data science in advertising: Digital ads have click-through rates that differentiate them from traditional advertisements. Henceforth, flashing the right ad at right time and right place is very important in online advertisement campaigns. Digital marketers use data science algorithms to display banners and digital billboards where it gets maximum viewership.

Data science in internet search: Since internet is the prophet of the digital society, we search for everything online. Fortunately, we get relevant content most of the time. Data science is being applied to online search engines to make us get the outcomes we expect for. It goes through our previous browsing history and filters the results according to our routine search.

Job responsibilities

Big data engineers’ core functions are similar to that of data engineers’. Data engineers should design the architecture of a big data platform, maintain data pipeline, customize and manage integration tools, databases, warehouses, and analytical systems, manage and structure data, and set up data-access tools for data scientists. Some of the common big data careers are,

• Big data engineer

• Big data analyst

• Data visualisation developer

• Business analytics specialist

• Machine learning scientist

Data scientists work closely with business executives to understand their goals and determine how data can be used to achieve those goals. They design modelling processes, create algorithms and predictive models to extract the data the business needs, and help analyze the data and share insights with peers. In general, data scientists are entitled to ask the right question to begin the discovery process, acquire data, clean and store it, explore data analysis, apply data science techniques, etc. to improve business functionality. The most common careers in data science are,

• Data scientist

• Data analyst

• Data architect

• Data engineer

• Business Intelligence specialist

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While many industries are struggling amid the coronavirus pandemic, both the IT industry and the broader trend of transition to remote work have revealed many areas where traditional approaches to managing businesses create unnecessary waste. Still, data science and its subdivision – machine learning – reveal that such expansion is nearly limitless.

Machine learning uses powerful algorithms to discover insights based on real-world data that can then be used to make predictions about future outcomes. As new data comes available, machine learning programs can automatically adapt and produce updated predictions. As with any tool, machine learning is not a silver bullet. However, there are many situations in which the technology can outperform linear and statistical algorithms.

Here are five of the most common use cases where machine learning can make a big difference:

When engineers can’t code rules for certain problems

Many human-oriented tasks (such as recognising whether an email is spam) aren’t solvable using simple (deterministic), rule-based solutions. Because so many factors may influence an answer, engineers would have to write and frequently update billions of lines of code. In addition, when rules depend on too many factors, and when those rules overlap or need fine-tuning, it becomes difficult for humans to code precise rules. Fortunately, machine learning programs don’t require users to encode actual patterns. These programs only need proper algorithms to extract patterns automatically.

When you need to scale a solution to millions of cases

You might be able to manually categorise a few hundred payments as either fraudulent or not. However, this becomes tedious or impossible when dealing with millions of transactions. As user bases grow, it’s no longer feasible for organisations to process payments by hand – end-users today want answers about their money in milliseconds, not minutes or hours. Machine learning solutions are effective at handling these types of large-scale problems with little or no human intervention.

When you can do it manually, but it’s not cost-efficient

There are situations in which in-house experts could process many requests quickly and accurately but at a high cost. For instance, imagine you assess DMV forms for in-state and cross-state car purchases to determine their validity before passing them on. In this situation, the business processes are well-defined, optimised, and serialised. It may take only a few minutes to check each form thoroughly. But allocating so much manual labor to this work is likely not the best use for your budget. Machine learning, on the other hand, offers predictable, pay-as-you-go pricing for fully scaled operations.

When you have a massive dataset without obvious patterns

Consider this – you’ve successfully prepared a well-curated dataset and know the underlying problem. However, you don’t see any explicit patterns in the data, preventing you from encoding those validations. Plus, there are many typos, missing fields, and other human-caused errors with no validation in place. You may even know the data is poor quality and can manually determine every affected row. But you can’t see any actual connections between valid and invalid records. Machine Learning algorithms can solve this problem. They can find hidden connections between data points that aren’t clear to humans. Tools like Interpreting Tracers can even describe how machine learning models arrive at their conclusion.

