Chaos, Chaos Everywhere

Picture this: You’re in a video game, running around with no map or quest log. Sounds frustrating, right? Well, that’s what it feels like without a PMO. Without a centralized authority overseeing projects, it’s like each department is off doing its own thing, with no coordination. The result? Chaos. Inefficient processes, duplicated efforts, and a tangled mess of crossed wires. A PMO brings order to the chaos, providing structure, streamlining processes, and ensuring everyone are on the same quest to conquer business success.

The Mysterious Vanishing Act

In some games, your character might suddenly disappear into thin air. It’s a glitch that leaves you scratching your head, wondering where they went. Without a PMO, it’s a similar story for projects. They vanish into thin air, never to be seen or heard from again. Why? Because without proper visibility and control, projects can easily go off track, deadlines get missed, and stakeholders are left in the dark. But fear not! A PMO provides the magical ability to keep projects in sight at all times, tracking progress, identifying roadblocks, and making sure everyone is on the same page.

Resource Roulette

Imagine playing a game without knowing which power-ups or abilities are available to you. It’s like spinning a roulette wheel, hoping for the best. Well, without a PMO, resource allocation feels a lot like that. You’re left guessing which team has the skills, time, and resources to tackle a project. The result? You end up with overworked heroes, underutilized talents, and an imbalanced allocation of resources. But with a PMO, it’s like having a strategy guide. You know exactly who has what skills, who’s available, and how to optimize your resources for maximum impact.

The “Oops, I Failed” Boss Battle

In any game, boss battles are epic showdowns where you either triumph or fail spectacularly. But without a PMO, projects can quickly turn into a never-ending boss battle of failure. Risks are overlooked, issues escalate, and before you know it, your project is defeated. But fear not, brave entrepreneur! With a PMO, you gain the power of proactive risk management. You can identify potential pitfalls, mitigate risks, and face your projects’ boss battles head-on, armed with a strategic plan for success.

The Side Quest Dilemma

In games, side quests can be fun distractions, but they often derail you from the main storyline. Well, without a PMO, it’s like every department is on a never-ending side quest. Each team is focused on its own objectives, losing sight of the big picture and the overarching business strategy. But with a PMO, you become the master quest giver, aligning projects with your strategic goals and ensuring that everyone is on track to complete the main quest for success.

The Hidden Level of Wisdom

Every gamer knows that there are hidden levels, secret items, and valuable knowledge to be found. But without a PMO, that knowledge remains hidden within individual teams. Lessons learned are lost, best practices are scattered, and the opportunity for growth slips away. But fear not, intrepid business explorer! With a PMO, you unlock the hidden level of wisdom. It becomes a platform for collaboration, knowledge sharing, and continuous learning, empowering your teams to level up their skills and achieve greatness together.

Conclusion

So, my fellow business warriors, as you embark on your entrepreneurial journey, remember this: Skipping the Project Management Office (PMO) is like choosing “hard mode” in the game of business. Chaos, disappearing projects, resource roulette, failure boss battles, side quest dilemmas, and hidden knowledge levels await those who dare to venture without a PMO. But fear not! Embrace the power of the PMO, and you’ll be equipped with the tools, strategies, and support you need to conquer the game of business and emerge as the true champion! Game on!

Originally published at http://agilecreativelabs.ca on June 11, 2023.

Alright, fellow startup enthusiasts, gather ‘round! We’re about to embark on an exciting adventure where we explore the modern, witty, and fun side of having a Project Management Office (PMO) for our beloved startups. Buckle up and get ready to unleash the power of project management like never before!

Supercharging Startup Success

Hey, we all know startups are like rockets fueled by dreams and caffeine. But to reach the stars, we need a guiding force-a PMO. Think of it as the superhero sidekick to our ambitious startup endeavors. With its strategic alignment mojo, the PMO ensures every project zooms straight toward our goals, leaving no room for wasted efforts or misguided ventures. It’s like having a personal GPS that keeps us on track and prevents us from veering off into the abyss of aimlessness.

PMO: The Guardian of Project Governance

Let’s face it, startups can be a bit chaotic. We’re all about embracing the unknown and thinking outside the box. But sometimes, we need a steady hand to keep things in check. That’s where our PMO swoops in, wearing its cape of project governance glory. With standardized processes and communication superpowers, it brings order to the chaos, ensuring projects are executed like a well-choreographed dance. Plus, it gives us the confidence to report to stakeholders with a sly grin, knowing our projects are under control.

Resource Allocation: From Chaos to Super Efficiency

In the startup universe, resources are as precious as that last slice of pizza. With a PMO in the mix, we can transform resource allocation from a messy free-for-all to a finely tuned symphony. The PMO acts as our superhero matchmaker, connecting the right resources with the right projects at the right time. It’s like having a dating app that pairs our talented team members with projects that make their skills shine. Say goodbye to resource clashes and hello to super efficiency!

