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C2020-002 | IBM Algo Financial Modeler Developer Fundamentals

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Vendor IBM
Exam Number C2020-002
Exam Name IBM Algo Financial Modeler Developer Fundamentals
Questions 60 Q & A
Recent Update November 14, 2018
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C2020-002 exam Dumps Source : IBM Algo Financial Modeler Developer Fundamentals

Test Code : C2020-002
Test Name : IBM Algo Financial Modeler Developer Fundamentals
Vendor Name : IBM
Q&A : 60 Real Questions

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IBM IBM Algo Financial Modeler

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The influence On IBM i Of massive Blue’s Acquisition Of crimson Hat | killexams.com Real Questions and Pass4sure dumps

October 31, 2018 Timothy Prickett Morgan

smartly, we are able to actually say that we didn't see that coming when IBM and purple Hat announced late ultimate Sunday afternoon that massive Blue can be doling out $34 billion to acquire the area’s most a success company that peddles aid for open supply infrastructure software.

ironically, at the time I came about to be writing about how IBM and crimson Hat had simply introduced that that they had introduced the OpenShift Container Platform, a mashup of Docker and Kubernetes, to vigour programs machines operating Linux, and that i turned into lamenting that it turned into now not trivial to figure out a way to integrate this software with the IBM i platform. I had some ideas, as I commonly do. IBM had a extra direct one, and that is to purchase purple Hat outright for the sort of high top class – sixty two.eight p.c above the $20.53 billion market value that Wall road had valued crimson Hat at on the Friday before the news broke – that it probably will no longer see aggressive bidders try to swoop in and steal its new hat.

this is an important sum of money for IBM to blow on an acquisition – almost 10 times what it paid for Lotus development Corp in 1995 to get the Domino platform, forty six times what it paid for Tivoli system’s Netfinity gadget administration tools, and seven times that it paid for Cognos for its statistics analytics tools again in 2008. Pricewaterhouse Coopers Consulting, the primary big deal that Ginni Rometty, IBM’s latest president, chief government officer, and chairman, took down in 2002, charge $three.5 billion and significantly delivered to what IBM international features might do. Rational application in 2003 can charge IBM an extra $2.1 billion. if you add all of the massive deals that IBM has executed considering that 2000 and doubtless the small ones, too, it doubtless does not exceed $34 billion. So if IBM is blowing its money and borrowing against its credit score strains and stopping its share buybacks in 2020 and 2021 to pay for crimson Hat, you have to determine it is fairly serious.

lethal severe, in reality. Rometty should do anything it really is coherent to the IT sector at large and that gives her an enduring legacy on pointing IBM toward a future that supply sit down a chance to be a much bigger player in IT, now not one that is step by step enshrinkening itself by using divestitures and revolutionary revenue declines. The acquisition also offers IBM a relatively comprehensive infrastructure platform that can run by itself vigour methods and equipment z servers as neatly as the X86 servers that are offered with the aid of its many competitors.

red Hat’s enterprise Linux is typical to more than just a few IBM i retail outlets, and is by a long way the dominant Linux distribution within the enterprise. it's doubtless probably the most common Linux on this planet, if you encompass the hundreds of thousands of consumers the usage of RHEL in addition to its CentOS clone, which the enterprise obtained a number of years lower back. Our bet is that there are round 2 million machines on the earth working RHEL and maybe one other 2 million running CentOS. if you count number up all of the homegrown Linux situations in use by seven of the super Eight hyperscalers and cloud builders – Google, Amazon, not Microsoft very a whole lot, and fb within the u.s. and Alibaba, Baidu, Tencent, and China mobile in China – there is likely with no trouble yet another 10 million to 12 million Linux servers in that mix, and including all of the supercomputing and technical computing that runs on Linux apart from RHEL and CentOS plus the portion of corporate datacenters that is also now not controlled by using crimson Hat likely offer you anything round 1 million machines. That offers Linux about a 3rd of the put in base of forty five million servers on earth, with the remainder in most cases operating home windows Server. if you add up all of the servers on the planet running IBM i, AIX, and z/OS plus every other working system that you would be able to believe of (there aren’t many options left), it truly is likely someplace around 1 million machines. Tops.

there is a reason Microsoft remains prosperous. And this is it.

