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Tag Archives: Through

Better Analytics Through AI: Our Take on Gartner’s AI Trends

September 6, 2020   Sisense

AI and machine learning are the future of every industry, especially data and analytics. In Growing Up with AI, we help you keep up with all the ways these pioneering technologies are changing the world.

Reading through the Gartner Top 10 Trends in Data and Analytics for 2020, I was struck by how different terms mean different things to different audiences under different contexts. We hear a lot about AI and analytics not only in internal conversations, but also from our customers and prospects. But what do we really mean when we talk about these issues?

Seeing as how they will only become more important to our world, I thought it would be worthwhile, as Sisense’s head of AI research (AIR), to dive into 7 of the 10 trends on the list and give my views on each.

The article starts with a big statement about AI starting to operationalize, moving the requirements for data and analytics infrastructure to accelerate the development and adoption phase:

“By the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in streaming data and analytics infrastructures.”

This is a major change in the way AI has been used in the past alongside data and analytics, making both more powerful and effective. Let’s dive into these trends and see what else is on the horizon.

packages CTA banners Cloud Data Teams Better Analytics Through AI: Our Take on Gartner’s AI Trends

Trend 1: Smarter, faster, more responsible AI 

Gartner:

“Within the current pandemic context, AI techniques such as machine learning (ML), optimization and natural language processing (NLP) are providing vital insights and predictions about the spread of the virus and the effectiveness and impact of countermeasures.

“Significant investments made in new chip architectures such as neuromorphic hardware that can be deployed on edge devices are accelerating AI and ML computations and workloads and reducing reliance on centralized systems that require high bandwidths. Eventually, this could lead to more scalable AI solutions that have higher business impact.”

My take:

Augmentation and reinforcement learning are much more powerful than out-of-the-box solutions, and this is what’s guiding us along the way. Planning for every feature starts with questions about how the user will be able to play around with and modify the input to see how it affects the result. It was only natural for us here at Sisense to put significant investment into knowledge graphs, NLP, and automated machine learning. Together, they enable users to actively engage with the system, enjoying recommendations along with analysis. These features also facilitate a positive feedback loop, using engagement to strengthen what works and get rid of what doesn’t.

One result is that systems become much more intuitive: Users can take advantage of the “Simply Ask” feature to check “what are my sales next two months” and receive chatbot messages with projected visualizations and suggestions for further exploration routes. In a similar way, the forthcoming “Explanations” feature provides users with possible drivers of the movements in the data automatically, using knowledge graphs to go beyond the boundaries of their charts. This can turn the problem definition environment to multidimensional and learn from the user interaction with the system to personalize and match the results.

From Forecast to Trends to natural language querying, we are completely transparent about the technology behind and the statistical characteristics of the output. Whatever you’re seeing when you use Sisense, you can easily dig into the systems behind it.

Trend 2: Decline of the dashboard

Gartner:

“Dynamic data stories with more automated and consumerized experiences will replace visual, point-and-click authoring and exploration.” 

My take:

At Amazon, everyone in a meeting sits down at the beginning and reads a full write-up, and then the discussion begins, rather than sitting through an endless PowerPoint presentation during the whole meeting. They focus on real storytelling rather than bullet points. We expect something similar to happen with dashboards: fetching insights-driven digests just in time, but also accompanying the daily routines with an “agent” supporting business flows in various tools.

Do you like to see what you missed first thing in the morning? Be alerted on significant movements? Is an executive summary enough to start the ball rolling, knowing you can always do a deep dive and ask for more? Using your favorite task management solution? The world is moving from the static, rigid experience to the data-, insight-, and personalization-driven assistant that knows how you want specific analytics to be served.

In order to make that work, a number of moving parts need to come together as one well-oiled machine: embedded interfaces (on-the-go via your device, in your email, chat, or in-app), pretrained analytics services and training pipeline, the vehicle to facilitate the data model creation, and the right visualization and narration to make the results digestible, trustable, and learning.

This is what keeps Sisense AIR busy: dashboard automation research and our knowledge graph, which has incorporated the behavior of thousands of past users. 

Trend 3: Decision intelligence

Gartner:

“By 2023, more than 33% of large organizations will have analysts practicing decision intelligence, including decision modeling.” 

“It provides a framework to help data and analytics leaders design, model, align, execute, monitor, and tune decision models and processes in the context of business outcomes and behavior.”

My take:

Decision-making automation requires a lot of steps: First you document the process, then configure it based on the result, then automate the possible parts. My take on it is that if you can automate the loop from data to analysis to decision back to data, it is not analytics, it’s robotic process automation. There’s an argument to be made that once decision-making on a use case becomes predictable, it should be moved from BI to a part of the back office.

