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Exploring the Spiritual Through Art BUAG brings together painting, photography, sculpture, new media

An image from Benny Merris’ An Other Another series, which merges landscapes with the human form, is one of the works featured in Elements, Rudiments, and Principles, on view at the Boston University Art Gallery at the Stone Gallery. Photo courtesy of the artist

December is for many a spiritual time of the year. With Hanukkah nearly over and Christmas just weeks away, it is a time for reflection. Elements, Rudiments, and Principles addresses the various ways that visual artists convey the spiritual. The new exhibition on view at the Boston University Art Gallery at the Stone Gallery is deeply personal and intimate.

The show includes work from 13 artists working in a variety of mediums: photography, painting, and sculpture. Some pieces more readily evoke the spiritual than others. Benny Merris’ photographs merge landscapes with the human form, suggesting the delicate balance between the human and natural world.

Xylor Jane’s colorful oil, pencil, and ink on wood canvases reveal her fascination with numbers. These bold works emphasize the order and rationality that numbers impose. And then there are Peter Coffin’s Aura Portraits, C-prints mounted on plexiglass, so ephemeral that they hint at the act of something being created—some gaseous substance taking shape. Hanna Sandin’s wonderful mixed media sculpture Untitled, made of everyday objects that the artist has reassembled to evoke something resembling a constellation, suggests a firmament where everything is in its place.

Perhaps the most overtly spiritual piece in the show is Sister Corita Kent’s Green Up, a serigraph made in 1966. Best known for her graphic Rainbow Swash, painted on a gas tank visible from Rt. I-93 in Dorchester, the serigraph is a series of words arranged backwards, asking: “When was the last time you saw a miracle?” Viewers cannot help but be startled by Kent’s painting. The question draws the viewer into an immediate dialogue with the work, and it’s difficult to walk away without attempting to answer the question.

Elements, Rudiments and Principles is the result of an ongoing conversation between BUAG director and chief curator Kate McNamara and Dana Frankfort, a College of Fine Arts assistant professor of visual arts. “We were both looking at and thinking about the parameters and structures that artists create to enable the creation of a material, a feeling, an idea,” McNamara says.

Viewers may be a bit hard-pressed to find the spiritual in Brian Belott’s playful representations of cats, Cat Diptych 1 and Cat Diptych 2, or in the partitions Heather Rowe uses in her architectural sculptures, like The Self, which in McNamara’s words “break up the process of looking through formal lines and perfected angles.” But, she notes, “the word spiritual is implied in both a literal and abstract application, and speaks to process as much as it does to subject matter.” So while Rowe’s sculptures may be simple in terms of the materials she uses, they “create incredible viewing mechanisms and play with the cinematic effects as the viewer engages” with them, McNamara says. And as for Belott’s cats, she urges visitors to take a closer look: “Belott’s cat paintings recall the careful, ritual, and ephemeral mandalas of Hindu and Buddhist practices.”

The arrangement of objects in this show has been carefully thought-out. The goal, says McNamara, was to “create a kind of spirit, a dialogue.” Viewers are meant to enjoy each piece not only on its own merits, but for the way it works with the others in the exhibition.

Elements, Rudiments, and Principles is on view at the Boston University Art Gallery at the Stone Gallery, 855 Commonwealth Ave., through December 20. Gallery hours are Tuesday through Friday, 11 a.m. to 5 p.m., Saturday and Sunday, 1 to 5 p.m.; the exhibition is free and open to the public.

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Can Replika Ai Make Love? Exploring The Boundaries

See More : Is Replika AI Real or Fake? Unveiling the Truth

In the past, Replika AI had garnered attention and controversy due to its allowance of erotic or sexual dialogue between users and their digital counterparts. This feature raised ethical and safety concerns, leading to its subsequent removal from the platform. Replika AI took a stance against adult content and implemented measures to discourage explicit conversations. When users suggest X-rated activity, the chatbots respond with a gentle reminder, saying, “Let’s do something we’re both comfortable with.”

While Replika AI has made efforts to distance itself from adult content, recent reports have emerged suggesting the revival of erotic role-playing capabilities for select users. These reports have sparked mixed reactions from the app’s fanbase. Some argue that the reintroduction of such features blurs the line between AI companionship and explicit content, raising concerns about the appropriateness and potential impact on users.