Now, IBM has put itself back into rivalry with Microsoft, and that is likely a good factor for its lengthy-time period financial fitness. whereas we are passionate about IBM’s efforts to sell extra Linux on energy programs for up to date workloads – all of the new and engaging simulation, modeling, desktop getting to know, and analytics application is at all times developed on Linux systems, and so are container structures for microservices style applications – there is not any manner that IBM could ever have constructed up the form of business red Hat has executed with the aid of pushing the Linuxes of others on its iron. I did an in depth analysis of the financials of the deal over at the next Platform, so if you want to see that, simply hit the hyperlink. What you need to examine right here in IBM i Land is what the acquisition means for power methods in everyday and IBM i in specific, and as I talked about closing week, how IBM will do a more robust job integrating crimson Hat’s stacks with IBM – devoid of disrupting things.

despite what Rometty and Jim Whitehurst, pink Hat’s chief executive officer, mentioned all the way through their conference name on Wall highway going over the deal, the place they both proclaimed there is no overlap in products from the two organizations, here is strictly no longer true and despite the fact that there is not overlap, there definitely is competition. If even IBM doesn’t knock out its own items with these from purple Hat, it could suggest that development on the native IBM products slows and even stalls out.

Let’s take a number of cases appropriate right here and now so you see my factor:

  • crimson Hat has its personal OpenStack distribution, referred to as OpenStack Platform; and IBM has its own known as PowerVC.
  • pink Hat has a Docker container orchestration platform known as OpenShift; and IBM has IBM Cloud private, as we mentioned closing week.
  • purple Hat has a multicloud administration stack that works at the digital laptop degree and can be prolonged to containers with OpenShift on top of it known as Cloud types; IBM has its new IBM Multicloud supervisor for containers and has a slew of virtualization administration equipment that it has bought over the years.
  • IBM sells AIX and IBM i on energy techniques and z/OS and other operating methods on its equipment z mainframes; Linux accounts for the majority of means bought on mainframes nowadays and has for years, and Linux is growing on vigor systems as a percent of capability offered. this is indirect competitors, intellect you, however there you've got it.
  • pink Hat sells JBoss Java application middleware, and it makes a lot of dough; IBM has WebSphere Java application middleware and it makes loads of dough.
  • pink Hat has the Gluster parallel file equipment and the Ceph object and block storage equipment; IBM has Spectrum Scale (formerly the accepted Parallel File gadget or GPFS) and Cloud Object Storage out on the IBM Cloud.
  • crimson Hat doesn't have a great deal in the method of relational database administration systems; IBM has Db2 in its a variety of guises. both peddle open source databases and datastores from the open supply group.
  • IBM sells SUSE Linux commercial enterprise Server and Canonical Ubuntu Server on energy techniques and system z mainframes.
  • purple Hat sells JBoss Developer Studio; IBM sells Rational Developer.
  • I don’t claim that here's an exhaustive list, however it hits the massive conflicts and overlaps and it most certainly is a non-zero number. IBM has committed to leaving purple Hat greater or much less by myself, in a “distinct” division the place Whitehurst reviews directly to Rometty, and materials of purple Hat’s could be stated in its Hybrid Cloud division and other elements in global functions, curiously. So there goes any visibility into what red Hat is basically doing once this deal closes. but the actual problem is not so tons that IBM should be tempted to stream corporations from its personal models of infrastructure software to the pink Hat types, and even wholesale off IBM i and AIX to Linux on vigour with Db2 at the back of it. We don’t think IBM will try this, at the least now not for a couple of years after red Hat’s business has been integrated and perhaps big chunks of its infrastructure utility. Our subject is extra that IBM will no longer fund building in its personal wares, and that AIX and IBM i shops will see utility tools that they use whither from forget about.

    this is now not anything else any of us should fret about for now, principally with the deal likely not closing for the more advantageous part of a year. nevertheless it is essential to keep an eye fixed on what IBM does with purple Hat and what it doesn’t do with its personal tools. and that's what we can do. it's why you retain us around, in spite of everything.

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    Watson economic features is born out of IBM’s buy of Promontory monetary community | killexams.com Real Questions and Pass4sure dumps

    If Nathan’s scorching Dog consuming Contest had a big information ingesting contest brother, IBM can be a major contender for first area. today the tech stalwart introduced that it had come to an contract to purchase Promontory fiscal group. 

