Archive for April, 2018

Four Principles For Using AI

April 30th, 2018

Pick a basic problem — it is the single biggest determinant of success. The first version of Vymo only solved a basic need. Salespeople around the world found it tedious to report sales data. Without sales data, managers and leaders couldn’t forecast accurately or help their teams achieve targets, which impacted their topline directly. So, we built a mobile-first solution to detect all sales activities and then, based on sales data, our solution did what a manager would do. This is quite contrary to the general perception of artificial intelligence (AI) solving — or exacerbating, depending on whose views you subscribe to on the matter — all of the world’s most complex problems. Basic problems exist all around us, regardless of industry. To paraphrase Anand Sanwal’s advice on running a successful newsletter, it’s best if you can find the things people in your industry say but nobody actually ever says out loud. That’s not to say a basic problem is an easy problem. In fact, it’s often quite the opposite.

AI Solves Basic Problems Efficiently

The major edge data-driven software has over software that just implements standard business logic is that it is more contextual and evolves progressively over time. For example, Vymo’s intelligent suggestions have different intervention thresholds for different types of salespeople and it varies over a period of time. The cumulative impact of this is real and tangible. Its impact is even greater if you can work with businesses to pick out the most useful data sources, find out where the bodies are buried in the data and construct good features to feed into your algorithm. The other advantage of solving a basic problem is that it is generally prevalent (and present in usable formats).

Build Based On Observations

The suggestions our AI gave also evolved. Our first version of suggestions was based on the premise that more sales activities led to more revenue. In simple terms — more calls, meetings and other such interactions with prospects and customers increased your probability of meeting your sales quotas. It seemed like a perfectly rational thing to assume. In reality, though, only 30% of the best reps were in the top quartile with respect to meetings — they just averaged higher conversions. This led us down a path toward understanding what activities had a higher return on investment (ROI). As an example, lunch meetings offered disproportionately high ROI for sales reps in wealth banking. We also looked at what leads, prospects or customers had to be prioritized for engagement (spoiler alert — it’s not just the leads that are funnelled by marketing).

The first major pivot was based on an important lesson — build based on observations and not how helpful you think your application can be. Of course, you start with a basic premise, but once you have enough data to prove or disprove your model, your algorithms should run based solely on field data from end users. We prioritize builds based on what user data is telling us rather than cool new machine learning (ML) or AI capabilities we are excited about. Yes, we do try to stay cutting edge, but that never comes at the cost of being relevant to the user.

Tie Your Application To An End Goal

This is all a user really cares about — how does your application tie to my end goal? We still use notepads in the digital age because they still serve a purpose. The same is reflected in an app’s usability, too. One of our most popular new features is “nearby,” which shows the sales rep prospects and customers that are around his location. Compared to some of our other, more complex builds, this requires us to build a layer of intelligence on Google application programming interface (API) and then make it functional across devices and modes, which, while non-trivial, is definitely simpler than figuring out how prospects ought to be prioritized. So, forget your fancy models and algorithms — what is the value that you are adding to the end user? A sobering test for this is the usability statistics of the app, which reflects, in reality, the most useful aspects of the app.

It Is Better To Be Vaguely Right Than Precisely Wrong

This brings me to the final point — it is better to be vaguely right than precisely wrong. AI presents a tremendous opportunity to analyze and learn from large data sets. Often, the payoffs are disproportionately large relative to the costs (which are self-correcting, anyway). For instance, maybe the first few suggestions your algorithm makes are way off the mark, but if your experimental setup is right and your data is sufficiently large, it is bound to get progressively better. At Vymo, we run into corner cases, but if we didn’t expose our models to those data points, then who knows what we could be missing? If you had all of the power in the universe, would you prioritize doing great things or not doing bad things?

So, go forth and conquer!


Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Venkat Malladi.

11 Project Management Tips To Keep Your Employees On Track

April 23rd, 2018

Good project management is essential for any team tackling large, multi-faceted initiatives. This is especially true in the tech industry, where individual tasks are often highly specialized, complex and time sensitive. Without a solid process in place, it’s easy to lose track of progress and issues that may arise.

To find out how leaders can keep their teams’ tasks organized and transparent, we asked a panel of Forbes Technology Council members for their top project management tips. Their best answers are below.

