Learning Automated Trading Can Give You a Major Investing Advantage – The Advocate

Learning Automated Trading Can Give You a Major Investing Advantage

Technology has changed everything, including the way people invest. There is always risk inherent in investing but fin-tech like quantitative and algorithmic trading can make life a little easier on investors who have the technical expertise to get a competitive advantage. Whether you're a regular investor or interested in starting out, you owe it to yourself to learn some of the fin-tech that's changing the industry, and QuantInsti: Quantitative Trading for Beginners Bundle can help.

In this seven-course bundle, you'll get a comprehensive fin-tech education. You'll start with an introduction to algorithmic trading, that is, programming a computer to take certain trading actions in response to market data. From there, you'll learn how to use machine learning tools like Python to automate your trading to limit your losses and maximize your gains. You'll even get access to an Interactive brokers platform to practice automating your trading and learn momentum trading skills for forex markets. By the end of the training, you'll be fully ready to trade on your own or ace a quant Interview to work for someone else.

Start investing like a modern genius. Sold separately, the courses of QuantInsti: Quantitative Trading for Beginners Bundle would go for over $500, but you can get them all for just $49 today.

Related:Learning Automated Trading Can Give You a Major Investing Advantage3 Bad Investing Habits You Should Drop Before It's Too LateMake Smarter Investment Decisions with These Courses

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Learning Automated Trading Can Give You a Major Investing Advantage - The Advocate

Machine Vision is Key to Industry 4.0 and IoT – ReadWrite

Machine vision joins machine learning in a set of tools that gives consumer- and commercial-level hardware unprecedented abilities to observe and interpret their environment. In an industrial setting, these technologies, plus automation and higher-speed networking, add up to a new industrial revolution Industry 4.0. They also offer brand-new ways to conduct low-waste, high-efficiency industrial activities.

Machine vision affects manufacturing, drilling, and mining. Further benefits are found in freight and supply chain management, quality assurance, material handling, security, and a variety of other processes and verticals.

Machine vision is going to be everywhere before long, adding a critical layer of intelligence to the Internet of Things buildouts in the industrial world. Heres a look at how companies are already putting it to work.

Machine vision is a set of technologies that gives machines greater awareness of their surroundings. It facilitates higher-order image recognition and decision-making based on that awareness.

To take advantage of machine vision, a piece of industrial equipment uses high-fidelity cameras to capture digital images of the environment, or a workpiece. The images can be taken in an automated guided vehicle (AGV) or a robotic inspection station. From there, machine vision uses extremely sophisticated pattern recognition algorithms to make a judgment about its position, identity, or condition.

Several lighting sources are common in machine vision applications, including LEDs, quartz halogen, metal halide, xenon, and traditional fluorescent lighting. If part of a barcode or workpiece is shadowed, the reading might deliver an error when there isnt one, or vice versa.

Machine vision combines sophisticated hardware and software to allow machines to observe and react to outside stimuli in new and beneficial ways.

The proliferation of Industrial Internet of Things (IIoT) devices marks an important moment in technological advancement. IIoT gives businesses unprecedented visibility of their operations from top to bottom. Networked sensors and cloud-based enterprise and resource planning hubs provide two-way data mobility between local and remote assets, as well as business partners.

The two-way mobility can be something as small as a mechanical piston or bearing. It can also be as large as a fleet of trucks, can yield valuable operational data with the right IoT hardware and software. Businesses can have their eyes everywhere, even when theyre strapped for resources or labor.

Where does machine vision fit into all this? Machine vision makes existing IoT assets even more powerful and better able to deliver value and efficiency. We can expect it to create some brand-new opportunities.

Machine vision makes sensors throughout the IoT even more powerful and useful. Instead of providing raw data, sensors deliver a level of interpretation and abstraction that can be used in decision-making or further automation.