But that kind of thinking comes from the world we used to know, a world that was less volatile and more manageable, more influenced by the proximity ecosystem than by world events and climate. Today, the world changes at a speed that’s hard to fathom, so decision-making needs to be adjusted based on insights coming from data, accompanied by recommended actions. “Survival of the fastest” is the rule today.

Trend 4: X analytics

Gartner:

“Gartner coined the term ‘X analytics’ to be an umbrella term, where X is the data variable for a range of different structured and unstructured content such as text analytics, video analytics, audio analytics, etc.”

My take:

The world is wider than the traditional BI tabular data. It’s visual, it’s spoken, it’s audible. Why use just one of the senses and limit your perspective?

Sisense recently used our ecosystem of ML service providers to help scan and surface the medical crowd wisdom of COVID treatments from piles of textual data from a site called G-Med. There was no point in reinventing the wheel to build our own video, image, speech, and text analysis tools — there are plenty of those on the market already.

How exactly is all that data going to talk to each other and come together to provide the end-to-end analysis? Knowledge graphs will be the base of how the data models and data stories are created, first as relatively stable creatures and, in the future, as on-demand, per each question.

Trend 5: Augmented data management

Gartner:

“Augmented data management uses ML and AI techniques to optimize and improve operations. It also converts metadata from being used in auditing, lineage and reporting to powering dynamic systems.”

My take:

The Gartner article doesn’t go beyond lineage or workload automation. That’s important, but that’s only what’s going on today. Fetching calculation results ahead of the question improves performance, but it’s still limited to the data model or dimensional paradigm of the single individual in the organization. Do they have the required perspective to include hurricane data for the supply chain dashboard for East Asia? Domain experts would likely decide to include that information after reading about losses in the news. What if the relevant data could be added to the context to tell the data story without humans needing to take action themselves? Data exchanges will play a more significant role in the future, extending their offerings to data modeling.

Trend 6: Cloud is a given 

Gartner:

“By 2022, public cloud services will be essential for 90% of data and analytics innovation. As data and analytics moves to the cloud, data and analytics leaders still struggle to align the right services to the right use cases, which leads to unnecessarily increased governance and integration overhead.”

My take:

Cloud is here to stay. I witnessed the mainframe/PC/cloud/personal graphics processing unit evolution. To me, the tipping point of cloud analytics will be in the “context as a service” combination of data and logic components served based on user questions. With offerings like AWS Outposts, it couldn’t be easier to start the cloud journey.

In the analytics world, it’s crucial to stay up to date, implementing “continuous integration/continuous delivery” systems and A/B testing for better performance and experience. This is only possible with cloud services. Cloud combined with compliance with General Data Protection Regulation and SOC are vital to gain customers’ trust. Data-hungry calculations will be costly to perform in the cloud if data is on-premises due to data gravity and latency. Adjusting a system’s architecture can make all the difference quickly, meaning you can easily pull insights from large datasets.

Trend 7: Data and analytics worlds collide

Gartner:

“Data and analytics capabilities have traditionally been considered distinct entities and managed accordingly. Vendors offering end-to-end workflows enabled by augmented analytics blur the distinction between the two markets.

The collision of data and analytics will increase interaction and collaboration between historically separate data and analytics roles. This impacts not only the technologies and capabilities provided, but also the people and processes that support and use them. The spectrum of roles will extend from traditional data and analytics roles in IT to information explorer, consumer, and citizen developer as an example.”

My take:

I agree that new roles are required. As new data and analytics products are built and every product begins to have data and analytics elements in it, data/knowledge product managers will emerge. These specialists will understand data and be able to run and create queries and transformations but will also be knowledgeable about the applications running on top of those data streams.

Regarding data and tools, “extract, transform, and load” (ETL) will become ETLT. The “T” stands for the “transformation pipelines” either bringing data from the exchanges or pre-trained ML services or training pipelines for both structured and unstructured data. Software developers and data scientists can use these same pipelines to deploy their parts of the application, and analytics workflows can be automated to the point where business users can even trigger them without outside help.

AI and analytics: Building the future together

If you have data, odds are you have a lot of it. You’ve probably got more than you can handle. Alone, that is. Only AI will be able to help humans make sense of the huge datasets being generated every day by countless individuals and devices. AI systems will play greater and greater roles in our personal and business worlds, so whatever you’re building, start thinking about the ways AI can help your product, service, colleagues, and customers be better. And whatever you’re working on, build boldly.

packages CTA banners Cloud Data Teams Better Analytics Through AI: Our Take on Gartner’s AI Trends

Inna Tokarev-Sela, Sisense’s Head of AI Research, has over 15 years’ experience in the tech industry. She spent the last decade at SAP, driving innovations in cloud architecture, in-memory products, and machine learning video analytics. A frequent speaker at industry events like IBC, NAB, Wonderland AI, and Media Festival, Inna holds a BS in physics and computer science, an MBA, and an MS in information systems, having written her thesis on neural networks.