Embracing the Quirk: Modern PMO Magic

Who said project management has to be boring? Our PMO breaks the mold and embraces the modern startup spirit. It’s the Robin Hood of standardized processes, stealing best practices from the corporate world and tailoring them to our funky startup vibe. It brings a touch of quirkiness to project management, allowing us to blaze our own trail while still benefiting from tried-and-true methods. With a modern PMO, we can rock the startup scene with style and pizzazz!

Risk Management: Dodging Curveballs with Finesse

Ah, the unpredictable rollercoaster of startup life! But fear not, for our PMO is our trusty sidekick when it comes to risk management. It’s like having a crystal ball that spots potential threats before they hit us like a rogue asteroid. With its superpowers of risk identification, assessment, and mitigation, the PMO helps us dance gracefully through the storms of uncertainty. It’s our secret weapon to adapt, pivot, and turn obstacles into opportunities.

Conclusion

In the wild and wonderful world of startups, having a Project Management Office (PMO) is like having a superhero team at our side. It supercharges our success, ensures project governance with a side of coolness, optimizes resource allocation, embraces modern quirkiness, and gives us the power to dodge risks with finesse.

So, whether you’re a scrappy startup or a thriving enterprise, Agile Creative Labs is your go-to project management consultancy for the modern age. With their finger on the pulse of innovation, they bring a fresh perspective to the table, blending the best of waterfall, agile, and hybrid methodologies. From agile rebels to waterfall warriors, they understand that no two projects are the same. With their expertise, they’ll craft a tailored approach that suits your unique needs, empowering you to tackle any challenge with confidence. Say goodbye to outdated project management practices and hello to a dynamic, adaptable future. Reach out to Agile Creative Labs today and unleash the full potential of your projects. The world of modern project management awaits, and they’ll be your guiding light on this exciting journey.

Originally published at http://agilecreativelabs.ca on May 28, 2023.

Waterfall, Agile, or Hybrid? This is the first question that every sponsor should answer before running projects in full steam. Oftentimes, organizations would stick to the default mode of running their initiatives.

An enterprise would typically stick to a predictive or waterfall approach, or a startup would often choose an agile or an adaptive approach. Both methodologies have their own pros and cons, so the question now is, is there a way to bring them together to leverage the best of both worlds?

What is Hybrid Project Management?

Hybrid project management is a methodology that combines the structure of a waterfall and the adaptability of agile implementations. Typically, organizations would choose a hybrid approach when they run complex projects that have a lot of unknowns such as innovations, process re-engineering, and business transformations. This would allow them to maintain the high governance and compliance requirements that are mandatory within their organizations.

Waterfall vs Agile vs Hybrid

So, how would you know which methodology to take? We prepared this simple table to guide you.

Use this table as a quick reference to decide which project management methodology would fit your requirements. Note that this is not an exhaustive list, and there are other factors to consider before deciding which approach to take. In fact, the most recent version of the Project Management Institute (PMI) body of knowledge, recommends a process called Tailoring. In this activity, the right approach is determined based on project objectives, needs, and constraints.

Jumpstarting your Hybrid Project Management journey

If you are looking to jumpstart your hybrid project management journey, it is best to leverage existing industry standards and best practices to reduce the risk of error and loss, such as hybrid project management turn-key systems and templates, such as this Hybrid Project management system in Notion.

Beyond the technicalities, having the right stakeholder mindset and most importantly, support is absolutely necessary to ensure a successful implementation.

Change is inherent in complex and innovative projects, and nobody should expect that everything will stay the same during the course of execution. The focus on business value delivered through agile governance, and having stakeholders with an adaptive mindset, are key to a successful hybrid project management implementation.

Originally published at https://agilecreativelabs.ca on August 15, 2022.

Being in the full time program, I wanted to document my journey, taking the perspective of an Agile project manager, and see how creative and innovative practices can be applied to the field to bridge a lot of gaps, with the hopes of building awesome products that users will love.

Team building on steroids

The first day of MDEI bootcamp was all about breaking the ice and trying to learn something about everyone (there’s more than 40 of us!). We had lots of activities that definitely sped-up the process of learning more about each other: we created our introduction video, our personal collage in Miro, took a Myers Briggs personality test and had our first remote game — Skribbl!

What really struck me during the first day is the breadth of web-based games out there that can be used for remote team buildings (and Sprint Retrospectives). Prof. Lennart Nacke gave us all the good stuff : Skribbl, Krunker and Codenames Online Game. I am so amazed by the talent and expertise of the Stratford School of Interaction Design and Business to make our remote learning a unique and rich learning experience

We were Multiverse Agents

The second day of the MDEI bootcamp focused on how to tell a good story through a game. We played Interactive Storytelling with Whatever You’ve Got by Prof. Daniel Harley.

