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Artificial intelligence and machine learning are two of the hottest buzzwords in the technology world. They have become so pervasive in our lives that we dont even realize that we are using AI and machine learning several times a day. We often use the two terms interchangeably, without realizing that they are not exactly the same thing. In this AI vs machine learning comparison, lets delve into what they are and how they differ.

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Technology heavyweights such as Google, Facebook, Tesla, Apple, and Microsoft are pouring billions of dollars every year to improve the AI and machine learning capabilities of their products and services. AI is used in all sorts of things ranging from robots that are stealing our jobs to web searches and more.

Artificial intelligence is an umbrella term, which means the artificial ability to think. AI algorithms can mimic the cognitive functions of humans to perform a given task in an intelligent manner. AI can be integrated into a system to give machines the cognitive ability to perform tasks.

AI works on if-then statements, which are rules established by programmers. The if-then statements are often called rules engines or knowledge graph. Depending on the purpose its built for, the AI will take up the information you provide, process it based on pre-determined rules, and give you the output.

There are a number of ways AI algorithms can simulate human intelligence. Your smartphone, bank, smart speaker, smart TV, and other items use artificial intelligence on a daily basis. It promises to bring major changes to medical diagnosis, entertainment, self-driving cars, and more in the next few years.

Machine learning is a subset of AI, meaning all ML is AI but not all AI is ML. Unlike the knowledge graph and rules engines of AI, machine learning is capable of learning from the experiences/data its exposed to. It can modify its own algorithms to evolve without requiring any human intervention.

Think of ML as a newborn baby. As its exposed to different experiences, it begins to form its own understanding of the world, and adjusts itself continuously to thrive in that world. The machine learning algorithms try to minimize error and maximize accuracy.

Technology giants such as Nvidia, Google, Amazon and Microsoft are focusing much of their efforts on machine learning. It will increasingly give machines the ability tothink like humans.

Machine learning is currently being used in self-driving cars, web searches, emails, smart speakers, medicine, genetics, and much more. The ML programs are helping marketers better understand consumer behavior. They are also helping scientists discover how the human genome works.

Googles services such as web search, email, and Maps use machine learning to offer a personalized experience. Gmails algorithms can predict and show you what youll likely reply to an email. Its not always accurate, but its getting better with time as Googles machine learning algorithms continue to learn from billions of email communications.

Artificial intelligence makes machines smart, giving them the ability to mimic cognitive functions of humans. Machine learning is the enabler for AI, allowing the programs to constantly learn and tweak their own algorithms to get better over time. Machine learning has become the fastest-growing subset of AI.

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AI vs machine learning: What is the difference between them? - ValueWalk

