HMG Strategy, the World’s #1 platform for enabling technology executives to reimagine the enterprise and reshape the business world, is excited to

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Moveworks, based out of Mountain View, California, has earned a spot on Newsweek's 2023 list of America's 100 Most Loved Workplaces.

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Lakeside Software, the leading IT data intelligence company, unveiled major enhancements to its SysTrack platform today, launching multiple generative AI features to enhance solutions for customers.

Moveworks is an AI platform that works to improve a company's relationship with its staff by providing a better way to support employees. Using their deep learning and AI technology, Moveworks provides staff with everything from IT support to HR help.

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Since launching on Teams about four years ago, Moveworks has earned dozens of Fortune 500 customers by saving countless hours of employee time, making work more efficient, and reducing millions of dollars in IT support costs. Three key strategies helped ...

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Looking ahead, HMG Strategy will also be hosting its 2023 Silicon Valley CIO Executive Leadership Summit on October 17 at the Hyatt Regency Santa Clara. Key topics to be explored at the summit include the role of the CIO as the CEO of technology in preparing the organization for what’s coming next.

CIO Leadership: Leading as the CEO of Technology Will Drive the Discussion at the 2023 Minneapolis CIO Executive Leadership Summit on November 2

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Artificial Intelligence is a lot more than a tech buzzword these days. This technology has disrupted almost every industry within a decade. Every company wants to implement this cutting edge technology in its system to cut costs, save time, and make the overall process more efficient with automation. Initially, AI and were as simple as Simple Perceptron models and Single layered neural networks. With the passage of time and focused research, we have complex neural networks with multi-layered architectures. Companies are using algorithms such as LSTMs, GANs, variational autoencoders in their software and services.. deep learning algorithms Given below are the hottest topics in AI and the companies working on them. Get ready to get awestruck after looking at these innovations. However, if you want to explore further, you can explore it here tl motion simulator Natural Language Processing - NLP is one of the hottest topics in AI. NLP deals with language (textual data) and performs tasks such as translation, transliteration, semantic analysis, development of chatbots, text mimicry, text to speech, etc. In the present day scenario, NLP is very crucial for organizations because it has disrupted customer support by automating the process and reducing human intervention. Natural Language Processing In addition to this, NLP techniques are also utilized in developing marketing strategies on the basis of semantic analysis done on text mined from social media platforms. In its crux, most of the NLP algorithms are based on complex deep neural networks such as RNNs, LSTMs, and GRUs. To draw a basic intuition, you can look at them as conventional neural networks that emphasize on previous data inputs (i.e. memory). Moveworks and Dialogflow are one of the leading companies based on NLP. Let us take a brief look at them. Dialogflow Dialogflow is Google’s child company which focuses on latest research on NLP techniques and develops modules, APIs, and platforms related to it. You can integrate Dialogflow’s services to Amazon Alexa, Siri, Cortana, and of course - Google Home. Its services use Long Short Term Memory (LSTM) and Recurrent Neural Networks to execute tasks related to texts and languages. Dialogflow is well known for its chatbot service. In addition to this, companies such as Dominos, Mercedes, Giorgio Armani are taking the help of Dialogflow for embedding chatbots and text to speech services within their system. You can easily read Dialogflow’s documentation and have your own chatbot ready on your website/mobile application in a matter of minutes. Moveworks Moveworks is a $200 Million company based in Mountain View, California. It was founded by Bhavesh Shah, Jiang Chen, Vaibhav Nivargi, and Varun Singh. The foundation of every chatbot and NLP product is laid by RNN or its successors. In its core, Moveworks service is also based on their custom recurrent neural networks and LSTM network trained on relevant data. Moveworks provides customer support automation and NLP solutions for business enterprises. It has served giants such as Nutanix, Autodesk, and Western Digital. If you want to cut down the IT support and automate it to save costs, you must definitely consider Moveworks. Convolutional Neural Networks or CNNs are the most widely used deep learning algorithms. CNN algorithm was a slight modification to conventional neural networks, which further emerged as a revolutionary concept in the field of artificial intelligence. Today, CNNs are used mainly for object detection, face recognition, computer vision, and forecasting techniques. Convolutional Neural Networks Convolutional neural networks involve the stacking of layers in a deep neural network with operations such as padding, convolution, scaling, etc. on the input image. The best part about CNN is that the images are converted into arrays (almost similar to the 1-dimensional matrix) and mathematical operations take place which finally results in the desired output. To implement CNN, you just need a lot of data and a computer to train your AI. Here are a couple of companies that are working on computer vision and CNNs. Matterport Matterport came into limelight when their R&D team published a paper on Mask RCNN. The company is based in Sunnyvale, California is one of the patrons of computer vision products. Moreover, they have developed an efficient computer vision algorithm used for detecting objects (Mask RCNN) which is a successor of Fast RCNN and Faster RCNN. It uses coordinates of bounding boxes and masking techniques to identify objects on which the model is trained. Apart from object detection, matterport’s Mask RCNN is also used in video calling applications to change the background. Currently, they’ve integrated augmented reality with deep learning and have invented one of the finest 3-D cameras of modern times. Matterport manufactures 3-D cameras that can be used for visualizing structures of houses, give virtual tours, and display an engaging floor plan. Neurala Based in Boston, this machine learning and software development company caters to the needs of organizations looking for process automation and computer vision solutions. Neurala’s models are used on more than 50 million devices across the globe. CB Insights has recognized Neurala amongst ‘100 Most Promising AI Companies’. Thanks to its Ph.D. holding founders who made Neurala’s AI software run on light devices including smartphones. They provide services on a wide range of devices including PCs, smartphones, drones, robots, and smart devices. These were some of the most reliable and best companies which might help you in building AI-based systems. Before blindly trusting any company on the internet, you must also take a look at these firms which might help you in cutting costs and provide you a state of the art service. Many AI developers have found that CBD has helped their clarity and consistency when developing algorithms, one of the leaders in this field has actually published an article talking about how CBD can assist with focus while programming. Wtphemp offers that are completely free from THC. Wtphemp CBD products

