5 AI Trends to Watch in 2020

| January 17, 2020

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Artificial Intelligence (AI) is embedded in our everyday lives. AI technologies have become so advanced that it’s not uncommon nowadays to be in contact with them without fully realizing it. In the enterprise, AI solutions such as Process Intelligence (IQ) have witnessed a staggering adoption rate and have significantly transformed how processes are monitored and managed across an organization. As AI continues to scale in 2020, it will continue to have a significant impact on our lives, both in the workplace and outside of it.

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Technology Planning: Streamline the M&A Process

Article | August 3, 2020

The M&A process is complex and nuanced. Technology compatibility, IT infrastructure longevity and overall IT security are all crucial to a successful end result. Below you’ll find the important milestones of each phase of the M&A journey and key considerations to help ensure a secure, streamlined and timely IT implementation for your organization.Consider IT transformation and determine how technology can help the company remain relevant to customers and more advanced than competitors.

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Basics of Artificial Intelligence and Machine Learning

Article | August 3, 2020

Lately, we all often come across two very hot buzzwords — Artificial Intelligence (AI) and Machine Learning (ML). Perhaps the impact of artificial intelligence and machine learning on today’s business world is more than our daily lives. According to a Bloomberg report, around $300 million were invested in 2014 to promote AI-powered startups. It was 300% more than the previous year’s investment in venture capital. It’s hard to deny the fact that artificial intelligence and machine learning are all around us. Whether it is about protecting confidential information at work or just playing your favourite games on PS5, AI and ML are there. Researchers, scientists, computer engineers, and analysts are working hard together to pass on human-like intelligence in machines so that they can think and act according to real-life scenarios. Businesses have changed their approach to AI keeping enterprise adoption in mind rather than treating it as just a research topic. Tech giants such as Google, Facebook, Microsoft have already invested billions in Artificial Intelligence and Machine Learning and already have started to reshape the customer experience. But the AI and ML incorporation we see today is just the tip of an iceberg. In the coming years, you will see them take over products and services one after another. What Is Artificial Intelligence and Machine Learning? It is nowadays common to see several companies marketing themselves as AI-powered startups even though their operations don’t really revolve around AI. To understand this type of gimmicky marketing, it is essential to first understand what Artificial Intelligence and Machine Learning are. Let’s be clear in the beginning about one fact — AI and ML are not the same things. If you think they are, kill this perception before it makes things very confusing. Both these terms crop up especially when the discussion is about the use of Artificial Intelligence in marketing, the use of Machine Learning in marketing, analytics, Big Data, and the modern-day tech that is transforming the world. To ease down the learning, here’s the best answer: Artificial Intelligence is a science used to develop systems that can mimic decision-making and behaviour like humans. In simple words, the main application of Artificial Intelligence is to make intelligent machines. Machine Learning is the subset of artificial intelligence that uses data to perform tasks. It involves designing and applying the data models or algorithms that can learn from their past experiences. There’s a subset of Machine Learning, too — Deep Learning. It counts on multilayered neural networks to perform tasks. Early Days of Artificial Intelligence The early mentions of AI trace back to Greek mythologies that have stories of a mechanical man that could mimic our own behaviour. Plus, the early computers were termed