    To make feel of this deal, you ought to keep away from relegating Promontory into the small container of economic services. instead, it’s most useful to think of it as a big records enterprise that additionally has a features enterprise. whereas it’s proper that it really works with some of the greatest banks on the planet, it has slowly amassed a group of regulatory and compliance statistics. Promontory also has a body of workers that comprises over 600 specialists within the area.

    earlier this year, IBM closed one other equally obscure deal to purchase The weather company. although, these two deals have a great deal in normal and point out that IBM is hungry for businesses in non-ordinary areas that have entry to tremendous stores of advice. The weather enterprise deal gave IBM Watson access to a huge assortment of weather data that helped to increase and add price to the Watson gadget in a extremely short time body. The Promontory facts will do tons the equal.

    Watson has grown to turn into one among IBM’s crown jewels. within the company’s Q2 incomes document, IBM introduced that its cognitive solutions division had posted revenues of $4.7 billion, up 4 % yr over 12 months.

    This deal puts IBM up in opposition t businesses like Thomson Reuters and Bloomberg that have their own regulatory and compliance solutions for the monetary capabilities business. These services are focused on monitoring, monitoring and analyzing regulatory adjustments. other companies like Palantir have developed prison and capital markets options for examining unstructured data for tasks like fraud creation.

    IBM hopes its new, smarter, Watson can more simply tackle projects like “monetary risk modeling, surveillance, anti-cash laundering, and be aware of Your client,” in accordance with a press release from the business.

    IBM may be including a brand new Watson monetary features unit to the enterprise’s new trade platforms enterprise created lower back in August.

    Assuming the deal closes, Promontory is expected to continue to function as its own business unit. This capability that clients of the enterprise will proceed to get hold of traditional functions, even as the business puts Watson via college. Gene Ludwig, CEO of Promontory, is also expected to stay in his leadership function all through the transition. If no further considerations arise, the deal will also be expected to shut late this 12 months.




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    How To Be a Superhero Software Developer, Agile or Not | killexams.com real questions and Pass4sure dumps

    The Agile Architect

    How To Be a Superhero Software Developer, Agile or Not

    If you've seen any trailers for movies lately, you know that superheroes are the new hotness. In a blatant rip-off of the genre, our Agile Architect describes how to have your own secret origin as a super software developer.

  • By Mark Balbes
  • 06/14/2016
  • Everyone who knows me knows that I collect comic books. I've long passed the days when I was somewhat ashamed to admit that a grown man's favorite super-hero is The Green Lantern. Now I'm taking the next step and writing about them in my purportedly technical column. For those of you waiting for an agile angle to this, trust me. I'll get there. For those of you happy to read about comic books. Cool! So let's dive in.  

    There are two main aspects to comic book collecting: collecting as a financial investment and collecting for enjoyment. While there are many people like me who purchase and read comics for enjoyment, I am always amazed at the people who collect comics only for investment purposes. They don't even read the comics they buy. In fact, there is a relatively recent initiative in comic book collecting to "slab" comics. That is, to have their condition professionally graded, then entomb the comic book in a sealed, archive quality plastic sleeve with a certificate indicating the condition. At this point, the comic book becomes, to me at least, a worthless commodity to be sold or traded purely for its perceived value. In other words, its valuable because somebody else is willing to buy something that they also cannot read or enjoy.

    Many of these investors spend a boat-load of money buying the new hot book on the shelf. I've seen people purchase hundreds of copies of a single book, thinking that each copy would someday bring them a fortune. It doesn't.

    In truth, if you are truly interested in making money on comic books, you should gather up as much money as you possibly can and invest it in a few (or even one) near-perfect copy of an early issue of a well known character like Superman or Batman. If you can afford to purchase and hold onto a copy of Action Comics #1 from June 1938, the first appearance of Superman, you are bound to make money. If you buy 200 copies of anything on the shelf today at $3 to $5 dollars apiece, they will probably bring you 25 cents apiece in a few years, maybe $1 if you are lucky.

    But if you collect comic books because you like to read them, and you buy the things you like to read, every once in a while there's a gem that makes it into your collection before it becomes valuable.

    So how does this relate to software and agile? I've been working with a group of recent Ph.D. physicists who are interested in moving from academia into commercial software development. While they are extremely bright and have some advanced algorithm programming skills, it's gotten me thinking about how to succeed in the commercial software industry. And as you may have guessed, there's a parallel to comic book collecting and it boils down to this:

    You can spend a lot of time chasing after the latest hotness, but in the end, your ability to craft professional-grade software depends on the investment you've made in understanding the fundamentals and your enjoyment of the current state-of-the-art technology.

