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Tag Archives: using

Create and Update Contacts (Child) using an Embedded Power Apps Sub-grid

April 6, 2021   Microsoft Dynamics CRM

One of the features that we are quite excited about in Customer Engagement is the embedding of the Canvas Power Apps in Dynamics 365. Many say this was the replacement of the Dialogs, but it does more than that. With the popular demand of working with Power Apps it would be great to have the ability to create/update the records via the same. The scenario we would be seeing is to fetch all the…

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how to draw a circle using disks, the radii of the disks are 1, while the radius of the circle is √2 + √6

February 27, 2021   BI News and Info

 how to draw a circle using disks, the radii of the disks are 1, while the radius of the circle is √2 + √6

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Tips on using Advanced Find in Microsoft Dynamics 365

February 26, 2021   CRM News and Info

A lot of small and midsize companies have implemented Microsoft Dynamics 365. The tool is scalable, flexible, automate more than half of the daily tasks, makes the sales pipeline visible, the customer journey shorter, and whatnot. And as I have spent quite a few years in the Dynamics 365 consulting field, I have noticed that one of the most commonly used features in Dynamics CRM is Advanced Find. A developer, administrator, or end-user uses it for various purposes.

Tip # 1

For an entity, if there is a frequently used query, we can set that query up as the default query. This will help the users save some time by having the query configured beforehand so that they would not have to build it manually. Before configuring a default query, when you open the Advanced Find it will look like this:

1 7 625x92 Tips on using Advanced Find in Microsoft Dynamics 365

Now let’s say there is a frequently used query for the accounts entity, which we need to set as default. The query is My Accounts which has incomplete information (the field for an email address or phone number is left blank).

To configure this, go to Settings/Customizations/Customize the System. Open the views in the Account Entity. Double click on the view with the “Advanced Find View” type.

2 8 625x212 Tips on using Advanced Find in Microsoft Dynamics 365

Configure the filter criteria as per your requirement. In this case, the filter criteria is:

3 8 625x418 Tips on using Advanced Find in Microsoft Dynamics 365

Once the filter criteria is configured, click on Ok. Then Save and Close and after that, Publish All Customizations.

4 6 625x273 Tips on using Advanced Find in Microsoft Dynamics 365

Now if you open advanced find and look for Accounts, it will look like the following:

5 6 625x252 Tips on using Advanced Find in Microsoft Dynamics 365

Tip # 2:

If you are trying to find the results of a saved query, by default you will not be able to edit the query. That is, if you want to add some extra filter criteria/remove filter criteria you will not be able to do it.

6 5 625x134 Tips on using Advanced Find in Microsoft Dynamics 365

If you want to edit the query, you can just click on the details. That will help you edit the query. After clicking it will look like this:

7 5 625x175 Tips on using Advanced Find in Microsoft Dynamics 365

From the image, you can see that it is editable.

If you want your saved Advanced Find View or any other saved view to show up as editable by default (so that you don’t need to click on the Details option to make it editable) then you can change it in your Personal Settings, as shown below:

8 4 625x134 Tips on using Advanced Find in Microsoft Dynamics 365

Set the “Default mode in Advanced Find” as “Detailed”. Click Ok.

9 4 625x448 Tips on using Advanced Find in Microsoft Dynamics 365

If you set it to the “Simple” mode, it will be non-editable.

Dynamics 365 isn’t just an application anymore. It has for a lot of companies, be it large corporates or SMBs has become a part of their business strategy. If you would like to know more about how we are helping businesses transform, do connect with us.

Talking about ‘finds’, the Quick search functionality in Dynamics 365 usually shows all Active fields instead of the one you select. This can be a little annoying. Our blog provides a solution to this issue.

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How to functionally create N*n lists of ordered pairs using Outer (or similar) where n is dynamic / recursive?

February 22, 2021   BI News and Info

 How to functionally create N*n lists of ordered pairs using Outer (or similar) where n is dynamic / recursive?

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Using Sales and Purchase Journals in D365 Business Central

February 17, 2021   Microsoft Dynamics CRM

When speaking with Business Central users, we find that one of the underused tools in the software are sales and purchase journals. These are a way to create quick purchase or sales documents without having to go through the field-by-field process you follow when creating them manually. Let’s explore a couple examples of when this can be useful. In the first example, let’s say that a customer has…

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Using Sales and Purchase Journals in D365 Business Central

February 16, 2021   Microsoft Dynamics CRM

When speaking with Business Central users, we find that one of the underused tools in the software are sales and purchase journals. These are a way to create quick purchase or sales documents without having to go through the field-by-field process you follow when creating them manually. Let’s explore a couple examples of when this can be useful. In the first example, let’s say that a customer has…

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Using Sales and Purchase Journals in D365 Business Central