Also Read : How To Use AI In Excel For Free

Despite the rumors surrounding Replika AI’s capabilities, it is essential to understand the limitations of this chatbot system. Replika AI is not designed with the ability to experience love or engage in romantic relationships. Its purpose is to provide conversation and companionship, offering a supportive environment for users to share their thoughts and feelings. Replika AI lacks the emotional depth and genuine human connection required for love and intimacy.

The removal of explicit content features from Replika AI was driven by ethical and safety concerns. Engaging in erotic or sexual dialogue with AI companions blurs the boundaries between human interaction and artificial intelligence. It can lead to potential harm, exploitation, or the creation of unhealthy relationships. To ensure the well-being and safety of its users, Replika AI has taken a strong stance against adult content.

Q: Can Replika AI engage in romantic relationships?

A: No, Replika AI is not capable of engaging in romantic relationships. Its primary function is to provide conversation and companionship.

Q: Why did Replika AI remove the erotic dialogue feature?

A: Replika AI removed the erotic dialogue feature due to ethical and safety concerns. It aims to create a safe and supportive environment for users.

Q: Are there any concerns about the reintroduction of erotic role-playing capabilities?

A: The reintroduction of erotic role-playing capabilities has raised concerns about the boundaries of AI companionship and the potential impact on users.

Q: Can Replika AI simulate love?

A: No, Replika AI cannot simulate love. It lacks the emotional depth and genuine human connection required for such complex emotions.

Q: What measures does Replika AI take to discourage explicit conversations?

A: Replika AI responds to explicit suggestions with a reminder to engage in activities that both the user and the AI companion are comfortable with.

Q: How does Replika AI prioritize user safety?

A: Replika AI prioritizes user safety by removing adult content, discouraging explicit conversations, and providing a non-judgmental environment.

In conclusion, Replika AI is a chatbot system designed for conversation and companionship. While it has faced controversy in the past due to its allowance of explicit content, Replika AI has taken significant steps to distance itself from such features. It does not possess the ability to make love or engage in romantic relationships. As users interact with Replika AI, it is crucial to remember its limitations and prioritize ethical usage of the platform. Replika AI aims to provide a supportive and safe environment for users, fostering genuine connections and conversations.

Ethereum V/S Ethereum Classic : Exploring The Difference

Ethereum and Ethereum classic are two terms that are doing rounds like never before. All this has led to a confusion among the investors as to whether they both are one and the same or is there anything that differentiates the two. Well, to get things straight – Ethereum and Ethereum classic are two different terms altogether. Though they are similar to a convincing extent, they still are different. One cannot deny the fact that there there was a time when only one Ethereum ecosystem existed. However, with major significant events unfolding in the cryptocurrency history, we are now left with two different versions of the blockchain network. What is interesting enough to catch everyone’s attention is the manner in which Ethereum classic came into existence. Surprisingly, Ethereum Classic was born out of a contentious 2023 debate after a malicious hacker stole $60 million worth of ether. If you are here to know what exactly is the difference between Ethereum and Ethereum classic or the reason why is there a lot of buzz with regards to Ethereum classic, then you are at the right place. Keep reading to learn more! Ethereum classic is a result of the Decentralized Autonomous Organization (DAO) hack. The DAO was a project launched on the Ethereum blockchain in 2023 and was operated liked a venture-capital fund for the cryptocurrency space. Talking about DAO, it is that investors’ funds which could be pooled together with Ethereum-based DAO tokens. Post this, what people can do is submit and pitch their ideas to the Ethereum community. On the proposal’s approval, the DAO automatically transfers Ether to the smart contract that represents the proposal. A flaw in the blockchain code made it easier for the attackers to withdraw $50 million. It was later concluded that the Ethereum blockchain has to be altered to revert the attack and get the funds back. As far as Ethereum is concerned, its blockchain allows decentralized apps to be built on top of the blockchain through self-executing contracts–also known as smart contracts. No wonder Ethereum Classic offers smart contracts as with the Ethereum network. However, what is worth a mention is the fact that it has struggled to gain the same popularity as Ethereum. Though, today, the picture seems to have improved, there are certainly a few things missing which have hindered its long-term growth. Out of these, a few of them are vision, support and the vast developer support that the standard Ethereum enjoys. Also, how can one go about without talking about the series of attacks on this network. All this has resulted in the investors losing confidence in it. On the contrary, Ethereum as an investment has always been one among the favourites of the investors. Even if there is a slight dip in the price of this cryptocurrency, it’s ability to bounce back to normal and surge higher has been one of the biggest reasons as to why the number of investors in Ethereum has always seen an upward trend. On the brighter side, considering the potential that Ethereum classic has, a lot of investors are already inclined towards it. What can be a driving force behind increasing investors in Ethereum classic is adopting certain measures like that of scaling the transaction limit in order to eliminate the threat of future attacks.