A Deep Learning Overview: NLP vs CNN

Let’s talk today about the mass adoption of the blockchain. It's something that people in this sphere are looking forward to. We really would love that to happen. And obviously, we would love that to happen soon. Is it going to happen soon or not? That's a big question actually. And I think it highly depends on how things go. Listen to the podcast on Captivate here But at the moment there are a few things that I think we have to address and look into. Those are going to impact the speed of the adoption. Which are the issues? To be fair, I’ve been in this space for a bit over three years and I thought that it was going to happen but now when I'm revisiting and looking at things and seeing how things are done, I'm not so sure that it's going to be happening as soon as I thought it would. I see several things that are done at the moment. I'm looking at this from the non-tech perspective, from the human side of it and I am representing the masses. I'm putting myself in the shoes of the masses that are supposed to adopt the blockchain. So where are the problems? First of all the masses and the blockchain needs a bridge. The bridge is basically all these projects that are creating some things people. Let's look at what's happened on that side. I may say something very obvious, but I feel like some need to hear this. Not only the projects that are creating services and products need to make sure that they are actually solving a problem, which NOT always is the case. They need not ONLY to  solve a problem, but solve a problem that MASSES have. Because very often, even when the blockchain projects are solving a problem, they are solving a problem for a very niche market for, for themselves, for tech people, for blockchain enthusiasts, crypto people, but NOT for the masses. So what happens when you are solving a problem that is very niche and it's a niche within the blockchain already? If you are solving a problem that blockchain people have, then that's who are going to adopt your solution.  And the adoption probably will not go beyond that niche. The easiest and fastest way to contribute to mass adoption would be trying to solve a problem that non-crypto people have, that people who don't care or are not very inspired by blockchain technology have and solve it for them. And make sure that when you solve that problem, you create the tools and the bridges that are very easy for the non-crypto people to cross. That's where another problem is lying. The problem comes from the fact that most of the blockchain founders are absolutely in love with the technology and are focused on building the tech. But when you are stuck in the technology sphere, when you talk only to other founders, when you talk only about the tech, you end up becoming isolated and detached from the non-tech crowds, right? So the more detached you get, the less you understand what their needs are, what their desires are, what they are looking for, and what their problems are. As a result, you won't even be able to come up with a solution to a problem that they have, because most probably you won't even know what their problems are. The deeper you get into the tech and the more isolated you get the bigger will become the gap between the masses and yourself. And then the harder it will be to build those bridges between the two. So don't get stuck and don't talk only to people in your field. Get out there and go broader and talk to more people, understand what's going on in the world. The world doesn't rotate around blockchain. Fortunately, or unfortunately, not everyone is as inspired about the tech and as excited about blockchain as you are. Another thing is the language: the language that you use. For example, I'm originally from Armenia and Armenian is my mother tongue. So imagine if I didn't speak English and I did this podcast in Armenian. I'm sure that no one would listen. No one would understand and there would be no engagement and whatever I'm telling you would be completely lost in translation. That's, what's happening very often with projects. They are so obsessed with the tech and use all these tech terms. They forget sometimes that the masses who are supposed to adopt that technology, have no clue what they're talking about. These days, people are leading in a hectic world. They are busy. They are running