Month: October 2020

Article originally posted on Data Science Central. Visit Data Science Central

• Conjunctions and disjunctions are useful tools for building algorithms. 
• They enable you to combine propositions. 
• Truth tables are a fast way to find solutions. 
• Analogies can help you to remember the results. 

Dive into machine learning, and you’ll come across algorithms that include conjunctions and disjunctions. For example, you might come across a set of conjunctive rules in a hypothesis space (the set of all functions a model can return) or create a learning algorithm that builds a conjunction using similar features.

Conjunctions and Disjunctions are one way to combine propositions into more complex ones. Propositions [noterm] are statements that are either true or false. For example, “2 is greater than 3” or “10 + 10 = 21.” Some statements like “He is a great swimmer” or “How are you today” don’t have true or false answers and so aren’t propositions. Once you have a set of propositions, you can combine them in various ways, including:

• Conjunction (and, &, ∧):  combine (add) propositions. 
• Disjunction (or):  choose (select) propositions. 

Many outcomes for these two simple statements are possible.  You can quickly find solutions using truth tables.

Truth Table for Conjunctions

In order to account all the possible combinations of truth values for two statements p and q, we can create a four-row truth table:

A key fact from the table: a conjunction is true only if all the variables in it are true. If you’re familiar with the simple model theory of eye color (where Brown B is dominant over blue b) [1], one way to make sense of the table results is just to remember a single fact: “False” is dominant over “true”. In order for true to appear, it must be paired (t, t). But if False shows in any position, it dominates truth; It will result in an F even when paired with true (F, t) or (t, F). 

What happens when the “Basket” is empty?

In that case, an empty conjunction is always defined as true. [2] The eye color analogy obviously doesn’t work here, but an old saying does work:

One bad apple spoils the bunch. 

Imagine you have a basket that you’re going to fill with varieties of good (true) and bad (False) apples. If you have a single false statement, everything in the basket is tainted (i.e. False). But if your basket is empty (a.k.a. the empty truth table), then it  hasn’t been filled with apples yet . You have no reason to assume that your basket is going to be filled with good apples. Unless you’re very pessimistic, in which case perhaps data science isn’t the career for you!

Truth Table for Disjunctions

Similarly, a truth table can find solutions for disjunctions. The format is the same but the results are slightly different:

Here’s my analogy for remembering the results here. It’s similar to the “bunch of apples” analogy except here we are given a choice: apple P or apple Q. So, given that basket of apples where some are rotten, which would you choose? Every time, you would choose the good (a.k.a. true) apple. In the last row, you have two bad apples so you have no choice but to pick one of those. 

The empty disjunction is defined as false. Back to our basket analogy, the “OR” here is you being forced to choose between good apples OR bad apples that are already in the basket. The basket is empty when everyone has chosen their apples. You’re left with a basket that has, unfortunately, mold in it from those bad apples. So you’re left with bad (False) residue.

How to Use a Truth Table: An Example

Let’s take two statements:

P: There are 99 cents in $1

Q: The dollar ($) is US currency.

We want to know: what is the conjunction of P and Q?

Solution:

Step 1. Construct a truth table. This is an “and” question, so create a conjunction truth table. 

Step 2: Determine whether the statements are true or false.  For this example, P is False and Q is true. 

Step 3: Refer to the line that reflects whether the statements are true or False. The third line (F, t = F) is the correct solution.

References

Table pictures by author.

[1] Eye Color and its Inheritance

[2] Foundations of Machine Learning

Article originally posted on InfoQ. Visit InfoQ

As organisations make remote working more and more the norm, the employee onboarding experience needs to change to engage new people with their colleagues and the organisation effectively. The onboarding experience needs to be designed to engage the new employee and actively make them feel welcome and a part of the team.

By Shane Hastie

Article originally posted on InfoQ. Visit InfoQ

Bytecode Alliance laid out a concrete vision for wasm-on-the-server. At the same time, the Wasm open-source community is now far larger than the corporations in Bytecode Alliance. There are multiple Wasm VM implementations, complier toolchains for programming languages, as well as host Operating Systems and environments (e.g., Node.js, Deno, or blockchains ).

By Vivian Hu

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When making estimations using story points didn’t feel helpful, a team decided to experiment with #NoEstimates. Breaking down stories into smaller tasks gives them insight into their velocity and has made them more predictable. It also helps them to spend less time on process and have more time available for delivering value.