Even before autonomous vehicles become a regular sight on our streets, modern cars are quickly resembling sophisticated computers on wheels.Increasingly connected vehicles come with as many as 150 million lines of code, far exceeding the 145,000 lines of code required to land Apollo 11 on the Moon in 1969. Self-driving cars could require up to one billion lines of code.For manufacturers, passengers and repair shops alike, vehicles running on software rather than just machines represent an unprecedented world of highly complex mobility. Checking the engine, tires and brakes to find a fault will certainly no longer suffice.Seeking to build trust in the new generation of automotive innovation, Tel Aviv-based start-up Aurora Labs has developed software for what it calls the self-healing car a proactive and remote system to detect and fix potential vehicle malfunctions, and update and validate in-car software without any downtime.(From left) Aurora Labs co-founder & CEO Zohar Fox; co-founder & COO Ori Lederman; and EVP Marketing Roger Ordman (Credit: Aurora Labs)The automotive industry is facing its biggest revolution to date, Aurora Labs co-founder and chief operating officer Ori Lederman told The Jerusalem Post. The most critical aspect of all that sophistication and software coming into the car is whether you can trust it, even before you hand over complete autonomy to the car. It poses a lot of challenges to car-makers.New challenges, Lederman added, include whether software problems can be detected after selling the vehicle, whether problems can be solved safely and securely, and whether defects can be solved without interrupting car use. In 2018, some eight million vehicles were recalled in the United States due to software-based defects alone.The human body can detect when something is not quite right before you pass out, said executive vice president of marketing Roger Ordman. The auto-immune system indicates something is wrong and what can be done to fix it: raise your temperature or white blood count. Sometimes the body can do a self-fix, and sometimes thats not enough and needs an external intervention.Our technology has the same kind of approach detecting if something has started to go wrong before it causes a catastrophic failure, indicating exactly where that problem is, doing something to fix it, and keeping it running smoothly.The companys Line-Of-Code Behavior technology, powered by machine learning and artificial intelligence, creates a deep understanding of what software is installed on over 100 vehicle Engine Control Units (ECU), and the relationship between them. In addition to detecting software faults, the technology can enable remote, over-the-air software updates without any downtime.Similar to silent updates automatically implemented by smartphone applications, Ordman added, car manufacturers will be able to update and continuously improve software running on connected vehicles. Of course, manufacturers will be required to meet stringent regulations, developed by bodies including the UNECE, concerning cybersecurity and over-the-air updates.When we joined forces and started developing the idea, we knew our technology was applicable to any connected, smart device or Internet of Things device, said Lederman. The first vertical we wanted to start with is the one that needs us the most, and the biggest market. The need for detecting, managing, recovering and being transparent about software is by far the largest need in the automotive industry as they move from mechanical parts to virtual systems run by lines of code.Rather than requiring mass recalls, Aurora Labs self-healing software will be able to apply short-term fixes to ensure continued functionality and predictability, and subsequently implement comprehensive upgrades to the vehicles systems.The company, which has raised $11.5 million in fund-raising rounds since it was founded in 2016 by Lederman and CEO Zohar Fox, is currently working to implement its technology with some of the worlds leading automotive industry players, including major car-makers in Germany, the United States, Korea and Japan.The fast-growing start-up also has offices in Michigan and the North Macedonian capital of Skopje, and owns a subsidiary near Munich.Customers ought to start being aware of how sophisticated their cars are, said Lederman. When they buy a new car, they should want to ask the dealership that they have the ability to detect, fix and recover so they dont need to go the dealership. Its something they would want to have. Just as the safety performance of cars in Europe are ranked according to the five-star NCAP standard, Ordman believes there should be an additional star for software safety and security.There should be as many self-healing systems in place as possible to enable that, when inevitably something does go wrong, there are systems in place to detect and fix them and maintain uptime, said Ordman.Does the software running in the vehicle have the right cybersecurity in place? Does it have right recovery technologies in place? Can it continuously and safely improve over time?With these functionalities, youre not just dealing with five stars of the physical but adding another star for the software safety and security. It is about giving the trust to the consumer. Im getting a car that will safeguard me and my family as I move forward.

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Israelis develop 'self-healing' cars powered by machine learning and AI - The Jerusalem Post

Nguyen says AI can be a rather challenging type of technology for people to fully understand because its a category that contains many different types.

You cant paint it with a single brush. There are some areas that are going to be quite challenging and it is those that people tend to associate with the term. However, just because a particular AI has been built to, for example, master a game, doesnt mean it is suddenly going to make decisions totake over the human race.

Nguyen also acknowledges that people have concerns about the potential for AI to cause widespread job losses as it automates tasks that previously have required a human.

While there will be job losses, there will also be new opportunities created as a result of its use, he says. When it comes to jobs, its important to consider which ones will be affected and what alternatives there are for those involved in the change.

Everything comes with balance and we need to see both sides of the story.

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Press the right buttons on machine learning - The Australian Financial Review

Artificial Intelligence, Machine Learning and Cloud Predictions by Jerry Kurata and Barry Luijregts, Pluralsight. In this article, they share their predictions for the ways that AI, ML and the cloud will be used differently in 2020 and beyond.

This decade has seen a seismic shift in the role of technology, at work and at home. Just ten years ago, technology was a specialist discipline in the workplace, governed by experts. At home things were relatively limited and tech was more in the background. Today technology is at the centre of how everyone works, lives, learns and plays. This prominence is shifting the way we think about, use, interact with and the expectations we have for technology, and we wanted to share some reflections and predictions for the year ahead.