  1. Create Squad-Based Teams

What helps our teams manage tasks is breaking down team silos with squad-based development to reduce miscommunication among team members. Squads are small, flexible teams that are responsible for the end-to-end delivery of each product. Each member is involved in sprint planning so that every person is allocated specific tasks that cumulatively match the capacity of the squad. – Sanjay Malhotra, Clearbridge Mobile

  1. Directly Align Tasks and Performance with Company Goals

Make sure everyone is aligned with the organization’s critical objectives with specific, individual performance goals paired with measurable outcomes. Tie the individual contributors’ performance directly to the organization’s goals with frequent progress reviews. This will naturally motivate your team to keep its eye on the ball and to avoid tasks that do not contribute to overall success. – Kevin Vliet, Target Corporation

  1. Use AI to Tackle Menial Tasks

The champions of the IT department have been struggling through some less-than-ideal work environments. It’s long been the fate of IT pros to handle mundane tasks that “keep the lights on.” Yet with the dawn of AI ops, noise reduction and alert clustering can be automated, granting IT pros the time and opportunity to focus on initiatives that drive the business forward. – Chad Steelberg, Veritone

  1. Follow Kanban Principles

While this system originated in the dev world, we at HyperGrid extensively use Trello and Kanban principles for organizing across the company. This has proved very useful as a way to quickly inspect and see how backlog is growing, who is overloaded and help with the reprioritization of tasks. – Manoj Nair, HyperGrid

  1. Make Sure Employees are Working on the Right Tasks

Unless employees are busy doing the right tasks, the business will suffer. Let intelligent data drive the framework, determining what each employee should focus on to achieve specific tasks. – Manish Sood, Reltio

  1. Train Everyone on Time Management

One-on-one time management training will help employees get the most out of their personal work times, which will help smoothen workflow among teams. Much emphasis, especially for tech employees, should be placed on blocking extended periods of time to focus on one sole task without distractions and breaking up longer projects into smaller, easily digestible parts. – Scott Stiner, UM Technologies

  1. Set Clear Expectations and Checkpoints

Strategic over-communication is key. Before teams start on a project, I like to make sure that everyone has a clear idea of deliverables, timelines and a measure of what success looks like. We also develop checkpoints along the way with our teams to figure out what is working and what is holding up progress. It’s vital to strike a balance between project management and employee independence. – Gregor Carrigan, Course Hero

  1. Encourage Communication, Feedback and Collaboration

Schedule daily stand-ups, weekly syncs and monthly alignment meetings. Provide ongoing feedback whether positive or negative. Don’t wait for periodic performance reviews. Everyone should know where they stand and where to improve. Foster an engineering team culture of close collaboration to solve siloing and project delays and to keep an overall tight adherence to a roadmap. – Bojan Simic, HYPR Corp.

  1. Choose the Most Important Thing For Employees To Focus On

Daily stand-ups are great to help share info but also for making sure that people rank the most important thing. If they have too many tasks, then they can’t be working on them all equally. The length of the list is just as useful as the top item on the list. Things in the middle of a long list are not likely to move forward and give a false illusion of progress because of constant status updates. – Joshua Greenough, InfoScout

  1. Prioritize Based on Urgency and Importance

Critical to task management is to identify tasks around two vectors: urgency and importance. Obviously, we want to tackle important things before less important things, so it’s critical to balance between important-urgent matters versus important-not-urgent matters. Regardless of how many important-urgent tasks teams have, it is always important to tackle the important-not-urgent tasks as well. – Han Yuan, Up work

  1. Trust Members of Your Team to Decide How They Work Best

Stop imposing process and overhead that prevents teams from being productive. Leaders should hire great talent, challenge them with an inspiring vision and then let teams decide on the tasks to best accomplish. As Laszlo Bock wrote, “Give people slightly more trust, freedom and authority than you are comfortable giving them. If you’re not nervous, you haven’t given them enough.” – Mike Weaver, Monsanto



Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Forbes Technology Council

4 Supply Chain Strategies To Drive Digital Transformation

April 16th, 2018

We see that industry boundaries are blurring due to new business models enabled by the digital economy. But are you equipped with the strategies you need to drive digital transformation and succeed in this fast-paced, ever-evolving landscape?