around and their attention span is very short. If you bet that this person is going to be so curious that he's going to go and start digging and looking into the terms and trying to understand what those mean, it's not going to happen. It's just not how life works. No one is going to do that. Just a simple example is the website copy… I go to the website and sometimes I read the whole page and I still have no clue what the project is about. I still have no clue what's the offering, which means that I don't even know whether it's for me or not. And I spend the time going over it. Not everyone is going to spend this time and try to read the whole thing. All they will do is just skip it and move on. So that's another thing that is not very complex. Just pay attention to who you're talking to and what are you saying. People can't get excited about something they don't understand. Usually, something that is unclear seems complex. It scares you and it makes you avoid it altogether. It just makes you think that it's probably not for you and you just move on. Let’s get to another reason that the masses don’t adopt blockchain. Very often blockchain projects bet on the fact that people are going to change their lifestyles. We talk about mass adoption through education. More clear and more simple materials for non-techie people to learn about blockchain and how things work is very useful obviously. But what we don't understand is that most of the people actually are not really going to want to learn about the tech behind it. They just don’t want to spend the time and effort to learn this - and they don’t need to! Most of the people probably will prefer to watch something on Netflix instead of going and reading a book about blockchain or watching a video about blockchain. Let's be realistic about it. So the educational route is betting on the fact that the people will be so intrigued and so curious that they're going to go out there and spend time and effort to learn about it. Sorry to shatter your illusions but is NOT going to be the case. The other problem is that all the solutions usually include a learning curve and there are loads of barriers to entry for masses. Even a simple thing, like opening a crypto wallet is not a straightforward process. Things are not so simple. So people will have to put extra effort in order to try things out. We need more effortless and simpler solutions, just a few clicks, very easy, not scary, nothing risky kind of thing that is going to make people use the thing without realizing that they are using blockchain and without needing to know how it works. When I'm sending an email I don't care how that email got from my inbox to your inbox.  I care about how long it takes and that my message actually gets into your inbox and that it took me just a few clicks. And it was very simple. That's all I care about. So what technology is underneath it - I don't care. All I care about is how easy for me it is to use and how well it works. And the problem now is that there are very few  blockchain projects that have come out with mass appeal products that are also easy to use. One of the examples that I'm always giving, because I use that on a daily basis is the Brave browser. And that is why it has millions of users, because I don't need to know about tech in order to use the Brave browser. I don't need to change my lifestyle or habits or learn something new in order to be able to use that browser because I use it as any other browser. So there is no rocket science behind it to learn and that's the way to go. Back in 2017, during the ICO, boom, the communities were mainly about communities, token buyers, right? But masses are not token holders. They are not token traders. They don't participate in any other kinds of tokenized crowdfunding mechanisms. So, what if you want to reach mass adoption as a B2C project? What you have to build is NOT a community of token buyers, but a community of your FUTURE USERS. So I guess what one needs to consider while building their blockchain startup is - stepping out of the blockchain bubble and become non-crypto customer-centric... and then maybe, the mass adoption process will move faster. Meanwhile, if you'd like to get more content about blockchain, startups and marketing make sure you check my website.

Blockchain Mass Adoption: What Are The Main Blockers?