This game definitely blew my mind — it is so simple yet so rich. This remote game makes the players use any object that is available in the house and use it as a game artifact. The game focuses on telling a story behind each object through gestures, sounds and a memory, stimulating the major senses — sight, sound and touch.

While we were playing this game, I keep on thinking how can I apply this to our remote teams. This can be a good way to spark creativity, bond a new team and take off the stress after a long day at work.

Innovation and Scrum

An area that is very close to me, we learned about applied innovation from Dr. Tobias Thielen and Scrum from Prof. Tabatha Dominguez on the third day of the MDEI bootcamp. Having worked on a lot innovation projects for so many years, I can relate to the challenges that big companies face whenever they are mandated to innovate.

A lot of times, big companies lose sight of the bigger picture because they are too focused on a product or service that has already reached its full maturity. They keep on adding features that oftentimes no longer add value and just puts more confusion on how to use the application (just like SharePoint, lol). Using applied innovation can help organizations focus on widening their portfolio and play the role of a market disruptor.

Usability & User Experience

In the world of finance and IT, Usability and User Experience are things that often come last when we design a new product or enhance an existing one. The lectures and readings shared by Prof Leah Zhang-Kennedy on the fourth day of the MDEI bootcamp are very informative and methodological that I found myself immediately applying it in my current project at work.

We are building an internal corporate website, that will allow our department to communicate and collaborate better with our customers. Applying the usability scenarios and tasks allowed me to give more depth on the UX part of our website and I also included some test cases related to that that will be part of our acceptance criteria.

Video Editing

I am so excited for the last day of the MDEI bootcamp. For years I desperately try to do my own videos from all the travels, road trips, events and gatherings — and they are all horrible. In just a day, I got very valuable tips, insights and guidance to start working on my video editing skills. I look forward to using Adobe Premiere in the next couple of days and see what I can do with some of my videos from last year’s travel. The tutorials of Peter McKinnon are the best.

So that concludes my five-day MDEI bootcamp. Its a Friday, and Monday’s a holiday, what an awesome and very productive week. For now, I’ll finish my tea and look forward to a restful long weekend.

Balloons bringing internet connectivity to the world, self-driving cars, high-altitude wind turbines that harvest energy — these are just a few of X’s (formerly Google X) moonshot projects. Since 2010, the moonshot approach as X’s business innovation strategy has been thinking of the craziest and most innovative ideas that could not just bring improvement to the status quo, but could deliver up to 10x impact. How do they do this? Through following the moonshot blueprint.

What are moonshots?

Moonshots are exploratory and ground-breaking projects that follow a three-point blueprint. Inspired by the Apollo 11 spaceflight project which brings man on the moon in 1969, moonshot was lifted from JFK’s speech at Rice University when he talked about a dream of putting a person on the moon by the end of the decade.

* Find a common problem that affects millions;

* Find a radical solution that is ten times more innovative than the status quo; and

* Prove that the technology is feasible.

Find a common problem

The moonshot blueprint starts by finding a common problem that affects millions of people. This business innovation strategy works by mandating employees to think creatively and imaginatively all the time. X’s CIO Astro Teller says that being “responsibly irresponsible” is the secret. Employee’s radical thinking must eventually be supported by facts derived from cheap experiments that would validate or trash the idea. At the event that the concept fails, people don’t get punished for it- instead, they are rewarded from killing their plans early on.

As Astro Teller says: The only way to get people to work on big, risky things-audacious ideas-and have them run at all the hardest parts of the problem first, is if you make the path of least resistance for them.

Find a radical solution

The second business innovation strategy is to find a revolutionary solution that is ten times (10x) more innovative. This corporate innovation strategy requires X to not only think of radical solutions but also prove in a fast and cost-efficient manner that their hypothesis is sound.

The company does this through what they call as Rapid Evaluation. This corporate innovation strategy follows two major stages: first is to understand the biggest risks of an idea to know if building prototypes to test it is financially feasible. Second, is to build prototypes and run it in the hardest and riskiest parts of the technology to understand if the problem that they are trying to solve is feasible. Here the prototype must undergo a techno-economic analysis that proves it could survive the market and its real-world application.

One good example of this is X’s lighter than air cargo ship that can transport massive infrastructures without railways. However, since designing and testing their prototype would require close to 200 million USD, X decided to kill it. It is essential to note here that X’s business innovation strategy follows a “fail fast, fail early” mantra. This means that it is better for companies to fail and lose a few thousands of dollars than to lose millions in the end just to know that their idea is not feasible.

Prove technological feasibility

To prove that the technology needed to build such radical solution is possible, X requires that ideas undergo a lot of experiments and rapid prototyping. X does this through the corporate innovation strategy called X Foundry.

Developed in 2015, X Foundry revolves around the premise that an idea that is just a few months old carries a lot of risks — including those that the company doesn’t understand or even know yet. X Foundry as a corporate innovation strategy provides a special environment where small teams focus on testing the riskiest elements of the project to see if it can be eventually turned into a product and business.