When you live in an ever-changing universe (adaptive)

The world, and its problems, are always changing. A problem you solved yesterday can easily mutate into something else entirely, rendering your previous solution inefficient or even useless. For example, if your organisation processed medical appointment recordings to extract diagnoses, procedure information, and billing codes, your rules might have to evolve constantly. However, you can’t make updates in real-time 24/7. Meanwhile, incorrectly labelled items could lead to insurance rejections, huge fines, and legal penalties. One major advantage of machine learning methods is that they can learn from data across the entire lifecycle of your application – from the first line of code written to the moment when the model is finally shut down. Moreover, it’s important for production-grade systems to have feedback loops so that you can catch the moment when your model no longer solves problems correctly.

It’s important to remember that machine learning is a tool – it’s not magic. Machine learning models are essentially advanced math-based algorithms, which identify patterns in data and learn from them. However, when properly applied to the right use cases, machine learning can reduce the amount of time spent error-prone manual IT operations, adding significant business value and greatly reducing IT costs.

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Artificial Intelligence, Machine Learning, and Deep Learning are terms that often overlap with each other and are easily confused. Let’s discuss all three in detail and go through their applications and uses.

Artificial Intelligence

Have you ever noticed how effortlessly we calculate the environment around us and keep learning from past experiences? Well, Artificial Intelligence (AI) is a method to teach a computer the same thing.

Artificial Intelligence is used to build tools, agents, bots, and robots that can predict human behavior & act on a human basis. Tesla’s auto-driving cars, Amazon’s Alexa, and Siri are all examples of Artificial intelligence.

AI has three different levels:

First, Artificial Narrow Intelligence (ANI) is the only type of AI we have successfully accomplished to date. ANI is designed to perform singular tasks & is goal-oriented. ANI is very capable of completing specific tasks it is programmed to do. A few examples of ANI are voice assistants, facial recognition, or driving a car.

Second, Artificial General Intelligence (AGI) is the concept of a machine with general intelligence that can mimic human intelligence and behaviors, with the ability to learn from data and apply its intelligence to solve any problem. Artificial General Intelligence can think, understand, and act in a somewhat similar way to a human in any given situation.

Artificial Superintelligence (ASI) is the hypothetical where machines can become self-aware and surpass human ability and intelligence. Practically, we are far away from achieving this form of AI in real life.

Machine Learning

While Artificial Intelligence is a concept of imitating human abilities, Machine Learning is a subset of Artificial Intelligence that teaches a machine to learn from previous outcomes.

Machine learning models look for patterns in the data and try to conclude you or I would based on previous outcomes and data. And once the algorithm gets really good at drawing outcomes, it starts applying the knowledge to the new data sets and keeps improving.

In a nutshell, Artificial Intelligence is the science of computers copying human behavior, while Machine Learning is the method behind how machines learn from data.

Types of Machine Learning

Supervised Learning is when a large amount of labeled data is fed to the algorithms, and variables that the algorithm needs to assess for correlations are also defined. However, supervised learning needs a vast pool of data to master the tasks.

Unsupervised Learning helps the algorithm look for patterns and data sets that don’t have labeled responses. You would use this technique to explore your data but don’t yet have a specific goal. The algorithm scans the data sets and starts segregating data into groups based on characteristics they share.

The mix of supervised & unsupervised learning is called semi-supervised learning. In semi-supervised learning mostly labeled data is fed to an algorithm, yet the model is free to explore & develop its own understanding of the data set.

Reinforcement learning is teaching a machine to complete a multi-step process with clearly defined rules. The algorithm takes its own decisions along the way & gets rewards or penalties for the actions it takes

Deep Learning

It would not be an exaggeration to say that deep learning is a technique for implementing Machine Learning. Deep Learning is a subset of machine learning that uses deep neural networks and imitates the network of neurons in a brain, and allows machines to make accurate decisions without humans’ help.

However, deep learning is sometimes seen as an evolution of machine learning. The depth of a model is represented by the number of layers it has. Deep learning is the new state of the art in terms of Artificial Intelligence. In deep learning, the training is done through a neural network.

Deep learning has empowered many practical applications in Artificial Intelligence. Self-driving cars, better healthcare, even better product recommendations are all here today or on the horizon.

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In today’s society, it can be hard to operate without relying on technology one way or another. Electronics have become an essential part of our daily operations. It seems we all use technology for productivity and communication.