Machine vision may help reduce the bandwidth requirements of large-scale IoT buildouts. Compared with capturing images and data at the source and sending it to servers for analysis, machine vision typically performs its research at the source of the data. Modern industry generates millions of data points, but a great deal of it can yield actionable insights without requiring transmission to a secondary location, thanks to machine vision and edge computing.

Machine vision complements IoT automation technologies extremely well. Robotic inspection stations can work more quickly and accurately than human QA employees, and they immediately surface relevant data for decision-makers when defects and exceptions are detected.

Guidance systems built with machine vision give robots and cobots greater autonomy and pathfinding abilities, and help them work faster and more safely alongside human workers. In warehouses and other settings with a high risk of error, machine vision helps robotic order pickers improve response time and limit fulfillment defects that result in lost business.

Todays and tomorrows economy requires companies and industries that operate while wasting far less time, material, and labor. Machine vision will continue to make drones, material handling equipment, unmanned vehicles and pallet trucks, manufacturing lines, and inspection stations better able to exchange detailed and valuable data with the rest of the network.

In a factory setting, it means machines and people working in better harmony with fewer bottlenecks, overruns, and other disruptions.

When you think about each of the steps involved in a typical industrial process, its not hard to see each point where machine vision can improve operations.

To manufacture a single automotive part, humans and machines collaborate to source raw materials, appraise their quality, transport them to a plant for processing, and move the items through the facility at each manufacturing stage. Ultimately, they see it successfully through the QA process and then out the door again, where at least one last leg of its journey awaits. At some later time, the retailer or end-user receives it.

Whether this product is at rest, in transit, or not even assembled yet, machine vision provides a way to automate the handling of it. It improves efficiency in every department, such as assembly, and maintains higher and more consistent quality levels.

Some applications are as simple as placing a line on a warehouse floor for an unmanned vehicle to follow safely. Other machine vision tools are even more sophisticated, although even the simplest examples can be game-changers.

Some of the most exciting examples of machine vision in the industrial world involve tasks once thought difficult or impossible to outsource to robots. As mentioned, picking from bins in warehouses is a process thats inherently risky when it comes to errors. Mistakes in fulfillment cost goodwill and customers.

There are already nearly 100% autonomous order-picking robots available today, which can navigate safely, inspect parts and products in the bin, make the right pick using a manipulator arm, and transport the pick to a staging or packaging area.

Ultimately, this means companies are at a far lesser risk of shipping damaged goods or incorrect SKUs that look similar to, but dont quite match, the one the customer ordered.

In some modern manufacturing settings, it can help employers automate and improve results from the QA process, even without sacrificing human jobs. Instead, automated inspection stations tackle this high-priority work while employees learn more cognitively demanding skills.

Cobots will likely achieve a 34% share of all robotics sales by 2025. This is due in large part to improvements in machine vision and the drive to eliminate as much inefficiency, inaccuracy, and waste from the modern industry as possible.

Expect machine vision to continue to evolve in the coming years and contribute further to Industry 4.0, which many call the Fourth Industrial Revolution. Eyes are already trained on newer, lower-cost products featuring embedded and board-level image processing with machine vision capabilities.

Machine vision capabilities will lead to even more widespread adoption of the IoT and machine vision and new ways for businesses to capitalize on digital intelligence.

Featured Image Credit: HAHN Group, CC BY-SA

Megan Ray Nichols is a freelance technical writer and blogger. She enjoys writing easy to understand science and technology articles on her blog, Schooled By Science. When she isn't writing, Megan enjoys watching movies and hiking with friends.

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Machine Vision is Key to Industry 4.0 and IoT - ReadWrite

After 6 Years in Exile, Edward Snowden Explains Himself …

Edward Snowden, arguably the worlds most famous whistle-blower, is a man who lived behind plenty of pseudonyms before putting his true name to his truth-telling: When he was first communicating with the journalists who would reveal his top-secret NSA leaks, he used the names Citizenfour, Cincinnatus, and VeraxLatin for truthful and a knowing allusion to Julian Assanges old hacker handle Mendax, the teller of lies.