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Email Messages Sent From Dynamics 365 Through Server-Side Synchronization Will Now Save to Exchange “Sent Items” Folder

August 5, 2020   Microsoft Dynamics CRM

Introduction

 

As part of Microsoft’s continued efforts to improve Dynamics 365 and Server-Side Synchronization, the behavior of how outgoing email messages sent through Server-Side Synchronization via Dynamics 365 interact with Exchange folder structures has been modified. This change will affect the sizes of Exchange mailboxes tied to Dynamics and can lead to mailboxes being unable to send email if Exchange limitations are reached.


Background on how emails sent through Server-Side Synchronization via EWS works in a nutshell

When Server-Side Synchronization is configured using default settings, pressing “Send” on a fully composed new Email Message will cause the following to occur:

  • The Email Message entity record is saved to the ActivityPointerBase SQL table (among others) in the “Pending Send” status
  • The User or Queue tied to the “From” field on the Dynamics 365 Email Message record has an associated Dynamics 365 Mailbox entity record. The outgoing polling period will elapse for this Mailbox, triggering a call to check for messages in a “Pending Send” status under the User or Queue context.
  • Messages in a “Pending Send” status are evaluated to ensure able to be provided to Exchange.
  • Messages are then provided to Exchange through EWS calls as part of the Dynamics 365 Server-Side Synchronization integration.
  • Exchange responds that it accepts the EWS request and will send the Email Message provided.
  • Dynamics 365 updates the Email Message record to a “Sent” status.
  • Exchange will save this email to the “Drafts” folder
  • Exchange will provide the email to the outbound connector, which sends the message.
  • Once sent, the email message is deleted from the Drafts folder.

This flow is markedly different than what a user would expect to see if they send an Email through Outlook, namely that messages sent through Server-Side Synchronization are not saved in the “Sent Items” folder. This ensures that mailboxes don’t increase in size and that Email Messages aren’t saved in two places (both Dynamics 365 SQL tables and Exchange folders).


Changes made to how Exchange handles Emails sent via Server-Side Synchronization

As a result of customer feedback, Email Messages sent through Dynamics 365 via Server-Side Synchronization now save a copy to the “Sent Items” folder in Exchange. This change allows Exchange administrators and legal discovery requests to easily identify messages that would otherwise require Dynamics 365 access to detect.


Effect of these changes on Exchange and Dynamics 365

Since messages sent through Server-Side Synchronization historically did not save a copy to “Sent Items” in Exchange, the Exchange mailbox never increased in size. As such, Dynamics and Exchange administrators might not be aware of this change, which will cause these mailboxes to increase in size over time unabated.

Due to this, the size of the mailbox or the number of items in the “Sent Items” folder (as examples) can reach a point where Exchange runs into limitations on the save of new items. These Exchange configuration values which prevent the save of new items are referred to as quota limitations.

In the event a quota limitation occurs when Dynamics 365 attempts to send a message through Server-Side Synchronization to Exchange, Exchange responds that it is unable to save the message and therefore cannot deliver it. This is provided to Dynamics in the form of an HTTP status code 403 response (forbidden), and the exception “Cannot submit message” due to “ErrorQuotaExceeded”. Dynamics will not change the status of the Email Message to “Sent” due to this exception.


How Exchange Quota Limitations can affect Users and Queues in Dynamics 365

In the event a quota limitation is encountered, the associated Dynamics 365 mailbox will cease to be able to send any outbound mail through Server-Side Synchronization. This can only be resolved by correcting the issue causing the limitation on the Exchange mailbox to occur. Quota limitations can be in many forms, such as the number of items allowed in a folder, overall size of the folder, overall size of the mailbox, etc.

For a Dynamics 365 User, this causes Dynamics 365 Email Message entity records to not be able to send if the User record is in the “From” field. Other users not running into quota limitations will be able to continue to successfully send email messages as normal. User mailboxes are typically monitored through Exchange clients (such as Outlook) by the users who own them, where the clients will normally point out mailbox size limits that are being approached. As such, user mailboxes are not typically affected by this change.

For a Dynamics 365 Queue, this causes Dynamics 365 Email Message entity records to not be able to send if the Queue record is in the “From” field. This can cause a much larger issue as Queues are typically used to send a much higher volume of emails, such as orders, customer service interactions, and marketing campaigns. When a queue that is successfully sending email messages suddenly runs into a quota limitation, it ceases to operate until the limitation is resolved.