By Ben Linders

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Life is full of obstacles, there’s bound to be a few blockers along the way. In this article, Carly Richmond ponders the successes and challenges of adopting their first Obstacle Board. She will discuss how they integrated the board into their practice, and provide some useful tips on how you can apply the lessons they learned into your own implementation.

By Carly Richmond

Article originally posted on InfoQ. Visit InfoQ

Recently, AWS announced budgets actions allowing customers to define actions to take when a budget exceeds its threshold (actual or forecasted amounts). With budget actions, customers will have more control over their AWS Budgets in order to reduce unintentional overspending in their AWS accounts.

By Steef-Jan Wiggers

Article originally posted on Data Science Central. Visit Data Science Central

We are living in a data-driven and data-centric world that’s only going to produce more and more data with time. This new data is collected in an unstructured manner and processed into a structured form as per the requirement of a company. Later, meaningful insights are extracted from this data for decision-making purposes. All this process helps a company to grow in this competitive market. But, who is in charge of all this process? Everything is done by a Data Analytics professional or a Data Scientist.

How is Data Analytics Benefitting Businesses?

Data analytics are driving the growth of businesses from the beginning of a startup, promoting robust growth, and turning them into large, formidable companies. Rutgers highlights the importance of data analytics, noting that “Tech professionals at big organizations like Google, Facebook, and Tesla, have leveraged the potential of data analytics to expand into new markets, build strategies and strong relations with customers, and optimize processes such as marketing management and supply chain.”

Big data and data analytics are two buzz words of this year and are too costly to be overlooked by any business. Companies generate large volumes of data every day, and to process that data is to understand the current state of a business and its future goals. If a company wants to break into new markets, tracking data analytics will provide ease and insight for it to grow in every aspect.

The top 5 data analytics companies globally are briefly discussed below, with an emphasis on their recent developments and acquisitions:

1. SAP – German Based Company

SAP SE is a German multinational software company specializing in enterprise software.  Since its establishment in 1972 by ex-IBM employees, the company grew in light of its enterprise resource planning (ERP) software. Lately, the company has been breaking into new markets, embracing new technologies, with its most recent SAP S/4HANA ERP platform showing cloud capabilities. SAP’s data analytics solutions comprise sales, human resources, marketing, finance, and operations. The company reported its 2018 revenue as USD 26.73 billion.

In 2018, SAP acquired an experience management company Qualtrics that also specializes in data analytics and subscription software solutions. The acquisition is helping SAP break into new sectors and supplement its data analytics business. In 2016, the company acquired data analytics company Altiscale that provides high-performance Big Data-As-A-Service (BDaaS) solutions and other operational services. This is considered a significant acquisition for SAP since it enables the company and its customers to make broader use of big data across data platforms, technologies, and applications.

2. Salesforce – U.S. Based Company

Salesforce is a U.S.-based multinational tech company specializing in cloud software for enterprise solutions, particularly customer relationship management (CRM). The company’s CRM solution, Salesforce Customer 360, is used by some of the most renowned companies in the world, such as T-Mobile and Unilever. Salesforce’s analysis offering is known as Einstein Analytics, which is a set of AI-powered advanced analytics that allows customers to explore all the data quickly and easily. The platform employs AI to augment analytics and make predictions for the future.

In August 2019, Salesforce completed the acquisition of data analytics platform Tableau for a staggering USD 15.7 billion, allowing both the companies to better serve their customers, and also deliver powerful AI-driven insights across all types of data and use cases. The company reported its 2019 revenue as USD 13.28 billion, employing more than 35,000 individuals worldwide.

3. Tableau – U.S. Based Company

A subsidiary of Salesforce, the U.S.-based Tableau has the reputation of being one of the best data visualization and analytics companies in the world that can make data more meaningful. The latest version of the software, Tableau 2020.1, has the capabilities of showcasing data on vector maps, which are mainly scalable. In FY2019, Tableau reported its revenues as amounting to USD 902.0 million, marking a 41% increase over the previous year.

The U.S.-based multinational technology company develops, manufactures, licenses, supports, and sells software, electronics, PCs, and related services. Valued over USD 1 trillion and reporting over USD 125 billion in revenues in FY2019, the company’s portfolio includes a wide range of products, including the data analytics platform, Power BI. The platform — used by companies like Adobe, HP, and Rolls-Royce — allows for the visualization of data with an emphasis on collaboration and speed of analysis.