But in his newly published memoir and manifesto, Permanent Record, Snowden describes other handles, albeit long-defunct ones: Shrike the Knight, Corwin the Bard, Belgarion the Smith, squ33ker the precocious kid asking amateur questions about chip compatibility on an early bulletin-board service. These were online videogame and forum personas, he writes, that as a teenager in the 1990s hed acquire and jettison like T-shirts, assuming new identities on a whim, often to leave behind mistakes or embarrassing ideas hed tried out in online conversations. Sometimes, he notes, hed even use his new identity to attack his prior self, the better to disavow the ignoramus hed been the week before.

That long-lost internet, Snowden writes, offered its inhabitants a reset button for your life that could be pressed every day, at will. And he still pines for it. To be able to expand your experience, to become a more whole person by being able to try and fail, this is what teaches us who we are and who we want to become, Snowden told WIRED in an interview ahead of his books publication tomorrow. This is whats denied to the rising generation. Theyre so ruthlessly and strictly identified in every network they interact with and by which they live. Theyre denied the opportunities we had to be forgotten and to have their mistakes forgiven.

Snowden's memoir revisits his youthful, freewheeling days on the internet. Buy on Amazon

No one has exposed more than Snowden how that individualistic, ephemeral, anonymous internet has ceased to exist. Perhaps it was always a myth. (After all, at least one trove of Snowdens chatroom musings on everything from guns to sex advice, under the pseudonym TheTrueHooha, remained online after his rise to notoriety.)

But for the former NSA contractor and many of his generation, that idea of the internet is a foundational myth, enshrined in Neal Stephenson novels and in The Hacker Manifestoboth of which Snowden describes reading as a teenager in a mononucleosis hazeand John Perry Barlows Declaration of the Independence of Cyberspace, which Snowden writes that he holds in his memory next to the preamble to the Constitution. The internet of the 90s, which Snowden describes as the most pleasant and successful anarchy Ive ever seen, was his community and his education. He even met his future wife on Hotornot.com.

Snowden says documenting that prehistoric digital world and its disappearance was part of what drove him to write Permanent Record, overcoming his own aversion to sharing details of his personal life. And in doing so, he may have also helped the world understand him better than ever before. This is actually more than a memoir from my perspective, he says. The way I got through it was by telling, yes the history of myself as a person, but also the history of a time and a changein technology, in a system, in the internet, and in American democracy.

The IT Guy Ascendant

The resulting autobiography is split roughly into thirds: Snowdens life before joining the world of spies, his whirlwind seven years in the intelligence community, and his experience as a whistle-blower and international fugitive. Against all odds, the first of these, a full hundred pages largely describing the very least unique part of Snowdens lifea hyper-intelligent but relatively unremarkable high school dropoutis not at all a waste of time.

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After 6 Years in Exile, Edward Snowden Explains Himself ...

Coming soon: The promise of artificial intelligence in servicing – HousingWire

One click, your mortgage process begins. Another click, that mortgage loan is pre-approved.Five minutes pass and you are ready to buy a home.

The digital application process for single-family mortgages has flourished with new technology, new companies entering the space and new capabilities that, even just 10 years ago, we wouldnt have thought possible.

Borrowers can sign closing papers on a new home remotely so that they dont have to miss hours of work. Travelers can close on their home from the other side of the world. The credit invisible, or those with no credit score, can learn more about their financial situation and what they can do to prepare to buy a home after a quick and painless application process.

But if you fast forward just a few weeks, to when a borrower is getting settled into their new home, the experience changes dramatically. Their mortgage loan is sold and their servicer steps in to introduce themselves. The smooth digital process is suddenly transformed into a rough, paper-heavy and confusing process.

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Coming soon: The promise of artificial intelligence in servicing - HousingWire

Explained: The Artificial Intelligence Race is an Arms Race – The National Interest Online

Graham Allison alerts us to artificial intelligence being the epicenter of todays superpower arms race.