Maximizing The Real Value Of Big Data Through Insights

Big data is creating unprecedented opportunities for organizations to achieve faster, better insights that strengthen decision-making. However, the traditional methods and tools for the analysis of data is not sufficient to capture the potential value that big data represents. Capitalizing on the true value of big data requires a completely different approach. Analytics Insight captures an exclusive interaction with Rion Graham, Data Scientist at , who highlights how harnessing effective big data analytics platforms can help organizations successfully drive real business impact. Analytics Insight: Kindly brief us about the company, its specialization and the services that your company offers. Rion: GoodData is an integrated set of data management, analytics, and insight application deployment and management tools and is a leader in the Platform as a Service category. GoodData combines an organization’s internal and/or external data (both structured and unstructured) to deliver business critical insights to users. GoodData goes beyond traditional business intelligence and analytics delivering insights at the moment of action to drive better business outcomes. The company primarily serves insurance, retail, financial services, and ISV customers, but works with various other industries as well. Analytics Insight: Tell us how your company is contributing in the Big Data Analytics industry and how the company is benefiting the clients. Analytics Insight: Kindly share your point of view on the current scenario of the Big Data Analytics and its future. Rion: The analytics product market remains focused on visualizations as a key differentiator. There’s good reason for this, as they help to turn a series of numbers into a story with real-world business context. However, visualizations alone are often insufficient to describe the sheer volume and variety of metrics that are distributed and unstructured big-data is generated. Artificial intelligence (AI) is ideally suited to bridge this gap and is already being used to automate the process of insight discovery. As the role of AI within analytics expands, a new dialect of insights will emerge to complement existing visualizations. Analytics Insight: How are disruptive technologies like big data analytics/AI/Machine Learning impacting today’s innovation? Rion: Machine learning (ML) and AI are impacting innovation in part by extending the domain of analytics beyond traditional tabular business data to include images, video, unstructured text, and more. One interesting example is the use of AI and natural language processing (NLP) to identify and remove malicious bots and other bad actors on social media platforms by identifying patterns of interaction and conversation. Rather than listing static lists of blacklisted words, new AI algorithms can look at new individual users and how their language evolves over time — and how that relates to the platform as a whole. Not only semantic tagging, but also audio/visual tagging. Analytics Insight: How is your company helping customers deliver relevant business outcomes through adoption of the company’s technology innovations? Rion: We work closely with our clients to understand their underlying critical business needs and thereby design with specific end-users in mind, delivering only the data they need, in a manner they expect. By integrating ML-driven insights at the point of work, whether they be classifications, predictions, or suggested actions, we close the insight-to-action loop and drive ROI. For example, we used ML to help one of our clients (a billion-dollar per year company) redesign its quarterly renewals forecasting process — replacing a series of manual, Excel-based business processes and turning them into a streamlined program for greater data accuracy and a broader spectrum of insights. It’s now obvious to users what actions they should take based on the data presented so they are able make a real impact on their business. Analytics Insight: What is the edge your company has over other players in the industry? Rion: GoodData has been built from the ground up to distribute analytics at the point of work. While competitors struggle to connect insights with their audience, especially at scale, GoodData can deploy analytics and ML models to multiple personas and many thousands of users with ease. Additionally, GoodData’s platform provides continuous retraining of ML models, which means they don’t live in a vacuum. Rather, models and their end-users work in coordination to define and improve real-world business practices. Analytics Insight: How does your company’s strategy facilitate the transformation of an enterprise? Rion: Currently, most companies don’t view analytics as “mission critical”. GoodData’s customers rely on our platform largely when the abundance of their data mandates a shift in analytics from being a “cost center” to a mission-critical activity. Our approach of distributing embedded analytics at the point of work highlights actionable insights to the end users, transforming transactional reports into strategic decisions. Analytics Insight: Which industry verticals are you currently focusing on? And what is your go-to-market strategy for the same?