AI Jerry Kurata

Increased User Expectations

As users experience assistants like Alexa and Siri, and cars that drive themselves, the expectations of what applications can do has greatly increased. And these expectations will continue to grow in 2020 and beyond. Users expect a stores website or app to be able to identify a picture of an item and guide them to where the item and accessories for the item are in the store. And these expectations extend to consumers of the information such as a restaurant owner.

This owner should rightfully expect the website built for them to help with their business by keeping their site fresh. The site should drive business to the restaurant by determining the sentiment of reviews, and automatically display the most positive recent reviews to the restaurants front page.

AI/ML will go small scale

We can expect to see more AI/ML on smaller platforms from phones to IoT devices. The hardware needed to run AI/ML solutions is shrinking in size and power requirements, making it possible to bring the power and intelligence of AI/ML to smaller and smaller devices. This is allowing the creation of new classes of intelligent applications and devices that can be deployed everywhere, including:

AI/ML will expand the cloud

In the race for the cloud market, the major providers (Amazon AWS, Microsoft Azure, Google Cloud) are doubling down on their AI/ML offerings. Prices are decreasing, and the number and power of services available in the cloud are ever increasing. In addition, the number of low cost or free cloud-based facilities and compute engines for AI/ML developers and researchers are increasing.

This removes much of the hardware barriers that prevented developers in smaller companies or locales with limited infrastructure from building advanced ML models and AI applications.

AI/ML will become easier to use

As AI/ML is getting more powerful, it is becoming easier to use. Pre-trained models that perform tasks such as language translation, sentiment classification, object detection, and others are becoming readily available. And with minimal coding, these can be incorporated into applications and retrained to solve specific problems. This allows creating a translator from English to Swahili quickly by utilizing the power of a pre-trained translation model and passing it sets of equivalent phrases in the two languages.

There will be greater need for AI/ML education

To keep up with these trends, education in AI and ML is critical. And the need for education includes people developing AI/ML applications, and also C-Suite execs, product managers, and other management personnel. All must understand what AI and ML technologies can do, and where its limits exist. But of course, the level of AI/ML knowledge required is even greater for people involved with creating products.

Regardless of whether they are a web developer, database specialist, or infrastructure analyst, they need to know how to incorporate AI and ML into the products and services they create.

Cloud Barry Luijbregts

Cloud investment will increase

In 2019, more companies than ever adopted cloud computing and increased their investment in the cloud. In 2020, this trend will likely continue. More companies will see the benefits of the cloud and realize that they could never get the same security, performance and availability gains themselves. This new adoption, together with increased economies of scale, will lower prices for cloud storage and services even further.

Cloud will provide easier to use services

Additionally, 2020 will be the year where the major cloud providers will offer more and easier-to-use AI services. These will provide drag-and-drop modelling features and more, out-of-the-box, pre-trained data models to make adoption and usage of AI available for the average developers.

Cloud will tackle more specific problems

On top of that, in 2020, the major cloud vendors will likely start providing solutions that tackle specific problems, like areas of climate change and self-driving vehicles. These new solutions can be implemented without much technical expertise and will have a major impact in problem areas.

Looking further ahead

As we enter a new decade, we are on the cusp of another revolution, as we take our relationship with technology to the next level. Companies will continue to devote ever larger budgets to deploying the latest developments, as AI, machine learning and the cloud become integral to the successful running of any business, no matter the sector.

There have been murmurings that this increase in investment will have an impact on jobs. However, if the right technology is rolled out in the right way, it will only ever complement the human skillset, as opposed to replacing it. We have a crucial role to play in the overall process and our relationship with technology must always remain as intended; a partnership.

Jerry Kurata and Barry Luijregts are expert authors at Pluralsight and teach courses on topics including Artificial Intelligence (AI) and machine learning (ML), big data, computer science and the cloud. In recent years, both have seen first-hand the development of these technologies, the different tools that organisations are investing in and the changing ways they are used.