Drawing heavily on Kai-Fu Lees basic thesis, Allison draws the battlelines: the United States vs. China, across the domains of human talent, big data, and government commitment.

Allison further points to the absence of controls, or even dialogue, on what AI means for strategic stability. With implied resignation, his article acknowledges the smashing of Pandoras Box, noting many AI advancements occur in the private sector beyond government scrutiny or control.

However, unlike the chilling and destructive promise of nuclear weapons, the threat posed by AI in popular imagination is amorphous, restricted to economic dislocation or sci-fi depictions of robotic apocalypse.

Absent from Allisons call to action is explaining the so what?why does the future hinge on AI dominance? After all, the few examples (mass surveillance, pilot HUDs, autonomous weapons) Allison does provide reference continued enhancements to the status quoincremental change, not paradigm shift.

As Allison notes, President Xi Jinping awoke to the power of AI after AlphaGo defeated the worlds number one Go human player, Lee Sedol. But why? What did Xi see in this computation that persuaded him to make AI the centerpiece of Chinese national endeavor?

The answer: AIs superhuman capacity to think.

To explain, lets begin with what I am not talking about. I do not mean so-called general AIthe broad-spectrum intelligence with self-directed goals acting independent of, or in spite of, preferences of human creators.

Eminent figures such as Elon Musk and Sam Harris warn of the coming of general AI. In particular, the so-called singularity, wherein AI evolves the ability to rewrite its own code. According to Musk and Harris, this will precipitate an exponential explosion in that AIs capability, realizing 10,000 IQ and beyond in a matter of mere hours. At such time, they argue, AI will become to us what we are to ants, with similar levels of regard.

I concur with Sam and Elon that the advent of artificial general superintelligence is highly probable, but this still requires transformative technological breakthroughs the circumstances for which are hard to predict. Accordingly, whether general AI is realized 30 or 200 years from now remains unknown, as is the nature of the intelligence created; such as if it is conscious or instinctual, innocent or a weapon.

When I discuss the AI arms race I mean the continued refinement of existing technology. Artificial intelligence that, while being a true intelligence in the sense of having the ability to self-learn, it has a single programmed goal constrained within a narrow set of rules and parameters (such as a game).

To demonstrate what President Xi saw in AI winning a strategy game, and why the global balance of power hinges on it, we need to talk briefly about games.

Artificial Intelligence and Games

There are two types of strategy games: games of complete information and games of incomplete information. A game of complete information is one in which every player can see all of the parameters and options of every other player.

Tic-Tac-Toe is a game of complete information. An average adult can solve this game with less than thirty minutes of practice. That is, adopt a strategy that no matter what your opponent does, you can correctly counter it to obtain a draw. If your opponent deviates from that same strategy, you can exploit them and win.

Conversely, a basic game of uncertainty is Rock, Scissors, Paper. Upon learning the rules, all players immediately know the optimal strategy. If your opponent throws Rock, you want to throw Paper. If they throw Paper, you want to throw Scissors, and so on.

Unfortunately, you do not know ahead of time what your opponent is going to do. Being aware of this, what is the correct strategy?

The unexploitable strategy is to throw Rock 33 percent of the time, Scissors 33 percent of the time, and Paper 33 percent of the time, each option being chosen randomly to avoid observable patterns or bias.

This unexploitable strategy means that, no matter what approach your opponent adopts, they won't be able to gain an edge against you.

But lets imagine your opponent throws Rock 100 percent of the time. How does your randomized strategy stack up? 33 percent of the time you'll tie (Rock), 33 percent of the time you'll win (Paper), and 33 percent of the time you'll lose (Scissors)the total expected value of your strategy against theirs is 0.

Is this your optimal strategy? No. If your opponent is throwing Rock 100 percent of the time, you should be exploiting your opponent by throwing Paper.

Naturally, if your opponent is paying attention they, in turn, will adjust to start throwing Scissors. You and your opponent then go through a series of exploits and counter-exploits until you both gradually drift toward an unexploitable equilibrium.

With me so far? Good. Let's talk about computing and games.

