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AGI isnt here (yet): How to make informed, strategic decisions in the meantime – VentureBeat

June 19, 2024 | Author: admin

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Ever since the launch of ChatGPT in November 2022, the ubiquity of words like inference, reasoning and training-data is indicative of how much AI has taken over our consciousness. These words, previously only heard in the halls of computer science labs or in big tech company conference rooms, are now overhead at bars and on the subway.

There has been a lot written (and even more that will be written) on how to make AI agents and copilots better decision makers. Yet we sometimes forget that, at least in the near term, AI will augment human decision-making rather than fully replace it. A nice example is the enterprise data corner of the AI world with players (as of the time of this articles publication) ranging from ChatGPT to Glean to Perplexity. Its not hard to conjure up a scenario of a product marketing manager asking her text-to-SQL AI tool, What customer segments have given us the lowest NPS rating?, getting the answer she needs, maybe asking a few follow-up questions and what if you segment it by geo?, then using that insight to tailor her promotions strategy planning.

This is AI augmenting the human.

Looking even further out, there likely will come a world where a CEO can say: Design a promotions strategy for me given the existing data, industry-wide best practices on the matter and what we learned from the last launch, and the AI will produce one comparable to a good human product marketing manager. There may even come a world where the AI is self-directed and decides that a promotions strategy would be a good idea and starts to work on it autonomously to share with the CEO that is, act as an autonomous CMO.

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Overall, its safe to say that until artificial general intelligence (AGI) is here, humans will likely be in the loop when it comes to making decisions of significance. While everyone is opining on what AI will change about our professional lives, I wanted to return to what it wont change (anytime soon): Good human decision making. Imagine your business intelligence team and its bevy of AI agents putting together a piece of analysis for you on a new promotions strategy. How do you leverage that data to make the best possible decision? Here are a few time (and lab) tested ideas that I live by:

Before seeing the data:

While looking at the data:

While making the decision:

At this point, if youre thinking, this sounds like a lot of extra work, you will find that this approach very quickly becomes second nature to your executive team and any additional time it incurs is high ROI: Ensuring all the expertise at your organization is expressed, and setting guardrails so the decision downside is limited and that you learn from it whether it goes well or poorly.

As long as there are humans in the loop, working with data and analyses generated by human and AI agents will remain a critically valuable skill set in particular, navigating the minefields of cognitive biases while working with data.

Sid Rajgarhia is on the investment team at First Round Capital and has spent the last decade working on data-driven decision making at software companies.

DataDecisionMakers

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DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation.

If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers.

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Q&A with Ray Kurzweil about nanobots and AI-human mind-melds – The Boston Globe

June 19, 2024 | Author: admin

You may find a lot of this hard to believe. I know I do. But it is not easy to dismiss Kurzweil, 76, as just another hand-wavy tech hype man. He has been working in AI since 1963, probably longer than anyone else alive today, and has developed several landmark technologies. In 1965, when he was a teenager, he got a computer to compose music, a feat that landed him on national TV and earned him a meeting with President Lyndon Johnson. He went on to invent a text-to-speech reading machine for blind people, an early music synthesizer, and speech-recognition tools. For the past decade, hes been the chief futurist at Google, where today he has the job title of principal researcher and AI visionary.

Every few years, Kurzweil unspools his ideas and defends his predictions in a new book that is rich with footnotes, charts, and carefully honed arguments. His most recent book, The Singularity Is Nearer: When We Merge With AI, is no exception. But it did not persuade me that his AI-maximalist vision is coming close to fruition or that it would be desirable.

My interview with Kurzweil has been edited and condensed.

You say in the book that these are the most exciting and momentous years in all of history. Why is that?

Theres a graph that is really behind it. It shows the exponential progress in computation from 1939 to 2023. Weve gone from computers performing 0.000007 calculations per second per constant [inflation-adjusted] dollar to 130 billion calculations per second per constant dollar. And then recently Nvidia came out with a chip with half a trillion calculations per second. That represents a 75 quadrillionfold increase in the amount of computation you get for the same amount of money. And thats why were seeing large language models now. If you look at the progress just in the last two years, its been amazing, and its going to continue at that pace.