X does this in their Loon project. Project Loon is X’s vision of a network of a balloon-powered internet in the stratosphere that can beam an internet connection to rural and remote areas. Project loon envisions up to four billion people benefitting from this project. To make this business innovation strategy happen, they did several prototypes.

The first one involves a balloon trying to get a Wi-Fi connection from a ground-based antenna. The second prototype then focuses on getting the balloon talk directly to handsets. The third prototype eventually aimed to get the bandwidth high enough to get a real internet connection of up to 15 megabits per second. X has done a couple more prototypes until it reached a milestone of having their balloon go around the world 19 times over 187 days.

How can we apply the moonshot blueprint?

Of course, not every one of us has a business like Google. However, we can apply the corporate innovation strategy of X’s moonshot thinking to innovate products, services, and internal branding strategy.

The fail fast, fail early business innovation strategy of X is a good way to validate the feasibility of new products and services before launching to the market. As a form of validated learning, creating prototypes and conducting experiments in controlled environments is a good way to test the likelihood of market adoption.

To improve internal branding, strengthen values such as creativity and innovation. On-board only what X calls as T-Shaped people — individuals who have deep expertise and intellectual capability who can collaborate easily across various domains.

Moonshot thinking pushes people to think ten times bigger than their current ideas. By following this mindset and creating a process inspired by its blueprint, companies can shape a strong internal branding message, focusing on innovation.

Originally published at https://www.linkedin.com.

High uncertainty, lack of in-house capability and the quest for a highly accurate model. These are just three of the main challenges in implementing a machine learning project.

I remember my boss asking me, “So… how are we supposed to derive insights from this unstructured data set?”. The rules of a typical business intelligence solution no longer apply. The high uncertainty of machine learning projects is harder to manage due to lack of in-house capabilities.

To tell frankly, no one in our organization knows the implementation of artificial intelligence tech beyond theories. Nobody implemented a decent solution good for production, not even a pilot that business users can play around. To further complicate things, there is always this lingering question: how are we sure that the machine learning model is “accurate enough”? How do we know if we’re finally ready for production?

Agile project management has a framework to handle highly uncertain projects such as machine learning. Scrum is a framework that’s fit for projects with many unknowns. As Gunther Verheyen said:

Agility is the state of high responsiveness, speed, and adaptiveness while controlling risks. It serves to better deal with the unpredictability so typical of the work of software development and to the markets that organizations operate within.

Agile development with Scrum is the light at the end of the tunnel. The ether to which our machine learning tech will operate. So, how are we doing it? Here’s what we have figured out so far:

1. Start with small to zero investment

License costs for an AI solution are no joke. For instance, an enterprise natural language processing solution hosted in the cloud can cost as much as $200K. It makes more sense then to try a proof of value (PoV) with small to zero investment to test the waters.

There are quite a number of open source and free enterprise products in the market today. More AI companies are starting the move to democratize its technology like WorkFusion. For instance, in 2017, the company turned one of its flagship products, WorkFusion RPA Express free to use. RPA Express is now a free automation software that is easy to install, use and scale. It is suited for companies that don’t have any in-house machine learning capabilities. Its free to download here.

2. Shift to agile driven contracting

When you’re done with the PoV and finally demonstrated that your chosen AI will cater to your business needs, its time to talk to procurement. Traditional time and material nor fixed priced contracts will not work.

Agile driven contracting is an agile project management process that emphasises on the delivery of value, not just the solution. The idea is, you pay only when the value is delivered. Wikipedia perfectly sums it up:

In Agile contracts, the supplier and the customer together define their common assumptions in terms of the business value, implementation risks, expenses (effort) and costs. On the basis of these assumptions, an indicative fixed price scope is agreed upon which is not yet contractually binding.

3. Design thinking for requirements gathering

The Stanford Design School (d.school), now known as the Hasso Plattner Institute of Design started the Design Thinking process with just three steps: Understand, Improve and Apply. This framework emanates the values of agile project management as it emphasises the importance of empathy in doing good project design. This means that rather than zooming into the functionality of a machine learning solution right away, it is best to start with knowing the current customer pain-point or challenge. This will drive which features to build and what the user experience should be like.

4. Scrum for project implementation

At the heart of our implementation is Scrum. Contrasting it to the traditional and predictive project management models, Scrum is effective in handling big unknowns, risks and changes typical of machine learning projects. Its streamlined process only includes four major events: Sprint Planning, Development, Sprint Review and Sprint Retrospective. It is perfect for cutting the bureaucracy and skipping right to building something which our business users can use asap. Our sprint runs in two-week increments with a build ready to use at the end of the 10th day.

5. Build the machine learning model with business

Agile and machine learning is a match made in heaven. By using scrum, we emphasise the importance the criticality of business participation to project success. Instead of being heavily reliant on the technical team, we asked subject matter experts from operations to build the machine learning model with the geeks. Together, the vision that we defined in the design thinking sessions is translated into machine and data science problems that the model should be able to reflect.