As stated, nearly any human can solve Tic-Tac-Toe, and computers solved checkers many years ago. However more complex games such as Chess, Go, and No-limit Texas Holdem poker have not been solved.

Despite all being mind-bogglingly complex, of the three chess is simplest. In 1997, reigning world champion Garry Kasparov was soundly beaten by the supercomputer Deep Blue. Today, anyone reading this has access to a chess computer on their phone that could trounce any human player.

Meanwhile, the eastern game of Go eluded programmers. Go has many orders of magnitude more combinations than chess. Until recently, humans beat computers by being far more efficient in selecting moveswe don't spend our time trying to calculate every possible option twenty-five moves deep. Instead, we intuitively narrow our decisionmaking to a few good choices and assess those.

Moreover, unlike traditional computers, people are able to think in non-linear abstraction. Humans can, for example, imagine a future state during the late stages of the game beyond which a computer could possibly calculate. We are not constrained by a forward-looking linear progression. Humans can wonderfully imagine a future endpoint, and work backwards from there to formulate a plan.

Many previously believed that this combination of factorsnear-infinite combinations and the human ability to think abstractlymeant that go would forever remain beyond the reach of the computer.

Then in 2016 something unprecedented happened. The AI system, AlphaGo, defeated the reigning world champion go player Lee Sedol 4-1.

But that was nothing: two years later, a new AI system, AlphaZero, was pitched against AlphaGo.

Unlike its predecessor which contained significant databases of go theory, all AlphaZero knew was the rules, from which it played itself continuously over forty days.

After this period of self-learning, AlphaZero annihilated AlphaGo, not 4-1, but 100-0.

In forty days AlphaZero had superseded 2,500 years of total human accumulated knowledge and even invented a range of strategies that had never been discovered before in history.

Meanwhile, chess computers are now a whole new frontier of competition, with programmers pitting their systems against one another to win digital titles. At the time of writing the world's best chess engine is a program known as Stockfish, able to smash any human Grandmaster easily. In December 2017 Stockfish was pitted against AlphaZero.

Again, AlphaZero only knew the rules. AlphaZero taught itself to play chess over a period of nine hours. The result over 100 games? AlphaZero twenty-eight wins, zero losses, seventy-two draws.

Not only can artificial intelligence crush human players, it also obliterates the best computer programs that humans can design.

Artificial Intelligence and Abstraction

Most chess computers play a purely mathematical strategy in a game yet to be solved. They are raw calculators and look like it too. AlphaZero, at least in style, appears to play every bit like a human. It makes long-term positional plays as if it can visualize the board; spectacular piece sacrifices that no computer could ever possibly pull off, and exploitative exchanges that would make a computer, if it were able, cringe with complexity. In short, AlphaZero is a genuine intelligence. Not self-aware, and constrained by a sandboxed reality, but real.

Despite differences in complexity there is one limitation that chess and go both share they're games of complete information.

Enter No-limit Texas Holdem (hereon, Poker). This is the ultimate game of uncertainty and incomplete information. In poker, you know what your hole cards are, the stack sizes for each player, and the community cards that have so far come out on the board. However, you don't know your opponent's cards, whether they will bet or raise or how much, or what cards are coming out on later streets of betting.

Poker is arguably the most complex game in the world, combining mathematics, strategy, timing, psychology, and luck. Unlike Chess or Go, Pokers possibilities are truly infinite and across multiple players simultaneously. The idea that a computer could beat top Poker professionals seems risible.

Except that it has already happened. In 2017, the AI system Libratus comprehensively beat the best Head's-up (two-player) poker players in the world.

And now, just months ago, another AI system Pluribus achieved the unthinkableit crushed super high stakes poker games against multiple top professionals simultaneously, doing so at a win-rate of five big blinds per hour. For perspective, the difference in skill level between the best English Premier League soccer team and the worst would not be that much.