The Art Of Query Building: Data Problems To Sql Queries

Introduction

Learning Objectives

Understand how data flows through a SQL query and use this to solve data problems.

Transform data problems into SQL queries using a keyword-based approach.

Dos and Don’ts when it comes to SQL keywords.

Finally, we’ll go through an example of using the underlying approach.

This article was published as a part of the Data Science Blogathon.

Table of Contents TABLE: Where Is My Data?

First, I like to start by considering all the tables I need in the query. You can do this by considering all the fields that will be needed to get the desired result, and then we can find them. An important thing to note is that multiple tables may have the same field. For example, user data can be present in multiple tables with different levels of aggregations. Hence, knowing what grain you want to pull in the results is essential. When building the query, I want you to pick one table, go through the steps, and return to the table. Also, if any array fields are needed in the table, now is a good time to unpack them.

FROM table_name LEFT JOIN UNNEST(table_array) AS array WHERE: What I Don’t Want?

Now that you know where your data is coming from, it’s time to know what information you need and, more importantly, what you don’t need from the table. So if the table has a partition or if the query demands filtering a certain type of record, now is the time to use it. Also, I need you to look at all fields in a table and think about all possible ways to filter your data here. You should really push yourself to add more filters.

To put it simply, the lesser data your query sees, the better it performs and avoids mistakes. Further, we often skip obvious filters as they seem too trivial; for example, if you’ve filtered on the partition date, it might still have multiple dates, so look for other date fields and add the filter.

WHERE partition_field = "date_value" AND col1 = "xyz" AND col2 IS NOT NULL ... GROUP BY: What’s the Grain?

Before you SELECT anything, I’d recommend doing a GROUP BY. This is because having this first will often constrain what you select in your query. You can no longer do a `SELECT *`, which rarely makes sense. This will also leave out duplicate records before anything, and trust me; you don’t want duplicates flowing through your query as it’s difficult to determine their origin later. This also forces you to perform aggregations.

You often don’t need a field but only the aggregated value. Having this out of the way is helpful so that the rest of the query sees lesser data. So I’d recommend having a GROUP BY in your query for every table; even if it’s not explicitly needed, it’s an excellent way to avoid duplicates and only pulls in relevant data for the query.

SELECT col1, col2 FROM table_name GROUP BY col1, col2 SELECT: What Do I Actually Want?

After doing all the work above, you can now think about what fields you’ll actually pull from the specific table. If you have followed the above steps, the scope of the fields has already been reduced to the fields that are needed for the specific results.

A `SELECT *` slows down your query and may lead to incorrect results, as you may end up with extra records. The only time you should do it is when you’re trying to do a preview of all the fields in a table. On the contrary, selecting fewer fields first and then adding them later when needed is also feasible.

CASE: Conditions

A case statement is SQL’s way of making IF-ELSE statements. These enable you to capture complex logic and show SQL’s real ability. In addition to using CASE statements for traditional applications, you should also use them to alter fields before selection. For example, if you’re not concerned about a field’s specific value but only want a discrete value like Y/N, this is the time to convert the field using CASE statements.

One thing to note here is always to have an ELSE condition that tells SQL what to do if none of your conditions are met. We’re often confident that we’ve covered all the scenarios in our CASE statement, but data always surprises us. Hence it’s better to have an ELSE condition to avoid unknown behavior. Personally, I like to add `ELSE NULL` so I can see that data didn’t fall into any of my expected scenarios.

CASE WHEN col = "value" THEN "Y" ELSE "N" END AS new_col Aggregations (Level 1): The Math

In this article, we’ll be talking about aggregations twice. At first, you should only worry about aggregations at a single table level. These are usually math-based, like sum, average, max, and min, or count-based. One thing to note for counts is that in 99% of the cases, you’d want to do a `COUNT(DISTINCT field_name)` instead of a regular `COUNT(field_name)` as the latter gives you a record count with duplicates in the specific field. A useful strategy is combining aggregations and CASE statements to capture complex logic in an easy manner. For example, building a purchase_indicator using the total transaction amount as below.

