Java API for kdb+

| May 31, 2018

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The Java programming language has been consistently popular for two decades, and is important in many development environments. Its longevity, and the compatibility of code between versions and operating systems, leaves the landscape of Java applications in many industries very much divided between new offerings and long-established legacy code.

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Vivvo

At Vivvo we are adamant about challenging the status quo in a good way. We believe in innovating with purpose for our customers, continuously improving the way solutions are built, delivered and supported. Talented people are the heart of any successful company and we seek the brightest minds in the industry, engaging the right people at the right time on every project.

OTHER ARTICLES

Coronavirus apps: trading privacy for effectiveness?

Article | April 30, 2020

As the world looks for a way to manage the spread of the coronavirus while getting back to a more familiar way of living, how can apps provide a solution that is both effective and respects the privacy of all citizens?It seems that no other app has attracted so much attention over the past weeks as the coronavirus app, described as a powerful tool to curb the spread of COVID-19 so we can go back to normal life.

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5 OBSTACLES TO SUCCESSFUL DATA GOVERNANCE

Article | August 11, 2020

Organizational leaders worldwide agree that data governance is important. However, data governance programs in most companies are still being planned or in progress. In a 2020 Dataversity report¹, only 12 percent of companies had fully implemented programs, while 38 percent of programs were a work in progress, and 31 percent were just getting started.

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These Mindblowing Theories About Human Consciousness Will Change How You Look At The World