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Explained: The Artificial Intelligence Race is an Arms Race - The National Interest Online

WATCH: Heres how Compass uses artificial intelligence to support its agents – Inman

The vast majority of what we do will disappear into the regular tools agents use every day, Compass CTO Joseph Sirosh said onstage at Inman Connect New York.

Inman Connect sessions are on video replay. Tune in for winning strategies, and discover whats next in real estate. Session videos, livestream access and event discounts for Connect are all exclusive to Inman Select subscribers.

Compass, to me, is an idea, Joseph Sirosh, the chief technology officer at Compass, said at Inman Connect in New York on Thursday. Agents grow their business and we invest as much as possible in agents growing their business with technology.

Compass has grown its technology team massively in the past year, nearly tripling it since Sirosh took the role. The company has pulled in talent from some of the worlds top technology companies like Amazon, Microsoft, Facebook and Google.

Among the key areas Compass has focused is artificial intelligence (AI), Sirosh, the former CTO of AI at Microsoft and the CTO of consumer at Amazon, told Clelia Peters, the president of Warburg Realty and Inmans editor-at-large, at Inman Connect New York at the Marriott Marquis.

To hear more about how Compass uses AI to support its agents, tune in to the video above, or read the original article here.

Dont miss out on the latest Inman Connect videos published daily. Discover whats next and grow your business by watching on replay or joining us at upcoming events for live learning and networking.

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Hyperautomation, 2020: A Rule-Based Artificial Intelligence – ResearchAndMarkets.com – Business Wire

DUBLIN--(BUSINESS WIRE)--The "Hyperautomation: A Rule-Based Artificial Intelligence" report has been added to ResearchAndMarkets.com's offering.

The Report Includes:

This key objective of this study is to provide an introductory analysis of the hyperautomation market and its enormous market potential. Hyperautomation, as defined in the report, is the application of technologies such as robotic process automation, artificial intelligence, cognitive process automation, and others to augment workers and automate processes so that they can have a significantly greater impact than traditional automation technologies.

The goal of this report is to provide an up-to-date analysis of the recent developments and current trends in the global hyperautomation market. In this study, the hyperautomation market is extensively defined and mapped out with a strong focus on the current market's competitive situation.

Key Topics Covered:

Chapter 1 Hyperautomation

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/kvet0f

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Hyperautomation, 2020: A Rule-Based Artificial Intelligence - ResearchAndMarkets.com - Business Wire

AI Will Probably Trick Us Into Thinking We Found Aliens – Popular Mechanics

NASA/JPL-Caltech/UCLA/MPS/DLR/IDA

Ever since the Dawn spacecraft picked up images of what look to be a vast network of bright spots in the Occator crater on Ceresa dwarf planet in the asteroid beltthere's been conjecture over whether the whiteish spots are made up of ice, or some kind of volcanic salt deposits. Meanwhile, another controversy has been brewing over them: What exactly are those shapes seen in the bright spots, called Vinalia Faculae? Are they squares or triangles? Did extraterrestrials create them?

Because the strange patterns are so strikingly geometric, researchers from the University of Cadiz in Spain have taken a closer look at the bright spots to figure out whether humans and machines look at planetary images differently. The overall goal was to figure out if artificial intelligence can help us discover and make sense of technosignatures, or potentially detectable signals from distant, advanced civilizations, according to NASA.

"One of the potential applications of artificial intelligence is not only to assist in big data analysis but to help to discern possible artificiality or oddities in patterns of either radio signals, megastructures or techno-signatures in general," the authors wrote in a new paper published in the scientific journal Acta Astronautica.

To figure out what people thought they saw in the images of Occator, study author Gabriel G. De la Torre, a neuropsychologist from the University of Cadiz in Spain, brought together 163 volunteers who had no prior astronomy training. Overwhelmingly, these people identified a square shape in the crater's bright spots.

Then, the same was done with an artificial vision system trained with convolutional neural networks, which are mostly used in image recognition. Training data for the neural net included thousands of images of both squares and triangles so the system could identify those shapes.