Artificial general intelligence will be able to do anything a human can do, at the best level that a human can do. And when it actually goes inside our body and brain, which will happen in the 2030s, we can harness that and make ourselves smarter. One of the implications is that were going to be able to make fantastic progress in coming up with cures for diseases.

I see how AI could give our civilization greater intelligence to solve big problems like finding new medical cures. But I am less sure that a lot of individual people will want so much more intelligence in their daily lives that theyll implant computers inside their bodies. Do you really think that access to more intelligence is fundamentally what people need most? I would suggest we really need more compassion, more forgiveness, more equanimity.

I think that also comes from intelligence.

And people dont necessarily say they want more intelligence, but when it actually [becomes available], they do want it. The fact that everybody has a smartphone if you had described it to people who came before by saying Everybodys got to carry this device around and tried to describe what it does, relatively few people would have voted for that. Yet everybody has a cellphone. So now if you go around and say, Would you want to put something that goes through your bloodstream and develop something in your brain that would talk to the web automatically? people would say, Theres no way Id want to do that. But when it actually happens and people who do that can cure diseases and can be much smarter in conversation youll have a lot more things in your mind that can pop up when a situation calls for it people definitely will do it, regardless of what they think about it right now.

The intelligence we get from having smartphones at our fingertips has also come with the downsides of distraction, solipsism, and other social trade-offs. Wouldnt those only be magnified with vastly more information at our disposal?

Well, were definitely going to have disagreements about things, and popular political figures that people dont like, and its not going to solve all of our problems. But fundamentally, more intelligence is better. Thats where the evolution of humans has gone, and thats why we create machines that make us smarter. And yes, there are always problems and things that humans can do that wouldnt otherwise be feasible that might be negative. But ultimately were much happier and have new opportunities because of making ourselves smarter.

I question your assumption that exponential rates of improvement in computing and related technologies will necessarily continue. I think its plausible that progress slows. GPT-4 inhaled essentially the entire internet but still has a limited understanding of the world. Where is a larger corpus of text going to come from that has a substantially richer representation of the world? And what about the energy consumption of all this computation?

Well, first of all, large language models are misnamed. They do a fantastic job with language, but thats not all they do. Were also using them, for example, to come up with medical cures, and thats not manipulating language thats manipulating biochemistry. Were using them to train robots so that robots can walk normally and do the kinds of things that humans can do, very simple things like clean up this table. So these models are coming that are going to learn really everything that humans can do, not just language. GPT-4 makes certain mistakes if it doesnt know a certain thing, itll just make things up. We actually know the solution to that: Thats going to require more computation.

I also think AI is actually a very valuable thing for humanity to have in terms of energy. We could meet all of our energy needs today if we converted one part out of 10,000 of the sunlight that falls on the earth, and our ability to actually turn that into energy is growing exponentially. If you follow that curve, well meet all of our energy needs from the sun and wind within 10 years.

But there are real physical constraints. Were not putting up new electricity transmission lines or putting electricity storage on the grid at a pace that would let us get all our energy from the sun and wind in 10 years.

I put the graphs in the book: The ability to have the sun in particular added to our energy sources is enormous compared to what it was five years ago or 10 years ago. Weve got plenty of headroom there. And you can look at applying AI to lots of related areas manufacturing buildings for example. I dont think the energy needs of these things are going to be a barrier. Also, there are ways of bringing down the energy needs.

Do you fundamentally see technological advancement as inevitable?

Absolutely. And we get much more benefit than we get harm.

I often think we live in a generally pessimistic period. Do you feel out of sync with the times?

Yeah, a lot of people are just pessimistic in general. And quite a substantial number of AI scientists think whats happening is disastrous and its going to destroy humanity. They imagine somebody using AI for something thats negative, and they say, How are we going to deal with that? But the tools we have to deal with it are also growing.