Apps. Machine learning. Artificial intelligence. Big data. Blockchain. Being a programer keeping up with new tech can prove difficult. It’s like being a kid in a candy store. In my case, I have had trouble finding the store before I even got the chance to taste the candy. There is no doubt that the is one of the most hyped technologies at the moment. As such, you would have thought finding info beyond mere general technical definitions is easy. However, that is not the case. #blockchain As most people I to have thought of the blockchain as a ’coin’ thing. After all everything related to the term for that matter is somewhat stigmatized and widely labeled as ‘stuff’. Bitcoin/Ethereum/Coin And the truth of the matter is that the blockchain is so much more than coin and coin mining. Being a newbie in this vast space of a new technology I have had a hard time finding resources and learning the ropes. The definition and the example Blockchain is essentially a sequential, distributed ledger of transactions that is constantly updated on a global network of computers. The ownership and history of a transaction is embedded in the blockchain at the transaction’s earliest stages and verified at every subsequent stage. Source: https://en.wikipedia.org/wiki/Blockchain To put that in perspective of a real world situation, what makes blockchain special is its ability to enable the safe exchange of value (value is whatever the parties have agreed it to be) without a central verifying partner, making transactions faster and less expensive. When it comes to Bitcoin and coins in general, it means you are taking away the middleman (the bank) from the equation allowing direct transfer of coins between two parties securely noted in a blockchain ledger shared, encrypted and secured across the network of computers. It is hard being a beginner in blockchain To an actual beginner interested in learning everything about blockchain technologies all this nerdy talk explains hardly anything. What’s more, for those first timers venturing into the blockchain finding legit sources among thousand upon thousands of scam and spam pages, is a tantalizing task. That can be rather discouraging. So, what’s the best road to take as a beginner in blockchain? Well, we have lectures from a reliable sources like , and . Webinars from known websites like . Now, that’s all fine and well, but I am a firm believer that the best way to learn is to be engaged in a workshop that would lead you through the process step-by-step. A workshop that is made to encourage beginners and rookies to venture in blockchain tech. MIT Berkley Coursera Blockchain Council The best way to learn is from the experience of others in workshops that are designed to educate and teach but also to share experience and collaborate with blockchain experts. Having an expert guiding you through these murky waters is by far the best option if you want to take a shot at blockchain. However, there aren’t many workshops like that. IBM Blockchain Workshop There is no doubt that the computer giant IBM is one of the best places to start or evolve your blockchain journey. The IBM Blockchain Workshop has multiple locations available worldwide, bringing business leaders, technical architects and developers together to create a deeper understanding of blockchain. Visit IBM Blockchain Workshop here: https://www.ibm.com/blockchain/garage/ Blockgeeks Courses Blockgeeks.com was the first real source on my blockchain path. It is a leading online learning platform that helps anyone learn Blockchain in business, software, technology and creative skills to achieve personal and professional goals. Through individual, corporate, academic and government subscriptions, members have access to the Blockgeeks video library of engaging, top-quality courses taught by recognized industry experts. Visit Blockgeeks here: https://blockgeeks.com/ Blockchain Workshop As far as I am aware of, these guys are among just a handful in business offering thoughtful, insightful and customized blockchain workshops and lectures. Not bad considering This came as bit of a surprise since they are from a small European country. Their tailored workshops aim at helping you explore and understand full potential of the blockchain technologies for your business rather than just understand the blockchain princips. Visit Blockchain Workshop here: https://blockchain.mvpworkshop.co Technology and Applications Unchaining the Blockchain Although this one deals with fundamentals of blockchain, the technology, characteristics and implications and it is available for everyone it is, in its actuality, a workshop made for those into research and development. Some basic knowledge is needed and especially interesting part are hands-on sessions which are to make sure that all attendees understand the functionality of blockchain technologies. Visit Unchaining the Blockchain here: https://www.vdi-wissensforum.de/en/event/blockchain/ GitHub’s Blockchain for Developers: From Zero to Running app in the Cloud As with all GitHub things some knowledge is needed to understand and participate in. Though not a workshop strictly speaking this one derived from a talk about Blockchain and Hyperledger. Visit GitHub Blockchain here: https://github.com/LennartFr/blockchain-workshop-2018 CryptoZombies Thinking out of the box can get you far. People at CryptoZombiess are trying exactly that and they offer a free, interactive code school that teaches you to build games on Ethereum. The course is designed for beginners to Solidity and starts off with the absolute basics providing you with a step-by-step guide in your game making path. Visit CryptoZombies here: https://cryptozombies.io This is great but your list is too short. What’s up with that? Frankly, the current state of the industry is focused on conferences and events, which offer an increasing amount of workshops and small number attendee sessions. Even the almighty Google associates keyword ‘ ’ more with conferences offering one than real deal workshops. Not sure if that’s the intent behind the search query (I know it wasn’t mine) but it is a current trend. Navigating through all this is a huge pain in the arse and as such it fails short of having all the workshops at one place. blockchain workshop However, it is a beginning much like my own in the world of blockchain. I am yet to start my very first block…but that’s another story. I’ll keep you posted. If you enjoyed this story, please click the 👏 button, follow me at , and and share it to help others find it! Medium Twitter Facebook Have I missed something? Let me know. Share your thoughts in the comment section below.

