#Data Science Company

[adzgurupng]

Adzguru.co is your premier data science company, turning raw data into powerful insights. We harness AI, machine learning, and advanced analytics to drive business success. Elevate your strategy with data-driven precision today!

[aiinfox]

Leading Data Science Company in Mohali - AiInfox

AiInfox, the Leading Data Science Company in Mohali, specializing in innovative solutions and advanced analytics. Our team leverages cutting-edge technology to transform your data into actionable insights, driving business growth and efficiency.

[pritivora26]

Data now governs even the most cutting-edge businesses. Due to increased internet access, many data streams are flowing across the world. Businesses are conscious of the fact that this data translates into knowledge they can use to enhance customer service, comprehend trends, or even identify market weaknesses. For all data-related solutions, they consequently seek out data analytics companies. Read more.

[techtinkerer]

Kellton is a dynamic data science services company that specializes in delivering innovative data-driven solutions. With a focus on AI, machine learning, and data analytics, we assist businesses in extracting meaningful insights from their data to drive growth and innovation. Our dedicated team of data scientists and engineers is committed to helping organizations leverage their data assets to make informed decisions and gain a competitive advantage.

[pattemdigitalsolutions]

Data storage is more crucial in the enterprises whether it is small or large organisations the data should be secure and it can be easily fetched to perform the various operations to do so the data consulting authority should be needed to fetched large amount of data. Pattem Digital being an top Data science consulting agency provided with data analytical services using AI and ML technology comprising of high end programming languages this helps the clients in fetching with the vast data and analyze it in simple manner and make their work simple. If you are willing to adopt data science related services to be done reach to us ! pattemdigital.com/data-science…

[aeolianblues]

I'm not an extrovert. At all. In everyday life, I'm a yapper, sure, but I need someone to first assure me I am okay to yap, so I don't start conversations, even when I really want to join in sometimes! It's just the social anxiety acting up. God knows where from and why I lose a lot of my inhibitions when it comes to talking to people about music. I don't know where the confidence has suddenly sprung from. I've made a crazy amount of friends in musical circles, either just talking to people about common music or (since it is after all in music circles) talking to bands about their own music. I let out a sigh of relief any time an interaction goes well, because in truth it's going against my every instinct. I wish I could do that in everyday life

[daedrabela]

people should have to take a computer literacy test before they get hired

[dogg0melette]

In happier life news, my company is letting do some experiments and acquire some preliminary data to apply for a seed grant to study the disease my mom has been which is really cool!! And the unrelated collaboration with my husband’s postdoc lab is going well. Doing cool science yeehaw.

[wickedhawtwexler]

one day. the excessive amount of music-related data analysis projects in my portfolio will get spotify's attention. and they will call me up like "hey ms. wickedhawtwexler, you're a genius data scientist, we'd love to hire you and pay you $200k a year to science our music data full time". manifesting

[offsoarmarketing]

Offsoar offers top-tier Data Warehouse Consulting Services, helping businesses optimize data management, enhance scalability, and drive smarter decision-making for digital transformation. https://offsoar.com/services/data-warehousing-consulting-services/

[glasshomewrecker]

In a silicon valley, throw rocks. Welcome to my tech blog.

Antiterf antifascist (which apparently needs stating). This sideblog is open to minors.

Liberation does not come at the expense of autonomy.

* I'm taking a break from tumblr for a while. Feel free to leave me asks or messages for when I return.

Frequent tags:

[baturwebdevelopers]

The web development company in Gujarat specializes in developing websites and web applications for clients. These companies have teams of experts, including web developers, and web designers, who work together to build websites according to business requirements.

[mangled-by-disuse]

I have such mixed feelings about the love languages thing specifically, because, like, gary chapman fucking sucks and there's no scientific validity to his work BUT

at the same time, i do think there's some value in recognising and discussing the fact that different people need different expressions of love in different amounts? Especially in relationships.