Can you imagine what would happen if we all stopped relying on technology all of a sudden? The world would be chaos at first, which further proves how much society depends on technological innovation.

One of these innovations revolves around artificial intelligence (AI). Though it used to only be in science fiction novels, AI is now a true venture for many businesses of today, including my own. In addition, much research is also being done regarding artificial general intelligence (AGI, or general AI), which is a more specific branch.

A Lot Of Research And Development Still Needs To Be Done

Before we dive too deep into AI, it’s important to note that this is still a new field of research. Scientists and AI experts everywhere are still developing the best programs and innovations they can think of. It might be a long time before we reach the “end” of AI development.

The good news is that many businesses are taking advantage of the developments already made. As a matter of fact, 72% of business leadersconsider AI development as an essential part of their business’s future success.

Since the subject is still new, some definitions are still fluid to an extent. When we talk about AI, for example, many experts would include AGI in the category of AI. Others, though, would claim there is a distinct difference.

It might be easy to think about AI as a broad field, while AGI is a more specific focus within it. General AI applies some of the same concepts, even. Below are the two distinctly separate definitions that the industry has come to generally accept.

AI Is Based On Human Cognition

Many would argue that AI itself is centered around performing cognitive tasks that every human can perform. These tasks include things like predictive marketing or complex calculations. Sure, a human could perform them, but allowing machine learning to sift through data on our behalf saves us valuable thinking power.

In fact, many businesses are starting to incorporate AI innovations. What’s one of the top reasons they’re now considering the technology? Well, most of them agree that possibilities in marketing could be perfect for AI technology.

AI, in essence, is designed to make life easier for humans in their daily lives. This design is programmed to be useful from the outset.

In other words, AI functions are preprogrammed beforehand. The “decisions” machine learning makes are logical ones based on empirical data. The goal of general AI, though, is to take these decisions a step further.

General AI Is Based On Human Intellectual Ability

General AI might be considered to fall under the umbrella of AI as a whole. It’s sometimes referred to as strong AI or strict AI. That’s because general AI expects the machine to be equally as smart as a human.

General AI would expect a machine to perform functions that are now only seen in science fiction robots. We don’t have a machine available, for example, that could walk into a home and do laundry for the entire household.

The number of decisions and intellectual energy require are still too far-fetched. Sure, a machine might be able to locate laundry baskets and sort the clothes by color. What about random clothing items that were thrown around a teenage boy’s untidy room, though? Or, how would the machine know which items are only for dry-cleaning? Some decisions that humans take for granted would overwhelm a simple machine’s mind.

Another case would be a decision in which “human instinct” comes into play. For example, sometimes we go with our “gut” to determine which food product to purchase at the store. A machine might not care about a brand name as much as the lowest priced item.

In other words, if it can’t be directly programmed into a machine, odds are that it won’t be able to make heavy intellectual decisions. This ability still is reserved for the part within all of us that is “human.”

Don’t Forget About Superintelligence

There is yet another category under AI as a whole that might be of interest. This would be “superintelligence,” which is also only a part of science fiction still.

Such superintelligence is more of a general fear of those who don’t fully understand the limits of real AI technology. These people are concerned that AI could someday surpass all human intelligence. While it makes for a great adventure movie, superintelligence is not at present a realistic concern for experts.

How Can AI Or General AI Benefit Businesses Today?

As mentioned above, many business leaders are starting to appreciate the possible applications of AI. Since the field is still fresh, no one knows just to what extent those applications could assist us.

Humanity has always been optimizing and automating business operations to reduce corporations’ bottom lines. As this displacement of the workforce might be frightening, it still opens up endless productive possibilities for everyone.

Technology and innovation deserve to be given a fighting chance to truly benefit humanity. A solid understanding of AI is beneficial for all professionals these days. Some professionals dedicated to AI and its progress continue to push for the spread of this exciting technology.

Stay Informed About Technology And AI Innovations

Such a broad field of research deserves to be thoroughly explored for the benefit of humanity. All kinds of perspectives and expertise could expand the possibilities of general AI innovation. It’s important to stay informed and updated on the progress so you don’t get left behind in the modern business world.