as “logical machines'' in Europe. These machines could solve arithmetic operations and even store memory. Scientists, fundamentally, were inspired by them to create mechanical brains. Over time, technology got more and more modern. And, our understanding of how the human mind works improved. Both these factors lead to the current AI revolution. Today, the use of AI is more focused on mimicking the decision-making process of humans rather than performing complex calculations. The prime motive of this is to allow machines to think and act more like humans. AI-powered machines that are designed to act intelligently come into two basic groups — General AI and Applied AI. General AIs are relatively less common and can theoretically handle any task. The most exciting improvements in the field of AI are happening in this specific area. In fact, it’s generalized AI that led to the rise of Machine Learning. On the other hand, applied AIs are designed to perform relatively smaller tasks like smartly trading shares and stocks, or guiding an autonomous vehicle to its destination, etc. The Rise of Machine Learning As mentioned earlier, Machine Learning is a subset of AI and can also be treated as the current state-of-the-art. It came into reality primarily because of the two major breakthroughs — the rise of the internet and human realization. In 1959, an American pioneer in the field of computer gaming and AI, Arthur Samual, realized that it can be possible to teach machines how to learn to perform tasks themselves rather than us telling them how to. As long as the emergence of the internet is concerned, that helped scientists with tons of digital information that could be analysed for the betterment of AI and eventually, ML. After these innovations, it was more efficient for scientists and engineers to program machines in a way that they learn to think like humans and then connect them to the internet so that they have all the needed information. Vertical AI And Horizontal AI No matter what kind of AI research it is, knowledge engineering is its essential part. Machines need plenty of information to think and act like humans. Therefore, AI needs access to objects, categories, properties, and relations between them to apply knowledge engineering. AI is responsible for generating analytical reasoning power, problem-solving abilities, and common sense in machines. And, it is not an easy task! The way AI serves us can be divided into two parts — Vertical AI and Horizontal AI. Vertical AI is used to perform single jobs such as automating repetitive tasks, scheduling meetings, etc. Vertical AI bots are so accurate in performing a single job that people often mistake them for human beings. Horizontal AI, on the other hand, can handle more than one task at the same time. The best examples of horizontal AI are Alexa, Siri, and Cortana. Different Types of Machine Learning ML can be best used to fix complex tasks such as enabling self-driving cars, face recognition, credit card fraud detection, etc. It uses huge, complex algorithms that keep on iterating frequently over big data sets. The following are the 3 major Machine Learning areas: ● Reinforcement Learning ● Unsupervised Learning ● Supervised Learning Reinforcement Learning In reinforcement machine learning, algorithms allow machines and software agents to automate ideal behaviour within a particular context to improve the performance of an overall system. It is characterised by learning problems rather than learning methods. If any method can solve a problem, it can be a reinforcement learning method. This Machine Learning technique assumes that the dynamic environment is connected to a software agent such as a computer program, bot, or robot. Ultimately, it chooses a specific action in order to rapidly deliver the most efficient result. Unsupervised Learning Due to the involvement of unclustered data, unsupervised machine learning is more complex than others. With it, the machine has to learn independently without any supervision. No fixed or correct solution is provided for any problem in this technique. The algorithm has to identify the data patterns and find the solution. The recommendation engines we see on several eCommerce websites