How to prevent Exchange Quota Limitations from affecting Server-Side Synchronization

Dynamics 365 organizations that utilize queues for sending large quantities of email messages should verify through Exchange what policies are in use for the associated mailboxes. Exchange administrators can perform the following to ensure that quota limitations are not encountered:

  • Change the values of the quota limitations mailbox-level to ensure they cannot be encountered
  • Enable auto-archival on the “Sent Items” folder in a way that would prevent quota limitations from being encountered
  • Manual observation of the folder structures and movement of associated items to prevent quota limitations from being encountered

On the Dynamics side, organizations that wish to change this behavior for Email Messages sent through Server-Side Synchronization must open a support ticket with Microsoft. This behavior can be modified by our support team at an organization-level to never save copies of the item to the “Sent Items” folder.

Due to customer feedback, Microsoft is working on the creation and testing of an OrgDBOrg setting that is toggle-able to change this behavior without needing to open a support ticket with Microsoft. More information on OrgDBOrg settings and how to configure them can be found here:

OrgDBOrg Settings and Documentation on How They Work

OrgDBOrg Solution Tool by Sean McNellis that easily allows modifications to these settings

I will update this blog when this OrgDBOrg setting is available.

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Google AI researchers want to teach robots tasks through self-supervised reverse engineering

May 26, 2020   Big Data

A preprint paper published by Stanford University and Google researchers proposes an AI technique that predicts how goals were achieved, effectively learning to reverse-engineer tasks. They say it enables autonomous agents to learn through self-supervision, which some experts believe is a critical step toward truly intelligent systems.

Learning general policies for complex tasks often requires dealing with unfamiliar objects and scenes, and many methods rely on forms of supervision like expert demonstrations. But these entail significant tuning; demonstrations, for example, must be completed by experts many times over and recorded by special infrastructure.

That’s unlike the researchers’ proposed approach — time reversal as self-supervision (TRASS) — which predicts “reversed trajectories” to create sources of supervision that lead to a goal or goals. A home robot could leverage it to learn tasks like turning on a computer, turning a knob, or opening a drawer, or chores like setting a dining table, making a bed, and cleaning a room.

“Most manipulation tasks that one would want to solve require some understanding of objects and how they interact. However understanding object relationships in a task-specific context is non-trivial,” explain the coauthors. “Consider the task [making a bed.] Starting from a made bed, random perturbations to the bed can crumple the blanket, which when reversed provides supervision on how to flatten and spread the blanket. Similarly, randomly perturbing objects in a clean [or] organized room will distribute the objects around the room. These trajectories reversed will show objects being placed back to their correct positions, strong supervision for room cleaning.”

VB Transform 2020 Online – July 15-17. Join leading AI executives: Register for the free livestream.

 Google AI researchers want to teach robots tasks through self supervised reverse engineering

TRASS works by collecting data given a set of goal states, applying random forces to disrupt the scene, and carefully recording each of the subsequent states. A TRASS-driven agent explores outwardly using no expert knowledge, collecting a trajectory that when reversed can be used by the agent to learn to return to the goal states. In this way, TRASS essentially trains to predict the trajectories in reverse so that the trained model can take the current state as input, providing supervision toward the goal in the form of a guiding trajectory of frames (but not actions).

At test time, a TRASS-driven agent’s objective is to reach some state in a scene that satisfies certain specified goal conditions. At every step the trajectory is recomputed to produce a high-level guiding trajectory, and the guiding trajectory decouples high-level planning and low-level control such that it can be used as indirect supervision to produce a policy via model and model-free techniques.

In experiments, the researchers applied TRASS to the problem fo configuring physical Tetris-like blocks. With a real-world robot — the Kuka IIWA — and a TRASS vision model trained in simulation and then transferred to the robot, they found that TRASS successfully paired blocks it’d seen during training 75% of the time and blocks it hadn’t seen 50% of the time over the course of 20 trials each.

TRASS has limitations in that it can’t be applied in cases where object deformations are irreversible, for example (think cracking an egg, mixing two ingredients, or welding two parts together). But the researchers believe it can be extended by using exploration methods driven by state novelty, among other things.

“[O]ur method … is able to predict unknown goal states and the trajectory to reach them,” they write. “This method used with visual model predictive control is capable of assembling Tetris-style blocks with a physical robot using only visual inputs, while using no demonstrations or explicit supervision.”

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Survey: How Business Leaders Are Managing Through COVID-19

May 1, 2020   NetSuite

It’s a tough time for businesses. Leaders are faced with managing critical short-term priorities as they try to ensure their business survives the global pandemic, while also having to determine how they can set their business up for a faster rebound and long-term success.

To learn more about how business leaders are coping and what we can do to help in both the short and long term, our research team at Brainyard surveyed 174 executives and managers from small ($ 10 million or less), midsize ($ 11-50 million) and midcap ($ 51-500 million) companies in North America over the first week of April. Here’s what we found.