Like, I have just recently been having a discussion with my partner about how he really doesn't tend to express his affection through gifts, whereas (as someone who is mega-bad at expressing sincere feeling) I do rely heavily on giving gifts and doing things for people as a less scary way to express love. Joe doesn't like giving gifts, because he's scared he'll do it wrong, and is only so-so on receiving them. He prefers to express love through physical contact and saying nice things. I hate having nice things said to me unless I am allowed to immediately rebut them with a joke or sarcastic comment that makes them less scarily close to emotional honesty. too many words of affirmation and i will genuinely just start avoiding you because it is painfully awkward to me.

and none of that means we are fundamentally different categories of people, which is where the 5 Love Languages stuff falls into being absolute bollocks. but I have seen, and done, enough throwing the baby out with the bathwater on that to be a little defensive - I think reasonable applications of the concept are actually really quite valuable. and for me, the taxonomy Chapman suggests (words of affirmation, quality time, gifts, acts of service, physical touch) while not at all exhaustive or thorough, is a useful framework to hang those conversations on. bc, like, no, the way people communicate and receive affection is not universal, and from personal experience, assuming that it is can have really significant problems for a relationship.

...you could argue that this is parallel to BMI in terms of "tools being used in totally not the way they should be used" though, tbf.

I can't keep having the same conversations about love languages, mbti, iq, bmi, "brain fully formed at 25" and shit over and over again...

[wallabywannabe]

My sister was reviewing survey responses at work and was disappointed that some of the responses (from elementary school teachers, mind you!) were clearly ChatGPT. She could tell because they referenced a lot of animals they didn't have at the zoo at the time the kids visited.

But what really worries me is that my sister was surprised that ChatGPT got it so wrong. Because that information is on the internet, and it just pulls info from the internet, right?

My sister is an intelligent person and I rant to her about AI all the time, so if she has this misconception, I'm sure a lot of people do, which worries me.

ChatGPT and LLM do NOT just pull info from the internet. They do NOT take verbatim sentences from online sources. They're not trustworthy, but not because the source is the internet. They take WORDS, not complete sentences, from the internet and put them together. They look for the most common words that are put together and put them in an order that SOUNDS LIKE the rest of the internet. They look for patterns. ChatGPT finds a bunch of articles about Zoo Atlanta and pandas, so it adds pandas to its sentences when you prompt it about Zoo Atlanta animals. It does not notice that all the articles were about the pandas going back to China. It does not know how to read and understand context! It is literally just putting words together that sound good.

The hallucination problem is not a bug that can be worked out. This is the whole premise of how LLMs were designed to work. AI that is trained like this will all be worthless for accuracy. You cannot trust that AI overview on Google, nor can you trust ChatGPT to pull up correct information when you ask it. It's not trying to! That was never what it was designed to do!

[jcmarchi]

AI enables shift from enablement to strategic leadership

New Post has been published on https://thedigitalinsider.com/ai-enables-shift-from-enablement-to-strategic-leadership/

AI enables shift from enablement to strategic leadership

CIOs and business leaders know they’re sitting on a goldmine of business data. And while traditional tools such as business intelligence platforms and statistical analysis software can effectively surface insights from the collated data resources, doing so quickly, in real-time and at scale remains an unsolved challenge.

Enterprise AI, when deployed responsibly and at scale, can turn these bottlenecks into opportunities. Acting quickly on data, even ‘live’ (during a customer interaction, for example), is one of the technology’s abilities, as is scalability: AI can process large amounts of information from disparate sources almost as easily as it can summarize a one-page spreadsheet.

But deploying an AI solution in the modern enterprise isn’t simple. It takes structure, trust and the right talent. Along with the practical implementation challenges, using AI brings its own challenges, such as data governance, the need to impose guardrails on AI responses and training data, and persistent staffing issues.

We met with Rani Radhakrishnan, PwC Principal, Technology Managed Services – AI, Data Analytics and Insights, to talk candidly about what’s working — and what’s holding back CIOs in their AI journey. We spoke ahead of her speaking engagement at TechEx AI & Big Data Expo North America, June 4 and 5, at the Santa Clara Convention Center.