and Facebook friend requests suggestions are the best examples of this sort of Machine Learning. Supervised Learning Training datasets are used in supervised learning. The algorithms are created in such a way that they can analyse the data patterns and develop an inferred function. The produced correct solution is then used to map new examples. The best example of supervised machine learning is credit card fraud detection. Final Words Artificial Intelligence and Machine Learning never fall short to surprise us with their exciting innovations. Their impact has reached all the industries including eCommerce, customer service, finance, education, healthcare, pharma, infrastructure security, and whatnot. Needless to say, all these industries are very keen on reaping all the benefits of Artificial Intelligence and Machine Learning. The human-like AI was an inevitable thing as most technologists thought. Today, we are indeed closer to this goal than ever. This exciting journey in the past couple of years is the result of how we predict AL and ML works. FAQs Why is AI Marketing important? With AI marketing, businesses and marketers can analyse and consolidate a large amount of data from emails, social media, and other platforms faster. The achieved insights can be used to improve campaign performance and eventually boost the returns on investment in a relatively lesser time. AI marketing is the best and the most efficient way to eliminate the risks of human errors while optimizing and streamlining the campaigns more effectively. The following benefits of AI marketing justify the attention it has received all over the world. ● A better understanding of your consumers ● Optimization of digital advertising campaigns ● Offer comprehensive customer profiles ● Allow real-time interactions with consumers ● Refined content delivery ● Reduced marketing costs ● Improved ROI Is artificial intelligence and machine learning the same? The straight answer to this question is NO. They are not the same thing. AI allows machines to learn human behaviour while ML is the subset of AI that teaches machines to learn on their own with the help of past data. Does AI need machine learning? Fundamentally, ML is not required for AI as AI systems do not need to be pre-programmed. Instead of such software agents, they get help from algorithms that can use their own intelligence to solve queries. These can be Machine Learning algorithms such as Deep Learning neural networks and Reinforcement Learning algorithms. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "Why is AI Marketing important?", "acceptedAnswer": { "@type": "Answer", "text": "With AI marketing , businesses and marketers can analyse and consolidate a large amount of data from emails, social media, and other platforms faster. The achieved insights can be used to improve campaign performance and eventually boost the returns on investment in a relatively lesser time. AI marketing is the best and the most efficient way to eliminate the risks of human errors while optimizing and streamlining the campaigns more effectively. The following benefits of AI marketing justify the attention it has received all over the world. A better understanding of your consumers Optimization of digital advertising campaigns Offer comprehensive customer profiles Allow real-time interactions with consumers Refined content delivery Reduced marketing costs Improved ROI" } },{ "@type": "Question", "name": "Is artificial intelligence and machine learning the same?", "acceptedAnswer": { "@type": "Answer", "text": "The straight answer to this question is NO. They are not the same thing. AI allows machines to learn human behaviour while ML is the subset of AI that teaches machines to learn on their own with the help of past data." } },{ "@type": "Question", "name": "Does AI need machine learning?", "acceptedAnswer": { "@type": "Answer", "text": "Fundamentally, ML is not required for AI as AI systems do not need to be pre-programmed. Instead of such software agents, they get help from algorithms that can use their own intelligence to solve queries. These can be Machine Learning algorithms such as Deep Learning neural networks and Reinforcement Learning algorithms." } }] }