Over the years, Microsoft has acquired several small- and large-scale companies, including its 2015 acquisition of open-source analytics company Revolution Analytics, to support its analytics services. In July 2019, the company acquired a startup named BlueTalon that will enhance Microsoft’s abilities to empower businesses across industries to transform while ensuring the right use of data digitally.

5. Qlik – Swedish Software Company

Qlik is a Swedish software company that provides an end-to-end platform with data analytics, data integration, business intelligence (BI), and conversational analytics capabilities. The company’s QlikView analytics software offers a faster, end-to-end data integration and analytics solutions, and the expertise needed to build a data-driven enterprise.

Posted on mongodb google news. Visit mongodb google news

… Insight Analysis, Strategic Assessment, Top Players – Oracle, RavenDB, EnterpriseOB, DataStax, ArangoDB, MongoDB, IBM, Couchbase, Microsoft, …

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Article originally posted on InfoQ. Visit InfoQ

Rich Harris, the creator of Svelte, lifted the curtain over the experiments that have been taking place around Svelte (the UI framework and compiler) and Sapper (Svelte’s application framework). Harris gave a glimpse of a potential future in which Svelte is a serverless-first framework.

By Bruno Couriol

Article originally posted on Data Science Central. Visit Data Science Central

Up until recently, we accepted the “black box” narrative surrounding AI as a necessary evil that could not be extrapolated away from AI as a concept. We understood that tradeoffs were sometimes necessary to achieve performance accuracy at the expense of transparency and explainability. Fortunately, there have been advancements in the last few years that make it technologically feasible to explain why AI models reach decisions, which represents an inflection point for the future of this transformative technology.

In order for AI to become mainstream, we must have transparency and insight into how AI-enabled decisions were reached—meaning we should be able pinpoint what factors contributed to a decision.

Today’s approach is one full of risk as organizations rely on trusting already overburdened data scientists to document and explain their work. This approach does not lend itself to adapt to the scale required for AI at enterprise scale and requires a lot of extra time wasted on explanation. Instead, organizations should look to adopt tools that allow for auditability to track AI behavior and usage over time within an organization.

ModelOps tools are emerging as a new way to monitor and manage AI models’ performance while providing the ability to look under the hood and audit AI usage within an enterprise. AI auditability is a crucial step to move past the current inhibitors and into widespread AI adoption—allowing organizations to lay the foundation for trustworthy AI.

Auditability for AI Systems

Auditability and compliance tend to conjure up painful sentiments associated with documentation and governance. However for AI systems, the stakes are much higher. Auditing AI is not much different than auditing any other emerging technology, e.g., cloud computing, cybersecurity, except AI has the potential to disproportionally impact already marginalized groups due to inherent bias in datasets that can be amplified (not to add pressure).

We’ve already seen the current approach play out time and yield perilous impacts on peoples’ lives, inviting a new approach to protect and ensure the integrity of AI-enabled decision-making. Fortunately, by auditing AI systems’ behavior, we can proactively mitigate and prevent some of these negative impacts.

Transparency is key to both end user and stakeholders’ ability to understand and trust AI, and auditability is one component of transparency. For a data scientist to know that AI is behaving as expected, they must be able to access performance metrics, easily identify when models have deviated from expected results, and have recommendations to be able to course correct. On the same token, key leaders involved in AI governance must also be able to understand how and where AI is being used across the enterprise including answering questions around who did what, when. Ultimately, monitoring AI systems via solutions that have ModelOps functionality built in provides organizations the ability to do this both in real-time. And, a historical view around AI performance and usage across an enterprise.

Another component of auditability rests on ensuring that AI is trustworthy, reliable, robust, and can be validated and verified by all stakeholders. Today’s ModelOps tools allow insight into model performance in real-time, as well as automated logging and tracking of individual users’ behavior within the system. Gone are the days where data scientists must dig around to reproduce results or show their work. These tools automate and track this information for AI, with simple dashboards for stakeholders to see performance metrics tailored to what matters most to them. This means that stakeholders, e.g., business leaders, IT leaders, auditors, security leaders, need information in a digestible way that aligns to their unique roles. Regardless of role or the information need, AI auditability provides the means to a holistic understanding of AI usage across an enterprise.

Auditability and Trustworthy AI

While AI auditability is not a novel or even new concept, it has the potential to catapult adoption by enabling transparent, trustworthy AI. Coupled with advances in AI model explainability, auditability offers a window into an organization’s AI health.

Now is the time to create a strong foundation for long term AI success by documenting and monitoring AI usage and performance. Embracing AI audibility will unlock greater value which leads to increased AI adoption with reduced risks.