Almost Every Business Has Been Impacted
Businesses have been affected, some positively. But returning to normal is still far out.

  • 85 percent of respondents said their businesses have been harmed by the coronavirus outbreak. 53 percent classified the harm as substantial.
  • The pandemic has created increased demand for some businesses, with 14 percent of businesses bringing on more workers and 20 percent asking for more hours from their existing employees to meet growing demands.
  • Almost universally, new safety and business policies/practices have been put in place. 93 percent of businesses have worked to educate staff on new safety practices; 95 percent have canceled business travel; and 89 percent have offered remote work to at least some employees.

A New 2020
Business leaders are re-evaluating spending for this year and starting to plan for the road ahead.

  • Every company is re-evaluating its spending plans for the year. Capital spending will experience the biggest lost with nearly 60 percent planning large (46 percent) or small (13 percent) cuts.
  • Only 17 percent of executives think they can get through this crisis without some form of financial help. When asked about the sources of financial help, 80 percent noted federal aid, 43 percent noted bank loans and 20 percent are looking for help from state led programs.
  • Business leaders from companies of different sizes had very different outlooks. A quarter (25 percent) of executives from businesses with less than $ 10 million in revenue are very confident of success in the coming six months compared to only 8 percent of executives from businesses with revenues in excess of $ 10 million.
  • Businesses are prioritizing technology investments. 30 percent of businesses are planning to increase spending on technology, while 31 percent expect investments in technology to remain flat from 2019 levels. In contrast, spending plans varied in other categories including payroll (48 percent), marketing (53 percent), sales (52 percent) and production (43 percent) planning cuts.

While business leaders expect this to be a difficult year for their companies and the economy, there is a sentiment of hope if they can control spending, get access to financial aid and focus on areas that deliver an immediate return.

We all know it’s a long road ahead, but every journey starts with a single step and together we can rebuild and return to growth.

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Speed Up Decisions To Steer The Ship Through This Time Of Unprecedented Disruption

April 16, 2020   BI News and Info
 Speed Up Decisions To Steer The Ship Through This Time Of Unprecedented Disruption

The current business and social climate has created unforeseen challenges for enterprises of all shapes and sizes. The disruptions, both internally and externally, are unprecedented in modern times. Within the enterprise, the CFO and the finance office arguably face some of the greatest pressures to steer the enterprise in the right direction.

Uncertainties and choices to be made

At this time, there are many uncertainties that must be navigated by CFOs and specifically the financial planning & analysis (FP&A) professionals on their teams. The choices are many and include:

  • Impact on revenue with disruption within sales channels
  • Cash flow position and liquidity management
  • Expense management and cost containment
  • Best-case earnings projections and guidance based on what’s known of the unknown

These choices don’t just come to the FP&A domain in a singular form, but as a collection of issues and choices to be addressed holistically. Should we tap into a line of credit to keep our working capital fluid? Will our current cash flow and liquidity allow us to cover short-term expenses and current and future capital initiatives? Can we take advantage of government programs with employee salary assistance? What do we tell our shareholders for balance-of-year projections? Are there opportunities available to assist our customers to succeed?

These issues are faced not only by finance but rather across the enterprise as different groups examine their budgets and potential tactics. Does marketing continue with current programs? Does the supply chain face disruptions? What is the effect on demand plans? What does human resources propose for the current headcount levels, and how does that affect outputs? All these individual budgets and models need to be brought together in a single view to enable understanding of the interdependencies.

Collaborative enterprise planning to break down silos

These challenges can easily be addressed by a modern planning paradigm: collaborative enterprise planning. Collaborative enterprise planning involves breaking down silos, allowing plans of all sorts to be linked instantaneously. This way, finance can get the true picture of current fiscal health to advise the best-case scenarios and steer away from poor ones. For example, finance can evaluate a sales plan’s impact on marketing campaigns, or link a reconciled S&OP process to a financial plan. An analyst can determine what headcount plans might impact employee productivity and cash positions.

Finance also needs to be in a position to make decisions quickly. With collaborative enterprise planning, the finance function can move away from scheduled plans. Today, it is no longer tenable to work in an environment where worst-in-class finance departments delay decisions because they are beholden to spreadsheets or outmoded planning tools. Rather, with the right technology on hand, they can simulate the impact of multiple scenarios immediately. They can use modern analytics complemented with predictive analytics and machine learning to support the recommended and best-case outcomes. Dashboards and built-in visualization allow for real-time reporting and an aesthetically pleasing experience for the end users with minimum chance of errors – unlike spreadsheets.