Continue researching and learning about AI and technology. The potential applications of the field might end up benefiting your ventures someday.

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Artificial intelligence is everywhere – although often the reality feels somewhat underwhelming compared to the potential for what it might become. AI has the power to change the world, but it’s a gradual process.

Machine learning is often used as a synonym for artificial intelligence, but it’s actually a different, albeit related discipline. While artificial intelligence refers to a computer program able to “think” for itself without programmed instructions, machine learning is one process by which a computer can learn its trade.

The philosophy behind machine learning is similar to how you or I learn about something: by experiencing it. The difference is that for now at least, machines are specialised in learning about one or two things at a time.

Let’s take a hypothetical photos app that allows you to search its content. Type in the word “cat” and it shows pictures of cats even though there’s no labelling of the files themselves. How does the app know what cats look like? In short, it’s picked it up through machine learning. That is to say that the computer program was fed thousands of pictures of cats, and it began to notice patterns of what a cat looks like. Just as you might begin to spot similarities (tail, whiskers, ears), so does the machine, until eventually, you can show it random pictures taken from the internet, and it will be able to tell you if there’s a cat in the scene or not. Teach the same AI to recognise buildings, dogs, pizzas and people, and you’ve got a program that feels a bit like witchcraft.

This kind of machine learning is happening all around you, and you’re feeding various companies training data all the time. It’s the way that Google can predict typos in search boxes, and how Netflix knows the kind of shows people like you might enjoy watching.

That’s undeniably useful, but the world-changing aspects of machine learning is where the AI can learn to spot things that humans can’t. Earlier this year, I interviewed Saffron Technology’s CEO, Gayle Sheppard. Of the many examples she gave of how her company’s AI is innovating in the airline, health and banking sectors, one example sticks in my mind. By training the artificial intelligence with echocardiograms from patients with restrictive cardiomyopathy and constrictive pericarditis, the AI was able to spot the difference 96% of the time in two months. Human eyes get it right between 50 and 75% of the time.

In this instance, it was artificial intelligence which made the eventual diagnosis, but it couldn’t be done without the training. Machine learning is just one of the ways in which scientists are ensuring that artificial intelligence is as intelligent as it can be.

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From personal assistants like Siri, to movie suggestions on Netflix, artificial intelligence (NYSE:AI) is rapidly becoming ubiquitous in everyday life. As this technology continues to advance in capability and prevalence, we sought to explore AI and several closely related subtopics: machine leaning, deep learning, and neural networks.

What are the Differences between Artificial Intelligence, Machine Learning, and Deep Learning?

While artificial intelligence (AI), machine learning (ML), and Deep Learning (NYSE:DL) are often used interchangeably, there are several key differences. One way to visualize the relationship is through a series of concentric circles. AI is the macro topic which encompasses the entire field of study, while ML is a subtopic within AI. DL is a further refinement of ML and represents the most cutting edge of AI applications that are being used today.

At a basic level, artificial intelligence is the concept of machines accomplishing tasks which have historically required human intelligence.1 AI can be broken down into two distinct fields:

Applied AI: Machines designed to complete very specifics tasks like navigating a vehicle, trading stocks, or playing chess – as IBM’s Deep Blue demonstrated in 1996 when it defeated chess grand master Gerry Kasparov.

General AI: Machines designed to complete any task which would normally require human intervention. The broad nature of General AI requires machines to “learn” as they encounter new tasks or situations. This need for a learned approach is what gave rise to modern Machine Learning.

Today, many firms at the cutting edge of AI are focusing on machine learning (ML). In simple terms, ML is the process of building machines which can access data, apply algorithms to this data, and then train themselves to deduce valuable insights based on these underlying datasets.

The key difference between ML and AI is that ML does not rely explicitly on the code of its creator. Rather, ML systems use computer code as a starting point and then gather data, information, and inputs which can be studied – similar to how a student might study for an exam. It is this relationship with big data that makes ML and the Internet of Things(connecting regular objects to the internet so they can collect data or be controlled remotely) so closely intertwined.