To meet the challenges of today’s digital economy, where customers want both products and services quickly and tailored to their unique specifications, your organization needs to fundamentally reimagine its business processes across the digital supply chain with the following four foundational principles:

  1. Customer centricity: Plan and deliver to the segment of one

In a digital economy, customer centricity is more than just an aspiration. It needs to be integrated with digital supply chain capabilities to serve segments of one consistently.

The experience starts with a touchless supply chain, where you automate wherever possible and manage by exception. This approach enables supply chain practitioners on the ground to focus on the value-added activities and processes that focus on customers first.

Automation at this level requires an accurate picture of actual demand. Improving forecasting accuracy remains essential, but it’s not enough. You need to capture demand signals in real time from multiple data sources. These sources include structured data – such as orders, point of sale, and Internet of Things (IoT) sensor data – as well as unstructured data – including texts, e-mails, social media, sentiment analyses, and predictive algorithms. All of this information must be brought together to deliver a true picture of actual demand to yield insights that facilitate improved business performance and superior customer service.

This demand drives integrated business planning processes that must be responsive and flexible to changes in supply, demand, and other signals across the supply chain. These improved processes enable companies to better manage collaboration across partner networks and more efficiently handle everything from a shipment of one item to multiple truckloads headed across borders. The result is the ability to deliver the outcomes customers want – even as preferences, requirements, and the digital economy continue to evolve.

  1. Predictive business: Design, make, and maintain the product of one

In the face of growing complexity and rapid change, how do you keep pace? One way is to see what’s coming before it happens:

  • Addressing issues before they become major concerns
  • Fixing machines before they break down
  • Adjusting shipments to avoid traffic or weather problems
  • Realigning manufacturing to adjust to sentiment analysis

Thanks to the emerging technologies available in the digital economy, all of this is possible. The leading-edge practice for a predictive business is to build and manage networks of  digital twins. A digital twin uses IoT sensor data to maintain a direct connection between a physical product or asset and its designed, manufactured, and deployed digital representation.

Through the use of digital twins, you can gain a 360-degree view of your entire network’s equipment, products, and assets – from products running in customers’ homes and full deployments of commercial-grade assets out in the field to machines and equipment operating within your business.

Visibility has little value in and of itself, however. You need to use the data available to create true product and asset intelligence and then act on it. The more you know about how products perform and how they’re used, the more accurately you can predict service disruptions and detect what customers want most.

By capturing and incorporating customer demand and usage data across a network of products and assets represented by a digital twin, you can create valuable insights about what’s on the horizon. This advantage can lead to more relevant product design, higher availability in the field, and improved customer service with the outcomes that consumers crave.

  1. Smart automation: Manufacture the lot size of one

Automation is everywhere across the supply chain, from robotics and autonomous forklifts in the warehouse to the potential of drones delivering goods. But when it comes to smart automation, manufacturing is leading the way – motivated in large part by the move from mass production to mass customization of individualized and personalized products.

To seize this opportunity, leading companies are rethinking their design, manufacturing, and logistics processes. A key trend is the transformation from continuous production lines to flexible production cells that can be moved and used in a nearly plug-and-play manner. Smart sensors that provide critical status data can assist in the automatic routing of products to the next cell in the production process.

As a result, companies can better manufacture the lot sizes of one that personalized products demand. In conjunction with more agile manufacturing processes, businesses are also retooling their distribution and delivery processes. Traditionally seen as cost centers, distribution centers are now viewed as strategic assets that can present a competitive advantage for savvy companies.

A range of powerful emerging technologies is enabling organizations to realize this advantage. To maximize flexibility without carrying the cost of large amounts of inventory, for instance, many companies are turning to 3D printing to generate products on demand. Other enterprises are turning to technologies such as machine learning, IoT, and robotics.

  1. Total visibility: Analyze and manage the supply chain of one

In addition to the individualization, automation, and responsiveness required to succeed in the digital economy, it’s vital to provide real-time visibility to every role across the extended supply chain. But how, exactly, do you achieve that?

Total supply chain visibility requires nothing less than a digital mirror of your business. The goal is to see everything – from the movement of goods in production or transit to demand signals and relevant data from sentiment analysis, point-of-sale systems, and other critical sources. Total visibility also means the ability to see traffic jams, accidents, and weather patterns that can affect sales and deliveries or cause supply chain disruptions.

Since modern supply chains always extend beyond the four walls of your organization, you need to flexibly coordinate and collaborate across complex business networks of partners, manufacturing facilities, warehouses, and distribution centers.