Instant discussion for fast decision-making

Finally, most organizations, including finance, are no longer working within four walls, but rather in a virtual capacity. Collaborative enterprise planning promotes instant discussion during planning and forecasts. Geo-political issues have made an extraordinary impact on commodity prices; this, along with pandemic-related issues, requires collaboration on the fly and consensus on the go. Decisions must be made immediately, not dependent on meetings, voice mails, and email threads.

With collaborative enterprise planning, finance departments can lead their enterprise to the best possible outcome and better results to navigate properly through these trying times. Collaborative enterprise planning enables finance to learn from all aspects of the organization, recommend and evaluate multiple scenarios, make the right decisions – and truly step up to be leaders in the enterprise.

For more information read this report from Ventana Research, “Collaborative Enterprise Planning: Improving the Business Value of Planning and Budgeting across the Enterprise.”

Follow SAP Finance online: @SAPFinance (Twitter) | LinkedIn | Facebook | YouTube

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Researchers propose paradigm that trains AI agents through evolution

March 25, 2020   Big Data

A paper published by researchers at Carnegie Mellon University, San Francisco research firm OpenAI, Facebook AI Research, the University of California at Berkeley, and Shanghai Jiao Tong University describes a paradigm that scales up multi-agent reinforcement learning, where AI models learn by having agents interact within an environment such that the agent population increases in size over time. By maintaining sets of agents in each training stage and performing mix-and-match and fine-tuning steps over these sets, the coauthors say the paradigm — Evolutionary Population Curriculum — is able to promote agents with the best adaptability to the next stage.

In computer science, evolutionary computation is the family of algorithms for global optimization inspired by biological evolution. Instead of following explicit mathematical gradients, these models generate variants, test them, and retain the top performers. They’ve shown promise in early work by OpenAI, Google, Uber, and others, but they’re somewhat tough to prototype because there’s a dearth of tools targeting evolutionary algorithms and natural evolution strategies (NES).

As the coauthors explain, Evolutionary Population Curriculum allows the scaling up of agents exponentially. The core idea is to divide the learning procedure into multiple stages with an increasing number of agents in the environment, so that the agents first learn to interact in simpler scenarios with fewer agents and then leverage these experiences to adapt to more agents.

 Researchers propose paradigm that trains AI agents through evolution

Above: Evolutionary Population Curriculum applied to agents ‘playing’ a Grassland Game.

Image Credit: Evolutionary Population Curriculum

Evolutionary Population Curriculum introduces new agents by directly cloning existing ones from the previous stage, but it incorporates techniques to ensure that only agents with the best adaptation abilities move onto the next stage as the population is scaled up. Crossover, mutation, and selection is performed among sets of agents in each stage in parallel so that the influence on overall training time is minimized.

The researchers experimented on three challenging environments: a predator-prey-style Grassland game, a mixed cooperative and competitive Adversarial Battle game, and a fully cooperative Food Collection game. They report that the agent “significantly” improved over baselines in terms of performance and training stability, indicating that Evolutionary Population Curriculum is general and can potentially benefit scaling other algorithms.

 Researchers propose paradigm that trains AI agents through evolution

“Most real-world problems involve interactions between multiple agents and the problem becomes significantly harder when there exist complex cooperation and competition among agents,” wrote the coauthors. “We hope that learning with a large population of agents can also lead to the emergence of swarm intelligence in environments with simple rules in the future.”

If indeed Evolutionary Population Curriculum is an effective way of isolating the best algorithms for various target tasks, it could help to automate the most laborious bits of AI model engineering. According to an Algorithmia study, 50% of companies spend between 8 and 90 days deploying a single AI model.

The code is available in open source on GitHub.

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Delivering More Effective Disaster Relief Through Technology-Enabled Humanitarian Logistics

March 24, 2020   BI News and Info
 Delivering More Effective Disaster Relief Through Technology Enabled Humanitarian Logistics

Passion-inspired thinking is solving complex problems on a global scale, such as bringing scale and speed to disaster relief. Although logistics has mostly been used in commercial supply chains, one type – humanitarian logistics – is also important in disaster-relief operations.

As a global society, we are all tasked with protecting the most vulnerable of our people and resources. Humanitarian logistics services are becoming more critical in response to the rising levels and severity of climate change-related humanitarian crises.

Here are some interesting facts:

  • 22 million people are displaced annually by climate or weather-related disasters, according to the UN.
  • The Paris Agreement of 2016 sets a global framework for limiting global warming to well below 2oC and with a target to limit it to 1.5o. It is widely accepted that dramatic cuts in carbon emissions are needed to meet these goals (emissions are still rising in several major economies).
  • The poorest countries contribute the least to global emissions but are most likely to suffer the impacts of climate change.
  • A 2oC warmer world could cause 190 million people to experience levels of vulnerability to food insecurity greater than today; a 4oC warmer world could impact 1.9 billion (the number does not simply double for a 2oC  warmer world).
  • 32x more people are expected to be displaced from climate-related hazards than other geophysical hazards such as earthquakes.