On my next article, I will talk about the other five things that we did to use agile to manage our first machine learning project. This zooms in right to development and how to effectively do a validated learning approach to make sure the increment is still on the right track.

Remember, doing a machine learning project will never work using predictive and traditional approach. Also, if you decide to use agile for implementation, but your contracting model is still fixed priced or straight time and material, chances are this will be a primary cause of the delay. This is most especially true if you have to change requirements midway, which is inevitable for machine learning projects.

Originally published at www.business2community.com on May 22, 2018.

This video of CEO Sundar Pichai at I/O 2018 demonstrated a perfectly human-like phone call of a personal AI.

This AI virtual assistant, made an independent call to a salon, using an incredibly natural woman’s voice. Sounding unbelievably normal, the person on the other end have no idea that they are conversing with a personal assistant robot. Google Assistant even responded with a very spontaneous “mmhmm…” together with other nuances such as word omissions and verbose sentences, that are typical of human conversations.

Achievements such as this in the field of natural language processing (NLP) is quite a feat. In fact, personal AIs such as Google Assistant is said to have passed the Turing test. Developed in the 1950s by the scientist, Alan Mathison Turing, the Turing test seeks to identify a machine’s capability to show intelligent behavior that is indistinguishable from that of a human.

Google scientists behind Duplex explained this level of achievement from Google. Leviathan, et al. in their blog said:

There are several challenges in conducting natural conversations: natural language is hard to understand, natural behavior is tricky to model, latency expectations require fast processing, and generating natural sounding speech, with the appropriate intonations, is difficult.

It is no longer surprising that the hypothetical scenario of AI takeover is becoming more real. However, instead of jumping to a conclusion of a robopocalypse — Skynet taking over and robots dominating humans, maybe it’s good to know first how AI virtual assistants will change our lives, for the better.

Know anything

Google Assistant have more than two dozen features that go beyond scheduling appointments. This AI virtual assistant can be your quick go-to to know what’s the latest on Netflix, know where to eat, drink, dance or shop and even suggest top vacation spots.

Manage activities

Being one of the pioneers of voice-based virtual assistant, Siri is a productivity champ. For instance, it can book movie tickets, play music and even set important reminders. Siri can also create a shopping list and even produce a travel plan down to the smallest detail. Using this personal AI can bring tremendous efficiency most especially if you are a fan of multi-tasking.

Helps meet and keep commitments

Part Siri, Part Google Assistant, Microsoft Cortana will help you meet commitments. It has a robust reminding setting capability that are time, people and location-based. This means that this personal AI can remind you to drop by your boss’ office every Monday the moment you parked your car. Or take a 30-minute jog every 6 pm. Cortana can also search your OneDrive storage using a natural-language-based query.

Hands-free command

Amazon Echo also known as Alexa has an amazing handsfree calling and texting capability. It can even place free calls to any landline or mobile number in Canada, Mexico, and the US. When paired with security cameras, this AI virtual assistant can even receive live feeds in the comfort of any room. Another interesting feature is its support for smart home devices. Echo can quickly turn your lights on/off (or even dim them), manage heaters, fans, coffee makers and thermostats.

Improve customer service

Moving to the corporate setting, Pepper of Softbank Robotics is a humanoid personal assistant robot that can analyze expressions and voice tones. Perfect for receptions at several office spaces, it can interpret main human emotions and even understand body language such as head angle. Pepper can make a natural interaction through perceiving and analyzing feelings thanks to its 3D and two HD cameras.

Automate Tasks

Personal AI assistants combined with robotic process automation (RPA) can create a smart digital workforce that can lead to end-to-end process automation. For example, a personal AI fused with RPA can create agent assist robots that can automate various applications, based on learnings on past customer interactions. It can also develop copy-paste robots that execute rule-based processes which involve copying data from one system to another.

WorfkFusion’s RPA Express is a free automation software that enables companies to deploy as much personal AI as they need. Perfect for companies which don’t have artificial intelligence capability, RPA Express can easily automate tasks by just pressing a button. It is a no-code and object-based recoding that is very straightforward to use. In fact, WorkFusion offers a free training to teach installation and usage of RPA Express, together with a how-to guide to transform manual business process to automation.

Start with free resources! There are lots of them

Admittedly, having a personal assistant robot is not an easy investment to make. In fact, comparing corporate solutions in the market, AI tech ranks top in terms of capital expenditures.

As the drive to democratize AI is taking its much-needed push, several open-source frameworks and free AI products are gradually dominating the market. WorkFusion RPA Express, for example, is one of the free RPA products that companies can download and start exploring with.

Hey Athena is another open-source framework that will allow you to build your own personal AI assistant. The company even allows developers to save their code to their cloud servers and run it for free. Registration is also free.