Article | October 21, 2020

Consciousness—it’s one of the biggest questions out there. One thing that people today have in common with those from the earliest ages is questioning consciousness and our own existence. It’s taken different forms through the years, but the questions are largely similar at their core and the answers are still at large after all this time. The good news is that while we don’t have the answers yet, or even a timetable for when we might get those answers, we know more now than we have at any other point in human history. It’s easier to share information than it ever was in the past, and in this era, even an average person can study the big questions about life without the requirement of a formal education or access to a university. But in this age where it’s easier to ask questions, what kind of answers are we actually being led towards? We could be in a simulation—but not in the way you think One of the more modern theories on consciousness proposes that we might be living in a simulation. And modern really is the right word to describe this one, because it would have been an unthinkable idea even 20 years ago. However, as computing has grown stronger and stronger over the years, a key question was raised by these advancements: Is it possible that somewhere, computers are already powerful enough to run an entire universe? And if that’s the case, are we living in one of these simulations? While this sounds outlandish, it’s certainly a theory that has at least some support. That includes support from Elon Musk, who says we probably are living in a simulation. Don’t think, however, that we’re living in some version of the Sims catered to an alien audience. Games might be the first thing that comes to mind for us when simulations are brought up, but a more serious answer is quite a bit different from that idea. Rather than a game, such a simulation may be for, to put it simply, historical purposes. That is to say, instead of some advanced alien civilization running their own simulated universe, it may be advanced humans from the future simulating the lives of their ancestors. But this simulation of the past would be real enough that for the simulated person on the other side, everything feels real and there’s no way to tell that it is a simulation. This isn’t just an idea from science fiction, as much as it might sound like one. It was proposed by Nick Bostrom, an Oxford professor. There’s a lot of possible reasons why a future society might want to run a simulation in this way, ranging from studying history to preserving the records of the past. If you don’t think this would be possible from a technical perspective, just consider the jump in quality between early computers and the computers of today. Computing has already improved exponentially within our lifetime. In the very far future, this growth may have continued to heights that would have been unimaginable previously, just like computers today would have been unimaginable to someone used to the first computers. Quantum mechanics could be part of the explanation We don’t know much about how the brain works. While there’s been a lot of scientific progress since the questions around the nature of consciousness were first raised, there’s still a long way to go in figuring out just what makes the brain tick so to speak. Quantum mechanics, however, is good at explaining these kinds of things that don’t operate along the regular laws of physics. It’s hard to explain exactly how quantum mechanics work also, but we do know a bit more about them than we know about the brain. Essentially, if you break things down to a small enough level, they begin to respond differently. Some of the laws and theories that would have dictated their behavior previously begin to behave more loosely. Take a toothpick for example. You can move it around or drop it or throw it and it follows the same laws of physics. And if you snapped it in half, those halves would also follow the same rules. However, if you kept doing this until you reached a certain tiny, microscopic level, things would get weird. But just saying that the brain might work on quantum mechanics doesn’t actually explain much. After all, that statement says nothing about what these mechanics may actually do, and more importantly, what that means for us. Fortunately, though, more detailed theories on the subject do exist. It’s been said that quantum mechanics could explain these different quantum laws working with our brain to create consciousness from a “fourth dimension” around us. Quantum laws may also dictate that particles behave differently depending on if they’re being observed or not. Of course, the definitions are complex. Observation is a general term that doesn’t literally mean looking at something in the context that the word would come up in a regular conversation. But at least in theory, it’s possible that much of how we experience the world has to do with our brain observing and interacting with particles around us at the quantum level. These observations may be a basic building block behind everything—a source code, so to speak, for the universe at large. Universal consciousness remains a theory Universal consciousness might be the oldest theory on this list. It predates the more modern ideas mentioned with quantum mechanics and the simulation theory, but there’s enough anecdotal evidence surrounding the subject to at least consider it. It’s not complicated such as quantum mechanics. On the other hand, it’s probably the easiest theory to understand between the three. It’s the idea that essentially we come from the same place, or that consciousness itself is an extension of the universe. This belief has been seen in religions from differing times and places, with Buddhism notably claiming that consciousness is around us everywhere. It’s not just Buddhism that has reflected these ideas, however. There’s many anecdotal stories over the years of people who have been close to death or have medically died and believed that during these experiences, they’ve become one with the universe or something else along those lines. Of course, these stories won’t hold up in the opinion of the scientific community and it’s obviously hard to study this kind of phenomenon in a meaningful way. But to consider a subject like consciousness, something that we don’t understand, entirely using the same scientific methods used for other things may be a mistake. After all, the concept of the universal mind has been around since at least 480 B.C., when it was introduced by Anaxagoras, a philosopher from before the time of Socrates. While this much time passed doesn’t necessarily mean the theory is true, a lot of people have put their belief behind it between that time period and now. Optimism about the future Earlier in this article, we mentioned Elon Musk’s belief that humanity is living in a simulation. It’s not the only time Musk has spoken about things that would be considered outlandish by a lot of people. He’s spoken of other things that might as well sound like something out of a science fiction novel, such as the threat of artificial intelligence. When Musk did speak about AI, however, he had a notable quote that didn’t have to do directly with that specific subject matter at all. Rather, it was a general outlook on philosophy and life. “You kind of have to be optimistic about the future. There’s no point in being pessimistic,” Musk said. “I’d rather be optimistic and wrong than pessimistic and right.” It’s philosophical advice worth keeping in mind. The fact of the matter is, we don’t have the answers. There’s various places to draw the answers from, whether it’s conventional theories or these newer modern ones about simulations and quantum physics, or even religions which have been around for hundreds or thousands of years. Whatever you do believe about the mind, or even if you don’t believe anything at all and you’re just waiting to see what answers scientists come up with in the future, keep your head up. When the answers aren’t around yet and all of them could be wrong, you can only keep a positive outlook on things and hold a hope that your preferred theory is one with truth behind it.

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Introduction To Artificial Intelligence And Machine Learning

Article | June 23, 2021

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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Spotlight

Vivvo

At Vivvo we are adamant about challenging the status quo in a good way. We believe in innovating with purpose for our customers, continuously improving the way solutions are built, delivered and supported. Talented people are the heart of any successful company and we seek the brightest minds in the industry, engaging the right people at the right time on every project.

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