    That's it. Pretty simple.

    And now I'm going to run the risk of complicating it by trying to explain it.

    First of all, I did not say not to learn the new shiny hotness. Just like with comic book collecting, there will be new technologies that catch your interest, either because they are directly applicable to your work or just because. Go for it. Learn it. Learning new things expands your horizons and gives you new insights into your craft.

    But, just as there isn't enough time to read every comic book published each month,  we are long past the days when we can learn every new technology that comes out. You can certainly read about them and play with them at a superficial level, but there isn't enough time in the day to be an expert at everything. And if you try, you won't have the time to learn any of them well. Better to pick the ones that you need for your current projects or that you truly enjoy and learn them thoroughly.

    And it's a tough choice, because there are all kinds of cool things out there, ranging from the Internet of Things (IoT) on the edge of our networks to Big Data distributed through our data centers, from iOS and Android native apps to highly available Web services fronted by Angular, React or any number of other web frameworks, from noSQL databases to Raspberry Pi's, and so much more. Oh, and let's not forget the drones!

    And while you are learning the cool things, make sure you understand the fundamentals, the things that don't change based on which technology you are using at the moment.

    Here's a list of some things I think every commercial software developer should know, agile or not, in no particular order.

  • Conversational UML: The Unified Modeling Language is a standard way to express object-oriented ideas. I've been in too many design discussions with developers who don't know UML who struggle to express their ideas to others. And even if they do, the artifacts they leave behind, e.g. whiteboard drawings, are indecipherable to anyone that comes late to the discussion. With UML, anyone can walk up to a diagram and understand its intent. Now, I'm not suggesting spending huge amounts of time learning all the nuances of UML. That's why I call this conversational UML. Just like any language, you need to know enough to express yourself. The UML is there to facilitate the conversations, not to replace it.
  • Design Patterns: The book "Design Patterns: Elements of Reusable Object-Oriented Software" by Gamma, Helm, Johnson, and Vlissides introduced the concept of design patterns to the world. It is still required reading today. Perhaps one of the single most productive learning experiences I have had as a software professional was absorbing this book over a weekend. If you've written object oriented software, you will recognize these patterns in your own work. Understanding them and being able to name them allows you to write better, more expressive software that can concisely convey the intent of your design through the use of these patterns and their names.
  • The Open-Closed Principle, Dependency Inversion Principle and Dependency injection: OK. This isn't just one thing but they are all related. The Open-Closed principal, which says that you should design things that are open to extension but closed to modification, sounds impossible to achieve but is quite simple if you understand dependency inversion, that the outside system should define the dependencies of an entity not the other way around, which can be implemented with dependency injection. In other words, when you build a class, function, etc., it should not have knowledge of the outside world. Anything external that it needs to function properly should be given to it. I acknowledge that this is very much an object-oriented concept. As I am learning more about functional languages, I'm curious to see if there is an analogous concept.
  • Version Control with Branching and Merging Strategies: Every developer that works in collaboration with others needs a basic understanding of how to use a version control system to manage their code base. Modern tools like Git provide great power in terms of the ability to easily create diverging branches of code and subsequently merge them back together. But the tool by itself doesn't make managing code easier. In fact, it can help you to quickly make quite a mess of things. Understanding concepts like a stable master branch strategy, an unstable master branch strategy and Branch By Abstraction and how these are different from branching as code promotion strategies can make your life much easier.
  • Automated Testing: Automated testing comes in many flavors from low-level unit testing up to large scalability testing, from functional testing to user acceptance testing, from performance testing to integration testing. And these can be done in a variety of ways from after-the-fact testing to the more agile test-driven and behavior-driven development. Knowing how to build robust tests, and knowing which tests not to write, can mean the difference between stable, well constructed software and a real mess of missed deadlines and broken promises.
  • Build Systems: Having the ability to build your software in a single, well-defined, well-controlled environment avoids the "it works on my computer" syndrome. Build systems like Jenkins and TFS not only compile your code but can also deploy and run all of your automated tests. Having a reliable build system is an important step in creating a successful devOps strategy.
  • Deployment Strategies: Having a well thought out deployment strategy for your software is important to ensure its stability in production. I've seen too many companies build their software in place on their production systems. Since they've bootstrapped their way, they have no repeatable process to stand up another system. When their product is successful and they need to replicate it, e.g. for scaling to support more users, they have no mechanism to do this. Understanding how to deploy your software means you can set up multiple identical, or nearly-identical, development, QA and production environments at any time.
  • Memory Management: While many modern languages don't explicitly require you to manage memory directly, it's still important to understand how your software allocates and frees memory. If you haven't learned a lower-level language like C that requires you to allocate and deallocate memory explicitly, then you run the risk of being too cavalier in your trust of garbage collectors and other automated memory management solutions. Any system can have memory leaks if you don't understand what they are doing under the hood.
  • Performance Tuning and Testing: Every developer needs to understand when and how to tune performance. Prematurely optimize and you run the risk of creating unnecessarily poor designs. Don't think about optimization at all and your software may die by a death of a thousand cuts. That is, too many slow implementations scattered throughout your code making it impossible to optimize. Of course, agile practices can help alleviate that issue.
  • Debugging: I'm not just talking about how to use a debugger. I am talking about general techniques to track down defects in your software. Knowing how to debug your code is important for multiple reasons. First, it means that you can track down defects quickly. Second, it informs you on how to best instrument your code to help track down problems while supporting the production application.
  • User-Centered Design: This is a slightly different beast from the topics above. Everyone on a software team, not just the UX person or the developers, should understand the importance, benefits and practices of user-centered design.  I once worked on a large multi-tiered application where one of the developers said, totally seriously, "Let's build the server first and then see what it can do." Umm, no.
  • Final Thoughts... So there you have it. Superman, science and software all wrapped up with a neat little bow. I didn't really tell you how to be a software superhero nor did I provide you with an exhaustive list of skills that modern software developers need. But I hope I at least got you thinking about your own skills and how they line up to the list above.