Think big, start small and scale fast. This must be the mindset of anyone venturing in AI virtual assistants, most especially if applied for the enterprise.

Originally published at www.business2community.com on May 17, 2018.

Quora users offer a lot of theories. One involves taunting Thanos to use the stones one at a time. While another one suggests, Doctor Strange should offer Thanos a bargain, just like what he did to Dormammu.

I think Doctor Strange hasn’t maximized the power of the Eye of Agamotto. I kept on asking, why hasn’t he tried to see the possibilities the first time Hulk crashed landed his staircase? They could have defeated him earlier since Thanos only has two stones at that time (i.e. Space and Power).

Come to think of it, the Time Stone’s power to predict the future is just like artificial intelligence.

If the Avengers would use AI frameworks and AI infrastructure to aid in their quest to beat Thanos, what are techs that they should use?

Here’s a comprehensive list. The Avenger’s guide to artificial intelligence.

AI Frameworks

Apache PredictionIO is an open source AI framework to build, evaluate and deploy engines with machine learning algorithms.

It has a noble vision: “to democratize and bring machine learning to the masses.” It has two major components, the PredictionIO framework, and the Event Server. The framework uses Apache spark to process machine learning algorithms. While the event server unifies events from various platforms using Apache HBase or any JDBC backends as its data store.

Accord.NET has C# at its backbone.

It is a .NET framework with audio and image processing libraries that enable enterprise-grade computer vision, audition, signal processing, and statistics. It can cater to various types of classification, regression, clustering machine learning algorithms. And, a couple of statistical algorithms such as distributions, hypothesis tests, and Kernel Methods.

The sample application shows how to use Gaussian Mixture Models. It is meant to perform clustering and classification using soft-decision margins. Download the application and source code here.

BigML is one of the leading startups in data science.

It has a web interface that guides from data upload down to model creation- both predictive and descriptive. Its command line interface utilizes Python API allowing for greater flexibility. For instance, it enables model prediction both locally and remotely. And perform cross-validation tasks to estimate model accuracy. Its RESTful API can also be used directly via curl commands or wrapper.

WorkFusion is one of the leading enterprise-grade robotic process automation (RPA) platforms with a strong machine learning backbone.

Its flagship product, called the RPA Express is a free solution that can automate a variety of complex knowledge work, particularly in banks, insurance, retail, and e-commerce.

As a free product, WorkFusion RPA Express is set to revolutionize the RPA industry. Alex Lyashok, WorkFusion COO said:

Making RPA free helps shared services and lines of business within companies support immediate transformation goals and get quick wins by automating enterprise applications. RPA Express lets customers freely automate the manual work of integrating Citrix, Oracle, SAP and other vital core systems by eliminating the “swivel chair” work of entering credentials, navigating application UIs, and performing core systems functions.

Fit for a global market, WorkFusion RPA Express can digitize complex business processes such as BPM, RPA, workforce orchestration and even machine learning-powered cognitive automation.

Download the free RPA Express here.

Caffe is another open source AI framework developed at the University of California, Berkeley.

Stemming mostly from the academic domain, it has a wide global network of contributors. Caffe specializes in deep learning feature extraction, classification and the scientific use of neural networks (NNS).

AI Infrastructure

Google Tensor Processing Unit (TPU) is an AI infrastructure made of machine-learning supercomputers or Cloud TPU pods that are fused through high-speed data connections.

It is the supercomputer behind Google Search, Street View, Google Photos and Google Translate.

The TPU includes the following computational resources:

• Matrix Multiplier Unit (MXU): 65,536 8-bit multiply-and-add units for matrix operations
• Unified Buffer (UB): 24MB of SRAM that works as registers
• Activation Unit (AU): Hardwired activation functions

BrainChip uses a technology called spiking neural network; the company claims its tech learns like a human brain.

BrainChip tech can provide a high performance yet low overhead neuromorphic computing approach that is very different from other deep neural networks. Its machines can learn instantaneously based on experience, just like a human.

Cerebras is a hardware startup that focuses on the next-generation chip for deep-learning applications.

It is a stealth-mode startup that is backed by premier venture capitalists and industry leading technologists. Its goal ultimately is to create a next-generation GPU that is used to apply machine learning on a colossal scale.

There are a lot of open-source and free AI frameworks. Infrastructures are also starting to advance, building supercomputers that are fit for ma achine and deep learning. Through AI, labor costs can be drastically reduced; while generating new and unexpected insights, discover new patterns, and create predictive models from raw data.

Beyond the Avengers

Beyond the Avengers, a Gartner study found three most often-cited AI applications. They are all related to customer interactions.

One in three organizations will use AI to customer engagement applications;

Three out of 10 use AI to call center service and support; and

One in four will use AI into digital marketing.

Beyond all the AI frameworks and AI infrastructure, it is essential to bear in mind that an AI-driven customer engagement scenario requires taking a humanistic design approach. Doing this will enable leaders to maximize value from their diverse, enormous and complex data sets.