With a digital mirror of your extended supply chain, you can connect the real world to the planned world, enabling you to:

  • Improve sustainability and compliance across your global supply
  • Help ensure ethical product sourcing
  • Streamline cross-border transactions
  • Minimize exposure when performing product recalls

The objective is to identify potential disruptions and sense surges in demand. Gaining this ability will help you improve business responsiveness, minimize risk across the supply chain, and provide the kinds of experiences and outcomes that customers crave.



Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Richard Howells.


What Is Blockchain And What Can Businesses Benefit From It?

April 9th, 2018

It seems the blockchain revolution is in full swing. Over the course of a one-year period, Google search requests for the keyword “blockchain” have increased by 250%. The U.S. Senate recently had a public discussion about the blockchain’s most prominent application, cryptocurrency. And several public entities have added “blockchain” to their company name. So what’s all the hype about? What is blockchain and how will businesses benefit from it?

What is a blockchain?

In simple terms, a blockchain can be described as an append-only transaction ledger. What that means is that the ledger can be written onto with new information, but the previous information, stored in blocks, cannot be edited, adjusted or changed. This is accomplished by using cryptography to link the contents of the newly added block with each block before it, such that any change to the contents of a previous block in the chain would invalidate the data in all blocks after it.

Blockchains are consensus-driven. A large number of computers are connected to the network, and to reduce the ability for an attacker to maliciously add transactions on the network, those adding to the blockchain must compete to solve a mathematical proof. The results are shared with all other computers on the network. The computers, or nodes, connected to this network must agree on the solution, hence the term “consensus.”

This also makes the work of appending data to the ledger decentralized. That is, no single entity can take control of the information on the blockchain. Therefore, we need not trust a single entity since we rely on agreement by many entities instead. The beauty of this construct is that the transactions recorded in the chain can be publicly published and verified, such that anyone can view the contents of the blockchain and verify that events that were recorded into it actually took place.

So to summarize, blockchains are:

  • Transaction ledgers
  • Immutable
  • Consensus-driven
  • Decentralized
  • Trustless (it’s not based on a system of trust)
  • Secured by cryptography
  • Can be made public


What businesses benefit?

Prior to the advent of the blockchain, there was no way to secure and validate ownership in a digital asset or verify a transaction in a trustless, public manner. Take, for example, the act of utilizing a software license to gain access to a program like Microsoft Word. To enforce the right to use the software, it must check a centralized server operated by Microsoft. If Microsoft wanted, it could deny access to the software or transfer those permissions to another user. While we consider Microsoft a trusted entity, the risk of illicit behaviour increases when an untrusted party is introduced.

Perhaps a better example is ownership of a more valuable asset, such as a substantial share in a company or valuable digital asset such as a one-off piece of digital artwork. To transfer shares of ownership in a company, the current model requires stacks of paperwork, a lawyer or a centralized and trusted entity, such as the New York Stock Exchange.

What about transferring a digital asset like art? How do you prevent people from copying the digital file and sending many others a copy? If there’s no way to publicly verify the transfer of a single asset to a single entity, then there’s no way to enforce ownership or authenticity. This is why the value in art is always in the physical good.

The blockchain is the first technology that enables the transfer of digital ownership in a decentralized and trustless manner. In fact, there are companies like Polymath that are disrupting the industry by creating digital tokens that can represent ownership in a company, or DAEX, which is seeking to disrupt the world of digital art by publishing ownership on the blockchain.

While technology and supporting platforms around the blockchain ecosystem are sure to evolve, to answer the question of which businesses will initially benefit from its use, are the ones which possess the following traits:

  • Transaction-based
  • Benefits from public scrutiny
  • Benefits from history that can’t be rewritten
  • Decentralization benefits the end user or customer


Revolutionary But Limited

It’s easy to get sucked into the hype of one of the fastest-growing new technologies. But it is important to understand that blockchain has its practical limits. It may not be a suitable replacement for where centralization is needed (or at least where there is no added benefit to decentralization) or where transaction malleability is needed.