Rani is especially attuned to some of the governance, data privacy and sovereignty issues that face enterprises, having spent many years in her career working with numerous clients in the health sector — an area where issues like privacy, data oversight and above all data accuracy are make-or-break aspects of technology deployments.

“It’s not enough to just have a prompt engineer or a Python developer. … You still need the human in the loop to curate the right training data sets, review and address any bias in the outputs.” —Rani Radhakrishnan, PwC

From support to strategy: shifting expectations for AI

Rani said that there’s a growing enthusiasm from PwC’s clients for AI-powered managed services that can provide both business insights in every sector, and for the technology to be used more proactively, in so-called agentic roles where agents can independently act on data and user input; where autonomous AI agents can take action based on interactions with humans, access to data resources and automation.

For example, PwC’s agent OS is a modular AI platform that connects systems and scales intelligent agents into workflows, many times faster than traditional computing methods. It’s an example of how PwC responds to the demand for AI from its clients, many of whom see the potential of this new technology, but lack the in-house expertise and staff to act on their needs.

Depending on the sector of the organization, the interest in AI can come from many different places in the business. Proactive monitoring of physical or digital systems; predictive maintenance in manufacturing or engineering; or cost efficiencies won by automation in complex, customer-facing environments, are just a few examples.

But regardless of where AI can bring value, most companies don’t yet have in-house the range of skills and people necessary for effective AI deployment — or at least, deployments that achieve ROI and don’t come with significant risk.

“It’s not enough to just have a prompt engineer or a Python developer,” Rani said. “You’ve got to put all of these together in a very structured manner, and you still need the human in the loop to curate the right training data sets, review and address any bias in the outputs.”

Cleaning house: the data challenge behind AI

Rani says that effective AI implementations need a mix of technical skills — data engineering, data science, prompt engineering — in combination with an organization’s domain expertise. Internal domain expertise can define the right outcomes, and technical staff can cover the responsible AI practices, like data collation and governance, and confirm that AI systems work responsibly and within company guidelines.

“In order to get the most value out of AI, an organization has to get the underlying data right,” she said. “I don’t know of a single company that says its data is in great shape … you’ve got to get it into the right structure and normalize it properly so you can query, analyze, and annotate it and identify emerging trends.”

Part of the work enterprises have to put in for effective AI use is the observation for and correction of bias — in both output of AI systems and in the analysis of potential bias inherent in training and operational data.

It’s important that as part of the underlying architecture of AI systems, teams apply stringent data sanitization, normalization, and data annotation processes. The latter requires “a lot of human effort,” Rani said, and the skilled personnel required are among the new breed of data professionals that are beginning to emerge.

If data and personnel challenges can be overcome, then the feedback loop makes the possible outcomes from generative AI really valuable, Rani said. “Now you have an opportunity with AI prompts to go back and refine the answer that you get. And that’s what makes it so unique and so valuable because now you’re training the model to answer the questions the way you want them answered.”

For CIOs, the shift isn’t just about tech enablement. It’s about integrating AI into enterprise architecture, aligning with business strategy, and managing the governance risks that come with scale. CIOs are becoming AI stewards — architecting not just systems, but trust and transformation.

Conclusion

It’s only been a few years since AI emerged from its roots in academic computer science research, so it’s understandable that today’s enterprise organizations are, to a certain extent, feeling their way towards realizing AI’s potential.

But a new playbook is emerging — one that helps CIOs access the value held in their data reserves, in business strategy, operational improvement, customer-facing experiences and a dozen more areas of the business.

As a company that’s steeped in experience with clients large and small from all over the world, PwC is one of the leading choices that decision-makers turn to, to begin or rationalize and direct their existing AI journeys.

Explore how PwC is helping CIOs embed AI into core operations, and see Rani’s latest insights at the June TechEx AI & Big Data Expo North America.

(Image source: “Network Rack” by one individual is licensed under CC BY-SA 2.0.)