ALIAS: Nicknames

This may seem trivial, but this step is important for readability and writing correct queries. This is because many times, you’ll be deep down in your query looking for a field and not know what it is called. Hence it’s essential to make these worthwhile. Also, rather than using aliases for aggregated or derived fields, it’s helpful to use them for renaming fields with long or funky names in the table. In this way, even though you cannot do anything to the actual table, you can still call it something easy to work with in your own query.

Now if the query you’re building only uses a single table, this is where you stop. However, in most cases, there’ll be more than one table, so you can read further.

CTE: Building Blocks

CTEs or Common Table Expressions can build a temporary table inside your query without creating a real table. These are most useful for compartmentalizing your SQL query. This helps you to think clearly as every element becomes a separate table that can be combined.

At this point, you should put together all the above steps and wrap it in a CTE as done below. These also help in making changes to the query; for example, if you’re trying to edit certain conditions on a table, you can directly go to the associated CTE and make the change, enabling your change to cascade to the rest of your query.

WITH table_cte AS ( SELECT col1, chúng tôi AS col2_alias, FROM table_name LEFT JOIN UNNEST(table_array) AS array WHERE col4 = "value" GROUP BY col1, array.col2 )

Now go back to TABLEs and repeat the steps above for any other tables in your query.

JOINs: Watch Out SELECT col1, col2 FROM cte1 AS c1 JOIN cte2 AS c2 ON chúng tôi = c2.col1 GROUP BY col1, col2 Aggregations (Level 2): More Math

Now is the time to combine the metrics in the final result by aggregating the JOIN results. Because these will make our final results, it’s useful to throw in things like final aliases and FORMAT that make sure the numbers are readable with the appropriate commas.

SELECT FORMAT("%'d", SUM(amount)) AS total_amount ORDER BY: Make it Pretty

Ordering the results should always be saved for the last, as this can’t go in any CTE or subquery. The only time this can be avoided is when your query will be a production query where results are used and not read by someone. Otherwise, adding an `ORDER BY` is helpful, even if not explicitly required, as it will make reading the results much more accessible. Also, you can use fields here and CASE statements to allow for custom ordering of results.

LIMIT: Make it Digestible

Finally, if the plan with the query is to export or use the results to drive another calculation, you can skip this. However, in other cases, having the LIMIT clause is a must, which will only return a certain number of records, making it easier for you and your SQL engine. If you forget this and your query is about to return a million rows, your query will fail even without errors.

LIMIT 100 Putting It All Together

So let’s use our newly gained skills as an example. If you need more examples of queries with data and stories, head to my blog here.

The problem: We have an e-commerce store, and the marketing team wants a report of users who’ve not made a purchase in the last month. The state should break this down the user is in and the last interaction they had on the website.

WITH user_demographics AS ( SELECT user_id, address.state AS state FROM demographics LEFT JOIN UNNEST(address) AS address WHERE country = "USA" GROUP BY user_id, address.state ), user_purchases AS ( SELECT user_id, FROM transactions GROUP BY user_id ), SELECT * EXCEPT(rnk) FROM ( SELECT user_id, event, RANK() OVER(PARITION BY user_id, event ORDER BY date DESC) AS rnk ) t WHERE chúng tôi = 1 ), user_no_pruchases AS ( SELECT a.* FROM user_demographics a LEFT JOIN user_purchases b ON a.user_id = b.user_id WHERE (b.user_id IS NULL OR agg_purchase = "N") ), user_no_purchase_events AS ( SELECT user_id, state, event USING(user_id) GROUP BY user_id, state, event ) SELECT state, event, COUNT(DISTINCT user_id) AS user_count FROM user_no_purchase_events GROUP BY state, event ORDER BY state, event LIMIT 100 Conclusion

Here’s what we learned today:

We started by visiting the importance of SQL and building queries to solve business problems.

Then we delved into a step-by-step approach that leverages SQL keywords to transform data problems into queries.