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AI TECH

AI Adoption: an advanced digital transformation process

Article | August 3, 2020

Common view is that AI software adoption is 'on its way' and it will soon replace many jobs (example self-driving cars with drivers etc.) and the majority of companies are starting to embrace the efficiencies that AI brings now. Being a practitioner of AI software development and being involved in many projects in my company AI Technologies, I always found my direct experience in the field in contrast with what the media generally portraits about AI adoption. In this article I want to give my view on how AI projects affect the work dynamics into clients work processes and compare that with the studies available on the impact of AI and new technologies on work. This should help the reader, especially if he is an executive, to set the right expectations and mentality when he is assessing the potential investment into a new AI project and if his company is ready for it. To start with, any software development project, including AI, can be summarized into 3 stages: proof of concept (POC) when the prototype has been built, product development when the software is actually engineered at scale, live support/continuous improvements. It occurs often that projects in AI will not go pass the POC stage and this is often due to 1) not right IT/data infrastructure in place 2) not specialist people have been hired to handle the new software or digital transformation process has not been planned yet. Regarding point 2, the most difficult issue is around hiring data scientists or data/machine learning engineers because many companies struggle with that. In fact, in a March 2021 O’Reilly survey of enterprise AI adoption, it has been found that “the most significant barrier to AI adoption is the lack of skilled people and the difficulty of hiring.” And in 2019 software it has been estimated that there were around 144,000 AI- related job openings, but only around 26,000 developers and specialists seeking work. Of course hiring an internal data scientist, it is not the only problem in restructuring the workforce. Often a corporation has to be able to re-train entire teams to be able to fully benefit from a new AI software. I can give an example. As many readers know a sales process involves 3 stages: lead generation, q&a call/mails with potential clients and deal closing. Now, a couple of years ago AI Technologies had been engaged to automatize the q&a call stage and we build a ai bot to manage the 'standard questions' a potential client may ask (without getting into the details, using AI and technically word3vec encoding, it is very possible to automate mails/chatbot for 'standardized questions' like 'how much it cost?' 'how long is the warranty for' etc.). Using this new internal solution, it meant the team responsible for the q&a would have been retrained either to increase the number of leads or the number of closing. The company simply decided to not embark into the transformation process required to benefit the new AI adoption. This example, in various forms, it is actually quite common: companies unless they are really innovative prefer to continue with their corroborated internal procedures unless some competitors threat their profitability. This bring to the fact that actually AI is not an out of the shelves solution which can be plugged in with no effort. As the moment a POC is under development it should be a good norm to plan a digital transformation process within the company. Also it is worth mentioning that, it is unlikely that the workforce has to be dismissed or made redundant as many expected following AI adoption. Just following the example above, what the AI bot does actually is to get over the repetitive tasks (q&a) so people can do more creative work engaging more clients (lead generation) or convincing to buy ( deal closing). Of course, it means that some people have to be retrained but also means that with the same people, you can close/generate more sales. It is a misconception to think that AI solutions will make human work redundant , we just need to adapt to new jobs. My example resembles a classical example on adoption of ATMs. When ATMs were introduced in 1969, conventional wisdom expected the number of banking locations to shrink, but instead, it actually made it possible to set up many more of them, it became cost-effective. There were under 200,000 bank tellers in 1970, but over 400,000 a decade later. The other common problem to face when companies want to embrace AI adoption (point 1), it is their current infrastructure: databases, servers, and crm systems have to be already in place. To put it simply, any AI system requires data to work with so it naturally sits on top of data infrastructure in day to day business operations. In the last two years AI Technologies has been engaged to work with a large public organization (70,000 employees) to build a solution to automatically detect malicious behavior of its employees manipulating their data. To build the AI software we had also designed a system to stream data from each employee terminal into a central database for processing. This infrastructure was not present at the beginning of the project since before the need for malicious detection was arised, the organization never really realized the necessity to gather certain data: a simple login and logout time was all the needed to monitor the activity of their employees (which company folder/file they accessed etc. was not important). This is a common situation and most of the companies' infrastructure are usually not ready to be used directly with AI solutions: their current infrastructure was simply designed with other objectives in mind. For sake of completeness, most companies decide to invest their internal resources in other areas of the business rather than crm or expensive data structures. There is no blame on this choice, at the end any business has to be profitable and investing in infrastructure is not always easy to quantify the return of investment. If anything, this article should have given an idea of the major pitfalls approaching AI projects which can be summarized as follows: • AI solutions are not out of the shelves , ready made software that can be immediately put in use: they often require new skilled hires within the client organization and potentially a plan how to re-utilized part of the workforce. • It is often a myth that AI solutions will necessarily replace the employees although it is possible that they have to be retrained. • Any AI project works on data and infrastructure which are necessary to benefit the new solutions. Before embarking on AI projects an organization has to either budget in a new infrastructure or at the very least an upgrade of the one in use. In essence, due to the implication on both employees and infrastructure, AI adoption should be considered as a digital transformation process more than a software development project. After the overwhelming hype of attention of the recent years, I would expect that in the next 2-3 years more companies will start to realize what AI projects really are and how to best use them.

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Empowering Industry 4.0 with Artificial Intelligence

Article | August 3, 2020

The next step in industrial technology is about robotics, computers and equipment becoming connected to the Internet of Things (IoT) and enhanced by machine learning algorithms. Industry 4.0 has the potential to be a powerful driver of economic growth, predicted to add between $500 billion- $1.5 trillion in value to the global economy between 2018 and 2022, according to a report by Capgemini.

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Fortinet (NASDAQ: FTNT) secures the largest enterprise, service provider, and government organizations around the world. Fortinet empowers its customers with intelligent, seamless protection across the expanding attack surface and the power to take on ever-increasing performance requirements of the borderless network - today and into the future.

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