NASA/JPL-Caltech/UCLA/MPS/DLR/IDA/PSI

Strangely, the neural net saw the same square the people noticed, but also identified a triangle, as shown in the image above. It appears the square is inside a larger triangle. After the people in the study were faced with this new triangular option, the percentage of them who claimed to have seen a triangle skyrocketed.

It's just one example of how our minds can be easily tricked when faced with a false positive. If we're told a system identified a given blip, we're more likely to blindly believe and truly think we saw the same blip due to our own tendency toward confirmation bias, or interpreting information in a particular way to fit a pre-existing belief.

That makes AI potentially dangerous in the search for far-away extraterrestrial life. False positives can confuse researchers, compromising its own usefulness in detecting technosignatures. De la Torre points out in the paper that what this all adds up to is actually a commonality between humans and AI systems: We both struggle with implicit bias.

So, if not artificial structures, what exactly are those funny shapes in the Ceres bright spot images? The quick answer is "we don't know." But De la Torre has an idea: It's "probably just a play of light and shadow."

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AI Will Probably Trick Us Into Thinking We Found Aliens - Popular Mechanics

Artificial intelligence in the real estate industry – Robotics and Automation News

While it was once considered an advanced technology of the future, artificial intelligence is very much a present-day reality.

Thanks to inventions like self-driving cars, home assistant devices, automatic vacuum cleaners and remote home security solutions, Artificial Intelligence is on everyones lips.

Since AI seems to affect both the public and private sector, we started thinking about all the different ways in which itll shake up the real estate world.

Read on to find out more about the current and future impact of AI in property sales, marketing and operations.

What is Artificial Intelligence?

Artificial Intelligence, or AI for short, refers to smart technological tools whose level of awareness allows them to learn from their environment in order to improve processes and decision-making.

AI tools can learn, plan, comprehend, and self-correct independently, thus saving time and resources. AI is typically separated into three different classes, namely:

Using these AI solutions in the real estate industry can help to fast-track decision making and improve operational efficiency.

Thats because these programs make it easier to identify and examine patterns and make connections between different components of large data sets.

Relevant Application of AI in Real Estate

AI brings innovation wherever its applied. Thats because AI tools are hardwired to self-optimize based on real-time data.

This makes them ideal for improving and streamlining complex processes. AI tools can help to improve efficiency for all the different stakeholders in the real estate industry, from investors, asset managers, brokers and sellers.

Moreover, AI solutions often lead to cost-efficiency as it becomes irrelevant for businesses to hire staff when they can just automate most of their operations. For instance, AI is pegged to automate real estate management, thus eliminating the need for large property management teams.

How AI Affects Real Estate Processes

Currently, information management is the main application of AI in the real estate industry. In this capacity, AI tools can be used to collect data on an entire property portfolio or an individual asset.

This creates a virtual data room that can be used to study documents, translate international real estate transactions in real time, and validate parameters.

However, AI is also useful at analyzing functions related to system control and monitoring, security and fire protection, especially when it comes to managing entire building structure.

Individual homeowners can also use AI to automate security, lighting and cooling systems. In fact, the machine learning processes imbedded in a lot of AI-powered Internet of Things devices allows for loads of energy and cost-saving potential.

AI Benefits for real estate agents and clients

AI can help to speed up real estate transactions by cutting in half the manual input and time required to complete them. AI also holds the promise of increased efficiency when it comes to marketing, due diligence and sales processes.

Commercial property operators stand to benefit the most from AI tools since they can be used to process massive data sets in half the time usually required.

With the right programming, AI can help you spot the potential pitfalls and advantages of a particular transaction without manually sifting through mountains of documents.

Real estate agents can leverage AI tools like chat bots to communicate with clients and analyze the facts from different perspectives, including property values.

Challenges of AI for the Real Estate Sector

One of the main challenges when it comes to AI in real estate is the fact that specialists are required to enable many of the automatic features. Keep in mind that AI tools can only work efficiently when independent learning has been enabled.