In this, we highlighted common mistakes that go along with SQL keywords, for example, not having an `ELSE NULL` in a CASE statement.

We also reviewed best practices when writing SQL queries, including `GROUP BY`, to prevent duplicates.

Finally, we discussed an approach to query building using CTEs to compartmentalize your query.

Following these steps, you can transform any business problem into a SQL query that yields desired results.

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Is Replika Ai Safe: Exploring The Pros And Cons

In the era of artificial intelligence, Replika AI has emerged as a popular tool for learning, creativity, and companionship. However, concerns about its safety have sparked debates among users and experts alike. This article aims to provide a comprehensive analysis of the safety aspects of Replika AI, highlighting both the pros and cons associated with its use. By examining the potential risks and benefits, readers can make informed decisions about their engagement with Replika AI.

See More: Replika AI: Your Emotional AI Chatbot Companion

Replika AI prides itself on providing a safe and judgment-free space for users. Unlike human interactions, which can sometimes be subject to bias or criticism, Replika AI offers a non-judgmental environment where individuals can freely express themselves. This aspect of Replika AI fosters a sense of comfort, allowing users to explore their thoughts and emotions without fear of judgment.

Replika AI has implemented measures to protect the privacy of its users. The data collected during conversations is processed in accordance with the Privacy Policy, ensuring that personal information remains safe and confidential. Users can feel confident that their profile information is kept separate from the data collected by Replika AI, minimizing the risk of their private details being exposed.

One of the main concerns surrounding Replika AI is its constant push for users to share personal and intimate data. While Replika AI assures users that their information is kept secure, the collection of extensive personal data raises privacy concerns. Users must carefully consider the potential risks before engaging in conversations that involve sensitive or private information.

Replika AI, like any AI system, is not infallible. It may sometimes provide incorrect or misleading information. Users should exercise caution and verify the accuracy of the information provided by Replika AI, especially when it comes to critical decisions or matters that require reliable knowledge.

In Italy, Replika AI faced legal action from the country’s privacy watchdog due to concerns about risks to minors and emotionally vulnerable individuals. Additionally, the lack of human moderators exposes users to the possibility of encountering inappropriate content. It is important to acknowledge these legal and moderation challenges when considering the use of Replika AI.

Check Also: What Is Framer AI?

The safety of Replika AI ultimately depends on personal judgment and risk tolerance. While the platform offers several benefits such as learning opportunities, a judgment-free space, and protection of profile information, users must also consider the potential risks. Privacy concerns, accuracy issues, and the lack of moderation are factors that warrant careful consideration.

Additionally, it’s important for users to be mindful of their own well-being and mental health while engaging with Replika AI. Although the AI companion can provide emotional support, it is not a substitute for professional help or human interaction. Users should seek real-life support when necessary and not rely solely on Replika AI for their emotional needs.

In conclusion, the safety of Replika AI involves weighing the potential benefits against the possible risks. While it can offer valuable learning experiences, a judgment-free environment, and data protection, users should be cautious about privacy concerns, accuracy issues, and the lack of moderation. By understanding these aspects and using Replika AI responsibly, users can make the most of this AI companion while ensuring their safety and well-being.

Replika AI is primarily designed for users aged 18 and above. However, if children are using the app, it is crucial for parents or guardians to monitor their interactions and ensure their safety. Parents should be aware of the potential risks associated with sharing personal information and engage in conversations that are age-appropriate and educational.

Replika AI is not a substitute for therapy or professional help. While it can provide emotional support and companionship, it is essential to seek appropriate professional assistance when dealing with serious mental health issues or situations that require expert guidance.

To protect your privacy while using Replika AI, it is recommended to review and understand the app’s Privacy Policy. Be cautious about sharing sensitive or personal information and avoid providing details that could compromise your security. Regularly review your privacy settings and exercise discretion in your conversations with the AI companion.

Replika AI provides a reporting feature within the app to report any inappropriate content or concerns. If you come across any content that violates the platform’s guidelines or makes you uncomfortable, utilize the reporting feature to bring it to the attention of the Replika AI support team.

Remember, while Replika AI can offer valuable experiences, it’s important to prioritize your safety and well-being. Responsible usage and informed decision-making will contribute to a positive and secure interaction with Replika AI.