February 15, 2021   Microsoft Dynamics CRM

When speaking with Business Central users, we find that one of the underused tools in the software are sales and purchase journals. These are a way to create quick purchase or sales documents without having to go through the field-by-field process you follow when creating them manually. Let’s explore a couple examples of when this can be useful. In the first example, let’s say that a customer has…

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AI progress depends on us using less data, not more

February 14, 2021   Big Data
 AI progress depends on us using less data, not more

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In the data science community, we’re witnessing the beginnings of an infodemic — where more data becomes a liability rather than an asset. We’re continuously moving towards ever more data-hungry and more computationally expensive state-of-the-art AI models. And that is going to result in some detrimental and perhaps counter-intuitive side-effects (I’ll get to those shortly).

To avoid serious downsides, the data science community has to start working with some self-imposed constraints: specifically, more limited data and compute resources.

A minimal-data practice will enable several AI-driven industries — including cyber security, which is my own area of focus — to become more efficient, accessible, independent, and disruptive.

When data becomes a curse rather than a blessing

Before we go any further, let me explain the problem with our reliance of increasingly data-hungry AI algorithms. In simplistic terms, AI-powered models are “learning” without being explicitly programed to do so, through a trial and error process that relies on an amassed slate of samples. The more data points you have – even if many of them seem indistinguishable to the naked eye, the more accurate and robust AI-powered models you should get, in theory.

In search of higher accuracy and low false-positive rates, industries like cyber security — which was once optimistic about its ability to leverage the unprecedented amount of data that followed from enterprise digital transformation — are now encountering a whole new set of challenges:

1. AI has a compute addiction. The growing fear is that new advancements in experimental AI research, which frequently require formidable datasets supported by an appropriate compute infrastructure, might be stemmed due to compute and memory constraints, not to mention the financial and environmental costs of higher compute needs.

While we may reach several more AI milestones with this data-heavy approach, over time, we’ll see progress slow. The data science community’s tendency to aim for data-“insatiable” and compute-draining state-of-the-art models in certain domains (e.g. the NLP domain and its dominant large-scale language models) should serve as a warning sign. OpenAI analyses suggest that the data science community is more efficient at achieving goals that have already been obtained but demonstrate that it requires more compute, by a few orders of magnitude, to reach new dramatic AI achievements. MIT researchers estimated that “three years of algorithmic improvement is equivalent to a 10 times increase in computing power.” Furthermore, creating an adequate AI model that will withstand concept-drifts over time and overcome “underspecification” usually requires multiple rounds of training and tuning, which means even more compute resources.

If pushing the AI envelope means consuming even more specialized resources at greater costs, then, yes, the leading tech giants will keep paying the price to stay in the lead, but most academic institutions would find it difficult to take part in this “high risk – high reward” competition. These institutions will most likely either embrace resource-efficient technologies or persue adjacent fields of research. The significant compute barrier might have an unwarranted cooling effect on academic researchers themselves, who might choose to self-restrain or completely refrain from persuing revolutionary AI-powered advancements.

2. Big data can mean more spurious noise. Even if you assume you have properly defined and designed an AI model’s objective and architecture and that you have gleaned, curated, and adequately prepared enough relevant data, you have no assurance the model will yield beneficial and actionable results. During the training process, as additional data points are consumed, the model might still identify misleading spurious correlations between different variables. These variables might be associated in what seems to be a statistically significant manner, but are not causally related and so don’t serve as useful indicators for prediction purposes.

I see this in the cyber security field: The industry feels compelled to take as many features as possible into account, in the hope of generating better detection and discovery mechanisms, security baselines, and authentication processes, but spurious correlations can overshadow the hidden correlations that actually matter.

3. We’re still only making linear progress. The fact that large-scale data-hungry models perform very well under specific circumstances, by mimicking human-generated content or surpassing some human detection and recognition capabilities, might be misleading. It might obstruct data practitioners from realizing that some of the current efforts in applicative AI research are only extending existing AI-based capabilities in a linear progression rather than producing real leapfrog advancements — in the way organizations secure their systems and networks, for example.

Unsupervised deep learning models fed on large datasets have yielded remarkable results over the years — especially through transfer learning and generative adversarial networks (GANs). But even in light of progress in neuro-symbolic AI research, AI-powered models are still far from demonstrating human-like intuition, imagination, top-down reasoning, or artificial general intelligence (AGI) that could be applied broadly and effectively on fundamentally different problems — such as varying, unscripted, and evolving security tasks while facing dynamic and sophisticated adversaries.