Its also important to ensure that you utilize AI tools in compliance with legal regulations while keeping in mind security repercussions.

With all their benefits, AI tools cannot make final decisions on anything. Their job is to improve data collection, organization and presentation methods in order to facilitate better decision making.

To get the most out of AI, real estate operators would have to enable better collaboration between human capability and AI software algorithms.

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Artificial intelligence requires trusted data, and a healthy DataOps ecosystem – ZDNet

Lately, we've seen many "x-Ops" management practices appear on the scene, all derivatives from DevOps, which seeks to coordinate the output of developers and operations teams into a smooth, consistent and rapid flow of software releases. Another emerging practice, DataOps, seeks to achieve a similarly smooth, consistent and rapid flow of data through enterprises. Like many things these days, DataOps is spilling over from the large Internet companies, who process petabytes and exabytes of information on a daily basis.

Such an uninhibited data flow is increasingly vital to enterprises seeking to become more data-driven and scale artificial intelligence and machine learning to the point where these technologies can have strategic impact.

Awareness of DataOps is high. A recent survey of 300 companies by 451 Research finds 72 percent have active DataOps efforts underway, and the remaining 28 percent are planning to do so over the coming year. A majority, 86 percent, are increasing their spend on DataOps projects to over the next 12 months. Most of this spending will go to analytics, self-service data access, data virtualization, and data preparation efforts.

In the report, 451 Research analyst Matt Aslett defines DataOps as "The alignment of people, processes and technology to enable more agile and automated approaches to data management."

The catch is "most enterprises are unprepared, often because of behavioral norms -- like territorial data hoarding -- and because they lag in their technical capabilities -- often stuck with cumbersome extract, transform, and load (ETL) and master data management (MDM) systems," according to Andy Palmer and a team of co-authors in their latest report,Getting DataOps Right, published by O'Reilly. Across most enterprises, data is siloed, disconnected, and generally inaccessible. There is also an abundance of data that is completely undiscovered, of which decision-makers are not even aware.

Here are some of Palmer's recommendations for building and shaping a well-functioning DataOps ecosystem:

Keep it open: The ecosystem in DataOps should resemble DevOps ecosystems in which there are many best-of-breed free and open source software and proprietary tools that are expected to interoperate via APIs." This also includes carefully evaluating and selecting from the raft of tools that have been developed by the large internet companies.

Automate it all:The collection, ingestion, organizing, storage and surfacing of massive amounts of data at as close to a near-real-time pace as possible has become almost impossible for humans to manage. Let the machines do it, Palmer urges. Areas ripe for automaton include "operations, repeatability, automated testing, and release of data." Look to the ways DevOps is facilitating the automation of the software build, test, and release process, he points out.

Process data in both batch and streaming modes. While DataOps is about real-time delivery of data, there's still a place -- and reason -- for batch mode as well. "The success of Kafka and similar design patterns has validated that a healthy next-generation data ecosystem includes the ability to simultaneously process data from source to consumption in both batch and streaming modes," Palmer points out.

Track data lineage: Trust in the data is the single most important element in a data-driven enterprise, and it simply may cease to function without it. That's why well-thought-out data governance and a metadata (data about data) layer is important. "A focus on data lineage and processing tracking across the data ecosystem results in reproducibility going up and confidence in data increasing," says Palmer.

Have layered interfaces. Everyone touches data in different ways. "Some power users need to access data in its raw form, whereas others just want to get responses to inquiries that are well formulated," Palmer says. That's why a layered set of services and design patterns is required for the different personas of users. Palmer says there are three approaches to meeting these multilayered requirements:

Business leaders are increasingly leaning on their technology leaders and teams to transform their organizations into data-driven digital entities that can react to events and opportunities almost instantaneously. The best way to accomplish this -- especially with the meager budgets and limited support that gets thrown out with this mandate -- is to align the way data flows from source to storage.

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Artificial intelligence requires trusted data, and a healthy DataOps ecosystem - ZDNet