    About the Author

    Dr. Mark Balbes serves as Vice President, Architecture at WWT Asynchrony Labs, and leads multiple Agile projects for Government and Fortune 500 companies. He received his Ph.D. in Nuclear Physics from Duke University in 1992, then continued his research in nuclear astrophysics at Ohio State University. Dr. Balbes has worked in the industrial sector since 1995 applying his scientific expertise to the disciplines of software development. He has led teams as small as a few software developers to as large as a multi-national Engineering department with development centers in the U.S., Canada, and India. Whether serving as product manager, chief scientist, or chief architect, he provides both technical and thought leadership around Agile development, Agile architecture, and Agile project management principles.


    AI World: What About Hardware? | killexams.com real questions and Pass4sure dumps

    While December's AI world conference was not primarily a hardware-based affair, it's hard to think about all of the potential machine learning applications and software without considering what it's going to run on. Also, there are the distinct categories of hardware platform.

    Algorithm and Model Development

    This is where you play with smaller data sets and think about how to organize and structure of the input data. (Note: this is often a very subtle and artful task.) In addition you make decisions about what type of machine learning will be applied. These days in the case of very large and complex problems this means some version of a deep Convolutional Neural Net (CNN). Those of you in the high priesthood of AI would create, modify, or the experiment with the actual source code that constructs these neural nets. For the simpler problems with smallish data sets this platform can be a fairly standard multicore desktop with enough RAM to hold your problem in memory. But to play at the higher end of the spectrum you will need significantly more power. Fortunately several vendors are working on specialized workstations configured especially for machine learning. Much like the demand for CAD/CAM engineering workstations drove specialized interactive graphics configurations two decades ago, the demands of AI development are driving the market for AI focused desktops. Dell was one of the exhibitors at the AI World conference and demonstrated some fully tricked out Dell/Nvidia systems that would fit under your desk (and most certainly keep your feet warm too). These systems fit into a conventional PC box but are loaded with CPUs that have a dozen or more cores as well as one, two or even three Nvidia cards (Teslas or Quadros), and lest we forget 256 GB of RAM (or even more with the new crossbar memristor boards). 