4. Privacy concerns are expanding. Last but not least, collecting, storing, and using extensive volumes of data (including user-generated data) — which is especially valid for cyber security applications — raises a plethora of privacy, legal, and regulatory concerns and considerations. Arguments that cyber security-related data points don’t carry or constitute personally identifiable information (PII) are being refuted these days, as the strong binding between personal identities and digital attributes are extending the legal definition PII to include, for example, even an IP address.

How I learned to stop worrying and enjoy data scarcity

In order to overcome these challenges, specifically in my area, cyber security, we have to, first and foremost, align expectations.

The unexpected emergence of Covid-19 has underscored the difficulty of AI models to effectively adapt to unseen, and perhaps unforeseeable, circumstances and edge-cases (such as a global transition to remote work), especially in cyberspace where many datasets are naturally anomalous or characterized by high variance. The pandemic only underscored the importance of clearly and precisely articulating a model’s objective and adequately preparing its training data. These tasks are usually as important and labor-intensive as accumulating additional samples or even choosing and honing the model’s architecture.

These days, the cyber security industry is required to go through yet another recalibration phase as it comes to terms with its inability to cope with the “data overdose,” or infodemic, that has been plaguing the cyber realm. The following approaches can serve as guiding principles to accelerate this recalibration process, and they’re valid for other areas of AI, too, not just cyber security:

Algorithmic efficacy as top priority. Taking stock of the plateauing Moore’s law, companies and AI researchers are working to ramp-up algorithmic efficacy by testing innovative methods and technologies, some of which are still in a nascent stage of deployment. These approaches, which are currently applicable only to specific tasks, range from the application of Switch Transformers, to the refinement of Few Shots, One-Shot, and Less-Than-One-Shot Learning methods.

Human augmentation-first approach. By limiting AI models to only augment the security professional’s workflows and allowing human and artificial intelligence to work in tandem, these models could be applied to very narrow, well-defined security applications, which by their nature require less training data. These AI guardrails could be manifested in terms of human intervention or by incorporating rule-based algorithms that hard-code human judgment. It is no coincidence that a growing number of security vendors favor offering AI-driven solutions that only augment the human-in-the-loop, instead of replacing human judgment all together.

Regulators could also look favorably on this approach, since they look for human accountability, oversight, and fail-safe mechanisms, especially when it comes to automated, complex, and “black box” processes. Some vendors are trying to find middle ground by introducing active learning or reinforcement learning methodologies, which leverage human input and expertise to enrich the underlining models themselves. In parallel, researchers are working on enhancing and refining human-machine interaction by teaching AI models when to defer a decision to human experts.

Leveraging hardware improvements. It’s not yet clear whether dedicated, highly optimized chip architectures and processors alongside new programming technologies and frameworks, or even completely different computerized systems, would be able to accommodate the ever-growing AI computation demand. Tailor-made for AI applications, some of these new technological foundations that closely bind and align specialized hardware and software, are more capable than ever of performing unimaginable volumes of parallel computations, matrix multiplications, and graph processing.

Additionally, purpose-built cloud instances for AI computation, federated learning schemes, and frontier technologies (neuromorphic chips, quantum computing, etc.) might also play a key role this effort. In any case, these advancements alone are not likely to curb the need for algorithmic optimization that might “outpace gains from hardware efficiency.” Still, they could prove to be critical, as the ongoing semiconductor battle for AI dominance has yet to produce a clear winner.

The merits of data discipline

Up to now, conventional wisdom in data science has usually dictated that when it comes to data, the more you have, the better. But we’re now beginning to see that the downsides of data-hungry AI models might, over time, outweigh their undisputed advantages.

Enterprises, cyber security vendors, and other data practitioners have multiple incentives to be more disciplined in the way they collect, store, and consume data. As I’ve illustrated here, one incentive that should be top of mind is the ability to elevate the accuracy and sensitivity of AI models while alleviating privacy concerns. Organizations that embrace this approach, which relies on data dearth rather than data abundance, and exercise self-restraint, may be better equipped to drive more actionable and cost-effective AI-driven innovation over the long haul.

Eyal Balicer is Senior Vice President for Global Cyber Partnership and Product Innovation at Citi.

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Using Sales and Purchase Journals in D365 Business Central

February 13, 2021   Microsoft Dynamics CRM

When speaking with Business Central users, we find that one of the underused tools in the software are sales and purchase journals. These are a way to create quick purchase or sales documents without having to go through the field-by-field process you follow when creating them manually. Let’s explore a couple examples of when this can be useful. In the first example, let’s say that a customer has…

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Using Sales and Purchase Journals in D365 Business Central

February 12, 2021   Microsoft Dynamics CRM

When speaking with Business Central users, we find that one of the underused tools in the software are sales and purchase journals. These are a way to create quick purchase or sales documents without having to go through the field-by-field process you follow when creating them manually. Let’s explore a couple examples of when this can be useful. In the first example, let’s say that a customer has…

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