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8 Artificial Intelligence, Machine Learning and Cloud Predictions To Watch in 2020 - Irish Tech News

By Steve Dunne, Staff Writer, Workday

In a recentMcKinsey survey,only 13 percent of CFOs and other senior business executives polled said their finance organizations use automation technologies, such as robotic process automation (RPA) and machine learning. Whats more, when asked how much return on investment the finance organization has generated from digitization and automation in the past 12 months, only 5 percent said it was a substantial return; the more common response was modest or minimal returns.

While that number may seem low right now, automation is coming to the finance function, and it will play a crucial role in furthering the CFOs position in the C-suite. Research suggests corporate finance teams spend about 80 percent of their time manually gathering, verifying, and consolidating data, leaving only about 20 percent for higher-level tasks, such as analysis and decision-making.

In its truest form, RPA will unleash a new wave of digital transformation in corporate finance. Instead of programming software to perform certain tasks automatically, RPA uses software robots to process transactions, monitor compliance, and audit processes automatically. This could slash thenumber of required manual tasks, helping to drive out errors and increase the efficiency of finance processeshanding back time to the CFO function to be more strategic.

According to the report Companies Using AI Will Add More Jobs Than They Cut, companies that had automated at least 70 percent of their business processes compared to those that had automated less than 30 percent discovered that more automation translated into more revenue. In fact, the highly automated group was six times more likely to have revenue growth of 15 percent per year or more.

In the right hands, automation and machine learning can be a fantastic combination for CFOs to transform the finance function, yet success will depend on automating the right tasks. The first goal for a finance team should be to automate the repetitive and transactional tasks that consume the majority of its time. Doing this will free finance up to be more of a strategic advisor to the business. AnAdaptive Insights surveyfound that over 40 percent of finance leaders say that the biggest driver behind automation within their organizations is the demand for faster, higher-quality insights from executives and operational stakeholders.

Accentures global talent and organization lead for financial services, Andrew Woolf, says the challenge for businesses is to pivot their workforce to enter an entirely new world where human ingenuity meets intelligent technology to unlock new forms of growth.

Transaction processing is one of the major barriers preventing finance from achieving transformation and the ultimate goal of delivering a better business partnership. It's not surprising that its the first port of call for CFOs looking toward automation.

RPA combined with machine learning provides finance leaders with a great way of optimising the way they manage their accounting processes. This has been a painful area of finance for such a long time and can have a direct impact on an organizations cash flow, says Tim Wakeford, vice president, financials product strategy, EMEA at Workday. Finance spends a huge amount of time sifting through invoices and other documentation to manually correct errors in the general ledger, while machine learning could automate this, helping to intelligently match payments with invoices.

Machine learning can also mitigate financial risk by flagging suspect payments to vendors in real time. Internal and external fraud costs businesses billions of dollars each year. The current mechanism for mitigating such instances of fraud is to rely on manual audits on a sample of invoices. This means looking at just a fraction of total payments, and is the proverbial needle in the haystack approach to identifying fraud and mistakes. Machine learning can vastly increase the volume of invoices which can be checked and analyzed to ensure that organizations are not making duplicate or fraudulent payments.

Ensuring compliance to federal and international regulations is a critical issue for financial institutions, especially given the increasingly strict laws targeting money laundering and the funding of terrorist activities, explains David Axson, CFO strategies global lead, Accenture Strategy. At one large global bank, up to 10,000 staffers were responsible for identifying suspicious transactions and accounts that might indicate such illegal activities. To help in those efforts, the bank implemented an AI system that deploys machine-learning algorithms that segment the transactions and accounts and sets the optimal thresholds for alerting people to potential cases that might require further investigation.

Read the second part of this story, How Automation and Machine Learning Are Reshaping the Finance Function, which takes a closer look at how automation and machine learning can drive change.

This story was originally published on theWorkday blog. For more stories like this, clickhere.

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Automation And Machine Learning: Transforming The Office Of The CFO - Forbes