    Sandbox Training

    Of course, there's a lot of overlap in each of these categories, but once you have a development version of your system you will want to train it with a significantly larger data set. Usually, this would be a small rack-based system that still fits within the lab context. Although at this point in the progression there is a significant consideration about how sensitive the data is and where it resides. If the data is not particularly sensitive (medical or financial) and is already available to the world at large then you might consider migrating to a cloud-based solution at this point. But for any number of reasons and institutions may not be willing to copy all of their data up to a server site nor would they be willing to make ready access to that data over the Internet. Regardless of whether you do this sandbox training in the cloud or in the lab, you will want to be able to demonstrate that the performance of the neural net improves significantly with more training data. You will want to explore and examine all of the idiosyncrasies of your new clever creation: false positives, false negatives, are categories in your data set underrepresented, etc. 

    Production Training

    Production training is only a distinct next step if you have truly huge amounts of data (exabytes). If your decision is to do production training in the cloud then the remaining consideration is how often will you want or need to retrain? If you don't expect your training data to change very much and you can get away with training every few months, then it will be cost-effective to rent compute cycles from one of the many vendors (e.g. AWS). Note: if the cloud environment is not exactly compatible with your code from the sandbox cycle, then you will have some code rewriting that will force you to do more quality control to make sure you haven't changed the logic of your underlying system in some way. If your training data is not static (or nearly static) you will need to train more often. Obviously if you're modeling stock prices then you must retrain regularly (perhaps daily or even hourly). So, if you are data is very dynamic and sensitive or proprietary you will need to seriously consider assembling your own dedicated AI data center.

    Production Execution

    Finally, we can address the issue of ML training versus ML execution.  Neural net technology is loosely based on biological learning and our naïve and primitive understanding about how a brain works, it's not surprising that learning a skill takes a great deal more time and energy than it does to perform an already learned skill. Learning to read or to ride a bike seems challenging and takes a lot of concentration at the time, but after they are learned they are seemingly effortless. Executing the training is much easier than the training was.

    Many of us have heard the tales of deep CNNs being trained on exabytes of data using millions of computing cores and taking hundreds of hours using thousands of kilowatt hours of electricity to to be able to distinguish between pictures of blueberry muffins and Chihuahuas. (Clearly, we haven't quite figured out how real brains work because the human brain uses about 20 W of power and can figure out the dog muffin quandary in a few seconds. Deep CNNs have a ways to go.)

    But I digress, once all the heavy computational lifting of training is done the much lighter load of using that training is what you have to support. Most of us remember solving multiple linear equations with multiple variables back in algebra class. Solving for all of the coefficients for the predictive equation took some time (if there were a large number of variables it took a lot of time). But, once you had that predictive equation (a.k.a. the solution) it was much easier to plug in new input parameters and instantly get a predictive result. (Note: Previous generations' AI were often based on the simplex method, which is a more generalized algorithm for solving multiple linear equations and deals with approximate solutions.) Another excellent example of the asymmetry in computing between training and execution is in the camera functionality of almost everyone's phone. A lot of data and computing time was put into training an algorithm to recognize faces. That algorithm can easily execute as a background process in real time identifying multiple faces.

    The point is that the vast majority of the expense for these amazing machine learning systems is in discovering the approach to the problem coupled with the actual training with huge data sets. I think of it as paying for college. (But now at cocktail parties we can all easily pontificate about Baudelaire. predictive branching, mitosis, etc. ad nauseam.)

    Other Systems

    At the exhibition, there were examples of systems and hardware that could tackle the spectrum of these problems. One of the corporate hosts of AI World was IBM Watson which could provide the racks of hardware necessary to run big problems. Watson provided a range of higher-level tools designed to digest raw input (e.g. technical journal articles) into a form useful for identifying patterns and correlations, and ultimately being able to generate responses to queries against that input data. We all remember what a good job Watson did while playing Jeopardy. IBM Watson represented the "many racks of multicore computers on site" type of solution. This approach assumed you would be feeding input data on a regular basis to a production scale system that can be interrogated in a regular and ongoing fashion.

    Another hardware/cloud company at the conference was DataRobot which provides a platform for which a wide range of open source AI algorithms and supporting systems are in place and ready to use.

    At the other extreme were purely executable solutions such as Affectiva that solved a specific and unchanging domain. The Affectiva product is a vision system that identifies facial emotions in real time. All of the system learning was done by the company and that learning could be executed on a lightweight platform. In fact, this company could execute their algorithm on a Raspberry Pi.

    There were other companies that provided systems ready to learn specific things (e.g. index raw video/audio). But that topic requires a deeper dive and another post (I'll keep you posted?). So until then...

    Topics:

    ai ,hardware ,convolutional neural networks ,sandbox



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