An example of where I think blockchain may complicate things but not add value to a problem is the case for medical records. Since information privacy is protected by federal regulation, having them accessible to the public may not necessarily be a good thing. The only way to make something like this work would be to encrypt the information, then store the decryption keys on centralized entities to allow other nodes the ability to read the encrypted data. But this would require a few specific parties to be able to read and write the encrypted data. And therefore, a central authority would need to control the licensing of this information to make sure that bad actors do not have the ability to hijack one’s medical records. Also, erroneous information that is added to the chain may be impossible to change.

No doubt, the supporting tech around blockchain will quickly evolve, as will the potential for applications that rely on it. With its growth will come an increase in consumer awareness to its benefits, as well as an equally supportive community. Businesses that believe they might be able to add value by incorporating this technology into their product or service can tap into a growing community of blockchain engineers, be it in a freelance setting or a professional blockchain development agency. As with any nascent industry, talent will initially be scarce, but as the ecosystem develops, the supply should hopefully increase to support it.



Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Forbes Agency Council.

How AI Is Improving The Landscape Of Work

April 2nd, 2018

There have been a lot of sci-fi stories written about artificial intelligence. But now that it’s actually becoming a reality, how is it really affecting the world? Let’s take a look at the current state of AI and some of the things it’s doing for modern society.

How artificial intelligence is improving the workplace

Creating New Technology Jobs

According to Indeed research, demand for workers with AI skills has experienced steady growth over the past few years. When you add the fact that there’s currently a shortage of job seekers who can meet that need, it only makes the skills more valuable for those who do possess them or would like to learn.

What kinds of jobs are being created, specifically? Obviously, they’re mostly tech-related, but there’s actually some variety when you break it down. Job listings that most frequently included “artificial intelligence” or “machine learning” include data scientists, software engineers, software architects, and full-stack developers.

A few non-tech roles made the list as well, including research scientists and product managers, so there are certainly options for others who want to enter the field.

Plenty of big companies like Amazon are doing the hiring, but there are also start-ups finding new and creative ways to utilize AI.

Using Machine Learning To Eliminate Busywork

Just about everyone has experienced that feeling of not having enough hours in the day to accomplish everything they need to. By enabling smart computers to complete certain tasks, workers can free up their time for their more important work.

According to a DigitalOcean report, while only 26% of developers are currently using AI or machine learning tools in their workflows, 81% are interested in learning more about them.

Those in non-developer roles stand to benefit here too, of course. For instance, perhaps accountants could use machine learning to fill out forms. Or clothing companies could use smart algorithms to make outfit recommendations. Or customer service teams could use it to answer basic questions on a support ticket or live chat session.

Preventing Workplace Injuries With Automation

According to this study by Injury Claim Coach, thousands of injuries and fatalities could be avoided by automating the hazardous elements of certain jobs.

The study discovered that across all industries, 5,190 people died from workplace injuries in 2016 (and many more suffered non-fatal injuries). That averages out to 100 people per week.

Particularly hazardous careers included motor vehicle operation and construction (trailed distantly by grounds maintenance). These careers also happen to be quite likely to experience automation in the not-too-distant future.

So, how many lives could automation save? Well, assuming just 14% automation, it could be as high as roughly 3,500 per year by 2030.

So rather than thinking in terms of AI taking jobs away, it might be more accurate to think about how many dangerous jobs humans won’t need to do anymore. Protecting lives (and freeing up those workers to pursue safer careers) is definitely a powerful use case for automation. However, just so you don’t end up in a phased-out career, work on becoming irreplaceable now.

Reducing Human Error With Smart Algorithms

While the human brain is a powerful thing, no one makes perfect decisions all the time. It’s frankly impossible for us to store enough data about past situations, actions, and outcomes, and evaluate the probability of each one occurring, in the time it takes us to make a choice. We’re simply operating with limited data-sets, which hinders our abilities to select the optimal decisions.

With computers, that’s not the case. If an AI can draw upon a database with thousands or millions of scenarios, it can process that information to figure out what decisions are most likely to result in successful outcomes. “That is much of what machine learning and AI is all about–taking complex information and organizing it to help make the correct decisions fast,” says Mark McFarland, Team Lead of Technical Recruitment at Relativity.

Of course, this won’t work for all types of decisions (at least in the current state of AI), as some decisions require uniquely human considerations. But especially in the business world, it can certainly enable businesses to optimize their decision-making as logically as possible.

This is just a fraction of the potential use cases AI could have in the future. If all this has you interested in pursuing a machine learning career, start by developing these key skills to succeed.


Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Laurence Bradford.