Originally published at www.techwebspace.com on May 8, 2018.

You are not a data scientist nor machine learning programmer. But your boss wants an AI project started in the next three months.

Mastering deep learning in a next few weeks is impossible. Nor, you have the budget to hire a consultant to help.

What do you do?

Think.

Surely there is a hack to all of this.

If you find yourself taking a project management role to implement a deep learning initiative in your organization, but have zero knowledge, it’s time to tap on your grit. Let us explore the ways on how you (a guy with zero ML skills) can make it work.

Understand the concept of Neural Nets

First, understand the concept of Neural Nets. Just like the human brain, deep learning is composed of neurons which receive signals from inputs and connect it to an answer.

There are two ways in which neurons process inputs. The first one focuses on linear combinations of weighted inputs (i.e., linear neuron). While the second one uses weighted sum to feed into the logistic function (i.e., sigmoidal neuron).

The logistic function then returns a value between 0 and one which in turn makes deep learning a more straightforward process. A neural network happens when these neurons link to each other to answer the learning problem.

Know how to train Neural Nets

Training neural networks can be viewed as a sort of optimization problem. The goal is to minimize the error of neural nets through constant training. Use training data to form weights for the neural networks that will produce the desired output.

Typical training of neural networks with TensorFlow uses a batch data. The primary goal is to train the machine to get intended results. Weight assignments define these results.

The batch data will be fed to the network and from there, determine how accurate the neural network is through looking at its percentage of error in relation to initially set weights. The goal is to minimize the error through continually training the neural nets to arrive at the pre-set weights.

There are a couple of ways to do this. The first one is through gradient descent. This uses derivatives and calculus techniques through back propagation, resilient propagation and Manhattan propagation and other optimization techniques based on gradient calculations.

Grasp Mathematical Foundations of Data Science and Machine Learning

Algebra and calculus is the foundation of the deep neural nets. Self-studying deep learning is no longer new. There are a lot of free courses online from top universities such as Stanford and Harvard that help professionals transition.

Andrew Ng’s linear algebra review videos and reference guide is an excellent place to start.

Professor Leonard’s Introduction to the Derivative of a Function also gives a succinct understanding of backpropagation.

Finally, jump right into deep learning through this Quoc Le’s (a Google research scientist) Deep Learning Tutorial.

Know about Deep Learning Software Tools and Platforms

The field of deep learning is growing fast. KD Nuggets compiled a comprehensive list of 50 deep learning software tools and platforms. These platforms can be used in natural language processing, speech recognition, image recognition and the likes.

Keep Deep Learning Cheat Sheets

Keeping quick reference guides is an excellent way to remember things you just learned.

Also a linear algebra cheat sheet for deep learning

Jason Brownlee compiled this comprehensive linear algebra cheat sheet for machine/deep learning. In here, he talks about arrays, vectors, matrices, matrix operations and factorization and statistics.

Take free deep learning courses

There are a lot of good free courses on machine and deep learning via Coursera, Udacity, and EdX.

For instance, Andrew Ng’s Introduction to Machine Learning is always the topmost recommendation among practitioners.

Deep Learning Specialization is also available Coursera. It has both a free and paid option focusing on major areas of deep learning.

Some AI companies also offer free webinars on deep learning in the enterprise.

WorkFusion provides a webinar called Forecasting the Future of Enterprise AI to teach how companies can solve common roadblocks and issues.

Taught by its co-founder and CTO, it maps our advancements in AI and how to benefit from robotic process automation (RPA) that runs under deep learning.

WorkFusion also offers a free version of their product called RPA Express.

RPA Express is free and easy to use. It enables automation of common applications even without coding. It is perfect for companies who wanted to start on RPA without having to tie themselves to multi-million dollar contracts.

Use deep learning in your personal life

Applying deep learning on problems that are close to you is the best way to learn it. To keep yourself motivated on your self-study, Jason Brownlee recommends working on problems that have an impact.

For example, using machine learning at home for personal finance, tracking your food intake and even how much TV or Netflix you are consuming in a week. He broke down machine learning into a 5-step tutorial process that you can take for free on his website.

When not to use deep learning

The decision to use deep learning should be more than the hype. There are instances where a simple statistical and rule-based model is enough.

For example, establishing causal mechanisms such as knowing if two molecules work in a cellular environment can benefit more from old-style Bayesian methods.

Using deep learning on unstructured data such as natural language processing might not work because of the lack of context to which word embedding can anchor on.

Diving head-on into deep learning is no easy feat. Before you start self-studying your way into this, warm up with this brief history.

Originally published at www.techwebspace.com on May 2, 2018.

The public sector and International Financial Institutions (IFI) seek to be knowledge and learning organizations. Yet, just like corporations and enterprises, they struggle.