Currently, ML is typically used to recognize faces, voice commands, and objects, as well as to translate languages. It has been successfully implemented in chatbots, such as Siri (Apple), Cortana (Microsoft), and Alexa (Amazon). With the victory of Google’s “Deep Mind” over the Go world champion in 2016, ML is now increasingly becoming accepted as a useful tool for decision making in the corporate world.

Deep learning takes artificial intelligence a step further, by mimicking how the human brain works through the use of artificial neural networks. In an artificial neural network, each neuron is charged with providing a binary (yes/no) response to basic questions about a piece of data. By layering thousands (or millions) of these artificial neural networks, a Deep Learning machine is able to generate reliable outputs (recommendations or interactions) without changing the underlying coding.

Consider a very basic artificial neural network which is responsible for determining if a photo contains a banana or an apple. The network has three neurons which are responsible for answering:

1.  Is the object in the picture round?
2. Is the object in the picture yellow?
3.  Does the object in the picture have a stem?

The network would respond with no, yes, no for the photo of a banana and yes, no, yes for the photo of an apple. Using binary, the network would learn that a banana is 010 and an apple is 101. Extrapolate this concept across thousands of yes/no questions of exponential complexity and you have the bases of artificial neural networks and deep learning.

Apart from being used in image and voice recognition algorithms, companies are implementing deep learning to predict customer preferences, detect fraud and spam, fight malware, conduct life-saving diagnoses, and recognize handwriting. In many ways, the possibilities for this technology are endless.

What’s Ahead?

Gartner’s recent study projects that by the end of this decade, the average person will have more conversations with a virtual assistant or bot than with his or her immediate family.6Such penetration of artificial intelligence into our everyday lives will depend on further advances in the technology to become smarter, more capable, and easier to interact with. While many expect this progress to occur from advances in machine learning and deep learning, there are new techniques to being introduced as well.

It’s with this momentum in mind that we developed our Robotics and Artificial Intelligence ETF (NASDAQ:BOTZ). The fund seeks to invest in companies that can potentially benefit from increased adoption and utilization of robotics and artificial intelligence.

1. https://www.forbes.com/sites/bernardmarr/2016/12/06/what-is-the-difference-between-artificial-intelligence-and-machine-learning/#35aa37802742

2. https://www.leverege.com/blogpost/the-difference-between-artificial-intelligence-machine-learning-and-deep-learning

3. http://www.techrepublic.com/article/machine-learning-the-smart-persons-guide/

4. http://www.techrepublic.com/article/7-companies-that-used-machine-learning-to-solve-real-business-problems/

5. http://www.explainthatstuff.com/introduction-to-neural-networks.html

6. http://www.gartner.com/smarterwithgartner/gartner-predicts-a-virtual-world-of-exponential-change/

Investing involves risk, including the possible loss of principal. The investable universe of companies in which BOTZ may invest may be limited. The Fund invests in securities of companies engaged in Information Technology which can be affected by rapid product obsolescence, and intense industry competition. In addition to normal risks associated with investing, international investments may involve risk of capital loss from unfavorable fluctuation in currency values, from differences in generally accepted accounting principles or from social, economic or political instability in other nations. The fund is non-diversified which represents a heightened risk to investors.

Shares are bought and sold at market price (not NAV) and are not individually redeemed from the Fund. Brokerage commissions will reduce returns.

Carefully consider the Fund’s investment objectives, risk factors, charges and expenses before investing. This and additional information can be found in the Fund’s full or summary prospectus, which may be obtained by calling 1-888-GX-FUND-1 (1.888.493.8631), or by visiting globalxfunds.com. Read the prospectus carefully before investing.

Global X Management Company LLC serves as an advisor to Global X Funds. The Funds are distributed by SEI Investments Distribution Co. (SIDCO), which is not affiliated with Global X Management Company LLC. Global X Funds are not sponsored, endorsed, issued, sold or promoted by Solactive AG, FTSE, Standard & Poors, NASDAQ, Indxx, or MSCI nor do these companies make any representations regarding the advisability of investing in the Global X Funds. Neither SIDCO nor Global X is affiliated with Solactive AG, FTSE, Standard & Poors, NASDAQ, Indxx, or MSCI.

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