There is clearly an increased level of risk to the well-being of vulnerable people posed by the effects of climate change. This creates pressure to make providing disaster relief faster and more efficient.

Cloud solutions combined with a global network can be used to enhance collaboration between humanitarian stakeholders (non-governmental organizations, government agencies, etc.), interagency procurement, and pre-qualified suppliers for disaster relief goods and services. Pre-qualified suppliers can be matched with emergency disaster relief needs using artificial intelligence and machine learning.

Contracts or catalogs for pre-qualified suppliers can be hosted on the network for standard, repeatable requests and promoted using automated workflows. Discovery functionality could be used to source suppliers for non-standard, ad hoc relief requests.

Cloud solutions and network technology used in this way enable:

  1. Collaboration between stakeholders (NGOs, emergency services, interagency procurement) and suppliers
  1. Regional disaster resilience/relief agencies to match supply with demand more effectively
  1. Value for money (cost, response time, the right level of aid at the point of need)
  1. Better solutions to the current practices of emails, calls, etc.
  1. Greater levels of transparency of goods/services ordered (track and trace)
  1. Real-time insights into the capabilities of suppliers in disaster impact zones

Tackling climate change is going to be harder than we expect. Most of the world’s nations recognize the need for coordinated, global action on environmental issues. In the meantime, we must ensure we have the means to deploy disaster relief support faster and more efficiently than before. It is estimated that 200 million people could be affected by climate change by 2050. Whether or not this is true, technology can enable humanitarian logistics to cope with future demand and to maintain both the quality of life and dignity of displaced people.

Discover what happens when companies create an environment where people can do work that matches their passions. Learn more about the SAP One Billion Lives initiative.

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Achievements Through Engagement And An Agile Mindset

February 22, 2020   SAP
 Achievements Through Engagement And An Agile Mindset

The modern-day enterprise has a number of factors to juggle. You are working to make your enterprise smart and to enable employees who will reflect you and your brand positively and will propel your enterprise forward. Every decision you make matters – but what exactly will being intelligent bring your company?

The intelligent enterprise

What separates an intelligent enterprise from an average enterprise? An intelligent enterprise gathers information – about the brand, the product, and the customers – and uses that information to take action. It doesn’t find itself playing catchup after something has happened. It utilizes the available intelligent technologies to gather all the data needed to be proactive and solve problems before they become an issue.

Regular data interpretation can help you and your employees understand how your products, services, and brand are seen. Staying on top of this information can ensure that you are always adapting, staying flexible in the face of changing opinions and that your product and offerings never stagnate.

Creating an intelligent enterprise will not only ensure that your products stay top-notch. Most notably, the effect of an intelligent organization creates a fertile and nurturing atmosphere where engagement will surge and an agile mindset can grow.

Boosting engagement

Picture this: you’re at a store, looking to make a purchase. The shop has a good atmosphere, the price is right, and you’re ready to commit. When you go to get help from the staff, they are supportive. They let you speak, and they act like your request is interesting to them.

Enable your enterprise to be represented by interested and engaged employees. An intelligent enterprise fosters the growth of engaged employees. When employees are engaged, they have myriad opportunities to develop an agile mindset, furthering the growth of your workforce and solidifying your brand.

An agile mindset

An “agile mindset” sounds like a simple phrase, but it is often misunderstood. A strong enterprise will be focused on making things happen, on pushing, on growing, and on moving forward. All of these encompass one similar thing: action. Before taking action, it is important to have your brain in the right place. An agile mindset anticipates and eagerly awaits change. It focuses on creating value and does not let itself get mired down in negativity following a setback; instead, it views it as a chance to learn and to grow.

If you’re ready to learn more about creating an agile mindset, watch my video.

As you work to build and improve your enterprise, consider these points for crafting an organization that leads. One that embodies intelligence and builds a strong foundation from which employees can grow. Higher engagement, an agile mindset, and long-term success will follow.

Please follow Dr. Gerd Ehrhardt on Twitter and LinkedIn

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Teradata and Deutsche Telekom Strike Strategic Partnership to Make German SMBs More Successful Through Data, Analytics

October 31, 2019   BI News and Info

Teradata becomes Deutsche Telekom’s central partner for providing data analytics to customers, Teradata Vantage is added into Deutsche Telekom’s portfolio of IT solutions for SMBs, Deutsche Telekom develops the “Digital Sales Assistant”

Teradata (NYSE: TDC), the cloud analytics company delivering Pervasive Data Intelligence, and Deutsche Telekom, announced a strategic partnership at Digital X in Germany, where Oliver Ratzesberger, President and CEO at Teradata, and Hagen Rickmann, Director Business Customers at Telekom Deutschland, were presenting to an audience of 20,000. The partnership will support the digital transformation goals of small and medium sized businesses (SMBs) in Germany, giving them access to the enormous potential of data analytics to provide the insights required for growth and innovation.
 