Implementing the right technology to maximize the value of thousands of research, lessons learned and experience amassed for the past few decades is still a huge gap unfulfilled.

For example, in 2017 alone, the IFI, Asian Development Bank (ADB), released on its 2017 Annual Evaluation Review that 60 percent of ADB staff surveyed said finding recommendations from completed projects are difficult.

Respondents call for a knowledge database that has a better capability for meta-analysis, lesson synthesizing and search.

The need to innovate

Yet, the call for innovation in both IFIs and public sector is double-bind. Jon Simonsson, Head of Innovation, Research, and Capital at Sweden’s Ministry for Enterprise and Innovation said:

The political incentives to risk public money are non-existent — it’s too easy to see the short-term political consequences of initiatives gone wrong and debate whether taxpayers’ money is going down the drain. Public money is to be spent according to rules and regulations.

The double-bind problem of IFIs and the public sector is a paradox. They are risk-averse, yet they need to innovate. In a macro perspective, these institutions do not differ from their corporate counterparts.

The world of VUCA

Just like enterprises, IFIs and the public sector navigate in a world of volatility, uncertainty, complexity, and ambiguity, otherwise known as VUCA.

The world of VUCA causes an efficiency gap that only innovation can solve. To be innovative means to take more risk.

A fail fast, fail early approach is the only way to bring a culture of innovation and technological advancement to financial institutions and the government.

Business Intelligence is dead

The days of the good old business intelligence platform is dead.

With the growing disparate data sources and fragmented knowledge management platforms, there is a need for IFIs to abandon the old ways of working.

Big data and artificial intelligence should be the backbone of knowledge and data systems, to transform institutions to a data-driven enterprise, maximizing the value it has amassed throughout the years.

Artificial Intelligence for IFIs and the Public Sector

The AI landscape is huge. A careful selection of a technology fit for purpose is key to a successful pilot AI.

Where should IFIs and the public-sector start? Here’s the topmost three:

Robotic Process Automation (RPA)

Digitizing core business or government operations is no easy feat. Aside from technical ambiguity, siloed implementation and unclear expectations further add to the toll.

How does it work

Robotic Process Automation (RPA) are software robots that can run transactional and rule-based tasks through copying user behavior.

The technology can automate high-volume and transactional processes. RPA products such as WorkFusion Smart Process Automation (SPA) is an enterprise software that allows integration of core systems and transaction automation.

Application

When applied in IFIs and the public sector, it can improve efficiency by automating repetitive work through AI.

This, in turn, allows organizations to focus on tactical priorities WorkFusion (SPA) can end recurring pain-points.

For example, frequent policy changes that need business process integration can be taken-cared of by WorkFusion SPA instead of a human. In effect, policy execution becomes faster, consistent, predictable and less prone to error.

Text Mining and Natural Language Processing (NLP)

Text mining is the process of extracting valuable information and knowledge from unstructured data.

Financial institutions and governments have a tremendous wealth of knowledge.

Buried in project documents, research and content. These are all unstructured text that a typical data mining process cannot handle.

How does it work

Unlike data mining which is typical of business intelligence applications, text mining can deal with both unstructured and semi-structured data.

Text documents, HTML tables, and emails are just a few examples.

Text mining allows for statistical and machine learning algorithm interpretations. This, in turn, could analyze thousands of unstructured word clusters and derive relevant summaries that could drive the success of future projects.

Application

Text mining, when combined with NLP, can help IFIs and the public sector make sense of thousands, even millions of unstructured data.

Emails, presentations, memos, and speeches archived for the past few years, even decades can be ingested to derive knowledge and insight that are key data-driven decision making.

Deep Learning

Closely related to human brain development is deep learning. Unlike task-specific algorithms like RPA, deep learning models work like a biological nervous system.

How does it work

In deep learning, computers are taught to learn by example. It is the tech behind self-driving cars, voice recognition, image recognition and natural language processing.

By utilizing neural network architectures patterned from the brain, deep learning eliminates the need for human training.

For example, deep neural networks such as convolutional neural networks (CNN) can process images on its own.

Unlike machine learning, it doesn’t need manual feature extraction to classify images.

Application

The model learns as it collects images, eliminating the need human-aided pre-training. IFIs and the public sector can use deep learning to analyze its financial statements, annual reports, and project evaluation reports.

Any document that typically takes three or more days to review can be analyzed in just a matter of seconds.

Deep learning models can do this by mimicking the thought process of subject matter experts, consultants and analysts.

Take action

IFIs and the public sector seek to be a knowledge and learning organization. Yet, the risk-averse and conservative nature of these institutions causes slow technology adoption.

However, the world of volatility, uncertainty, complexity, and uncertainty doesn’t tolerate inaction.

A fail-fast fail early approach is the middle ground between innovation and small risk appetite. IFIs and the public sector should start small and scale up. Not in a five or seven-year horizon, but today.

Originally published at www.techwebspace.com on April 19, 2018.