Blockchain Workshops: The Complete List For 2018

A case study between Python and Go. In Go, a common complaint from newly-minted gophers who come from another language is the error handling pattern: result, err := DoSomething()
if err != nil {
    return nil, err
}

another, err := SomethingElse(result)
if err != nil {
    return nil, err
}

// We can be a bit more terse if we don't need to save any
// variables outside of the if-scope:
if err := MoreWork(another); err != nil {
    return nil, err
}
... Any time we write similar-looking code over and over, that’s considered . boilerplate A key principle of programming that we all learn is (DRY). In a way, that’s the entire point of programming: To automate a task that we keep repeating manually. Who wants to keep artisanally sorting text files each morning when we could have a program do it for us faster and more reliably? Similarly, why should we keep writing some code pattern over and over when we should be able to abstract it away into some terser syntax? Don’t Repeat Yourself Proportional work After writing Python code for over a decade and becoming quite proficient at it, I’d take any opportunity to write terse code. It feels good to be clever and , right? more code means more bugs One thing I didn’t notice until I switched to writing Go as my primary language: The done in Python code is hugely variable. proportionality of work Consider these two lines: What do I mean by proportionality? a = some_variable + 42
b = sum((j if j % 2 else 0) for (j, k) in results if k) The second line does more work than the first line. The first line is a simple integer addition, while the second line has a loop and a variable split and two conditional branches and an accumulator on a generator. a lot In Python (and many other languages), it’s very easy to unintentionally hide of work in a single line of built-in syntax. Some lines do almost no work, other lines do a moderate amount of work, other lines do what I’d describe is tens of lines worth of work—a lot of work. It varies a lot, even in beginner code. tons In Go, I’ve found that this variability is much more diminished. A loop is always a loop, an error check is always an error check (rather than implicit exception propagation), a logic branch is always a branch. This is the most reasonably-terse way I could write the above in Go: b := 0
for _, r := range results {
    j, k := r[0], r[1]
    if !k || j % 2 != 0 {
        continue
    }
    b += j
} Is this good? I believe it is. I believe this code is more proportional to the complexity of the work that it’s doing. It’s also possible to write Python in a similar level of proportionality, why don’t I? When reading proportional code, it’s easier to notice where the interesting bits are. It’s easier to notice where the bulk of the work is being done, and focus our review on that. Maybe this is subjective but I posit that the code’s looks more like what it is. shape We can continue to argue that it’s easier to what the work is actually doing, but that’s not a point I want to make. Instead, I want to make a different point: It’s easier to tweak what the code is doing. understand Opportunity for correction After writing of code in Python, and writing a moderate amount of code in Go, I noticed an interesting phenomenon: I tweak code very differently in Go. a lot Programming is a very iterative process. As we implement our solution, our mental model of the problem evolves. We often go back and change some assumptions, tweak our data structures, remove obsolete crutches and stubs, make error handling more robust, and so on. I noticed that when I wrote Python code, I would often to avoid messing up a beautiful terse set of lines by moving logic to places other than where it might naturally belong. I would unconsciously move complexity from where it belongs to a tangential place that made the tweak more complex than it should have been. go out of my way For example: Rather than refactoring a try/except block into separate appropriately-layered method calls or multiple try/except phases, I’d introduce a variable to maintain error state that gets handled in a finally block. ( .) An example of this pattern in urllib3 When I write Go, I noticed that my tweaks almost always go exactly where they naturally belong. My boilerplate doesn’t stay untouched! For example: I’ll usually write lots of Go boilerplate in my first iteration, but by the time I look at my code closer to release I notice that it’s no longer looking like boilerplate. By the time I’m done—adding more recovery scenarios, better logging, augmenting errors with more context, handle more edge cases—most of my boilerplate gets a lot more interesting. I did not realize that good boilerplate could be fertile ground for iteration. When a friend was reading through the , he was surprised that I implemented my own type. I explained that while Go doesn’t have a built-in it’s just a few lines of boilerplate to make your own on top of a . In fact, I noticed that evolved to be fairly specific to how it was being used. In retrospect, I’m glad that I was tweaking my own implementation iteratively rather than spending time working around whichever limitations a generic library might have had. source code for ssh-chat Set Set, map my version of Set I realize now that disproportional code makes me unintentionally dance it like tiny black holes of complexity. Having boilerplate helps balance out the ease of modifying it with the amount of work that code is actually doing—whether it’s error handling or looping or whatever else. around proportional Proportional code creates less complexity I am not claiming that more lines of proportional code is less buggy than fewer lines of disproportional code. I am not claiming that all boilerplate is good: boilerplate can be disproportional in the other direction, too—where it’s doing far work than we’d expect. less My claim is: For non-trivial projects, proportional code is not cumulatively more lines than disproportional code. When we write disproportional code (or code that varies a lot in proportionality), we shift natural complexity into unnatural places which is an unexpected sacrifice. Our beautiful one-liners are short indeed, but the rest of the project suffers in other places to pay off that purchase. By the time we’re done iterating and fleshing out our project, we might not be winning as much as we’re losing from getting rid of boilerplate indiscriminately. boilerplate can help us iterate in a less constrained way. Proportional

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