By combining the strengths of Teradata and Deutsche Telekom, customers benefit from dual technology expertise and an end-to-end offering: Teradata contributes leading software for data analytics and decades of experience in data science and business consulting; Deutsche Telekom provides a comprehensive cloud ecosystem, demand-driven infrastructure, highly secure data centers, IT security and IoT solutions over the proven Telekom network.
 
“This partnership gives SMBs access to data analyses that only large companies have been able to carry out to date,” said Hagen Rickmann, Director Business Customers at Telekom Deutschland. “By combining our strengths, we can provide affordable solutions created especially for our customers.”
 
 Teradata and Deutsche Telekom Strike Strategic Partnership to Make German SMBs More Successful Through Data, Analytics
Teradata Vantage: Turning Data into Answers
As the basis for this partnership, Deutsche Telekom is integrating the leading analytics platform, Teradata Vantage, into its IT solution portfolio. For SMBs in Germany, this provides unprecedented easy and efficient access to data analytics, bypassing the technological complexity that has previously been a barrier to entry. With Vantage, customers can analyze enormous amounts of data in the cloud from a wide variety of sources – including, but not limited to, sensors, machines, smartphones or social media – in real time. By leveraging tools such as machine learning for artificial intelligence, SMBs can discover answers to business-critical questions, increasing their ability to make data-driven decisions.
 
“Data is the backbone of any digital transformation, much like SMBs are the backbone of success and innovation in the German economy,” said Oliver Ratzesberger, President and CEO of Teradata. “Data and analytics – areas that are so crucial for all businesses today – are especially challenging for SMBs to properly leverage, given their lack of in-house expertise and smaller budgets. Together with Deutsche Telekom, we are proud to close this gap by offering a cloud-based, simple solution: we manage the infrastructure, and entrepreneurs focus on the analytics that lead to answers, innovation and growth.”
 
Digital Sales Assistant: Application in Development
Using Teradata Vantage, Deutsche Telekom is already developing its data analytics application for the company’s SMB customers: The “Digital Sales Assistant”, which will enable the sales force to immediately recognize the needs of its customers and respond to them more precisely. By combining existing data sources, such as CRM, ERP, public open source, social media, IoT and telecom data, the sales force gains previously undiscovered information about its customers and is empowered with intelligent recommendations, such as “next best offer,” price/margin optimization and demand forecasts.
 
The Digital Sales Assistant, an easy first access point for SMBs to begin using data and analytics, is expected to be available in spring 2020.
 
Industry Focus: Retail, Real Estate, Industrial Production
Teradata and Deutsche Telekom will initially address companies that do business in retail, real estate and industrial production. Each of these industries can particularly benefit from data and analytics in the following ways:

  • Retail: Data helps retailers optimize their stores, calculate prices correctly, manage logistics chains better and, most importantly, understand customers’ needs.
  • Real estate: Providers can use data analytics to make building-specific forecasts on value creation, occupancy and operating costs. These factors work to determine the value of a property on the basis of factors such as location and trends, or to determine the costs of buying, selling and converting buildings.  
  • Industrial production: Data is the basis for the smart factory of the future and smart manufacturing. By analyzing sensor data in machines, anomalies can be detected and a possible failure can be predicted. This type of predictive maintenance offers enormous savings potential.

 
About Deutsche Telekom
https://www.telekom.com/en/company/company-profile
 

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Automatically rotate through Power BI report pages on your browser

October 10, 2019   Self-Service BI

This question comes up pretty regularly, I have a big screen in my hallway and I want to show some Power BI reports that rotate. Now there is some build in functionality in the Windows 10 App for Power BI for it that you can check out here. But there have been some cases where this doesn’t work, like for example if you want your report to run outside of your domain using B2B or when using Power BI embedded that both cannot load the report in the Win 10 app.

Turns out there is a pretty simple solution to do this using a simple Chrome extension called Tab Rotate that will do the trick. Once you install it you can configure which pages it needs to load like this:

You can just copy and paste the page you are on from Power BI, every report page has it’s own URL so that works great. Also make sure to add “?chromeless=1” the URL to make sure it opens the report fully without Power BI navigation.

Now it will rotate between all the tabs and also refreshed the pages each time.

The extension also allows you to create a single JSON file that you can place somewhere on the web if you want to run this on many TV’s and manage all links in a single place.

There are plenty of other ways to do this that I found but this one was one of the easiest and free.

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