I know theres a lot of AI experts who are very much against whats going on. Im just waiting until they get a disease which has no cure and then theyre saved by some cure that comes from AI. Well see how they feel about that.

Brian Bergstein is the editor of the Globe Ideas section. He can be reached at brian.bergstein@globe.com.

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Q&A with Ray Kurzweil about nanobots and AI-human mind-melds - The Boston Globe

Greenpeace: Bitcoin mining companies are hiding energy data, Wall Street is responsible – crypto.news

June 19, 2024 | Author: admin

In a new report filed by Greenpeace, the climate group called for Wall Street accountability in crypto mining, and it correlated bitcoin mining to excessive global energy usage.

Greenpeace claimed that Bitcoin (BTC) mining has evolved into a significant industry dominated by traditional financial companies that are buying up and operating large-scale facilities, using lots of energy.

In 2023, global Bitcoin mining used approximately 121 TWh of electricity, comparable to the entire gold mining industry or a country like Poland. This resulted in significant carbon emissions, the report contended, as these facilities consume as much electricity as a small city.

Despite the guise of Bitcoin being independent from the mainstream financial system, the industry is deeply connected to traditional finance for Bitcoin mining companies to access capital and to enable trading and investing in Bitcoin, the report read.

The report highlighted traditional financial institutions substantial role in supporting Bitcoin mining. These companies rely on capital from banks, asset managers, insurers, and venture capital firms to build and maintain their operations.

The report identified the top five financiers of carbon pollution from Bitcoin mining in 2022: Trinity Capital, Stone Ridge Holdings, BlackRock, Vanguard, and MassMutual. Together, they were responsible for over 1.7 million metric tons of CO2 emissions, equivalent to the annual electricity use of 335,000 American homes.

Bitcoin mining companies Marathon Digital, Hut 8, Bitfarms, Riot Platforms, and Core Scientific generated emissions comparable to 11 gas-fired power plants.

The report pointed out that Bitcoins environmental impact compared to its market value is similar to beef production and gasoline from crude oil. It also mentioned that Bitcoins environmental effects have worsened as the industry has expanded.

Bitcoin uses a lot of electricity due to its Proof-of-Work (PoW) consensus mechanism. Unlike traditional currencies, cryptocurrencies operate through a decentralized digital ledger. Bitcoins PoW requires miners to solve complex algorithms that use significant electricity.

Energy-hungry miners are straining electrical grids across the U.S. and worlddraining electricity when more is needed to power electrification of housing, transportation, and manufacturing to meet global climate targets, the report read.

The report contended that Wall Street, traditional financers, and banks are more responsible for the alleged energy disparity than Bitcoin miners themselves. Greenpeace contended that institutions encourage (through tax breaks and bank benefits) miners to use more energy.

The report contended that miners depend on backing from banks and asset managers, and Wall Street and the banking industry are responding favorably, seeking their portion of the rewards.

Greenpeace argued that financial institutions should be more transparent about their environmental incentives to reduce the negative impact of these incentives.

Bitcoin miners need to disclose data about their energy use and carbon emissions, the report read. Financial companies also need to report on the financed and facilitated emissions associated with their investments, loans, and underwriting services for Bitcoin mining companies.

They called for Bitcoin miners to pay a fair share for their electricity use, strain on electrical grids, greenhouse gas emissions, water consumption, and disruption to nearby communities. They suggested implementing a different consensus mechanism for Bitcoin to address the current energy-intensive proof-of-work model and ultimately resolve Bitcoins environmental impact.

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Greenpeace: Bitcoin mining companies are hiding energy data, Wall Street is responsible - crypto.news

Jordan Peterson exposes the ‘drowning’ effect of assisted suicide drug – Live Action

June 17, 2024 | Author: admin

Jordan Petersons recent interview with mental health advocate and host of The Brass & Unity Podcast, Kelsi Sheren, exposed dark truths about the process of assisted death and what happens to the people electing to die by assisted suicide and euthanasia. As previously reported by Live Action News, the process is not peaceful, because the person can drown to death while paralyzed.

Ive already seen romanticized death encounters distributed online, said Peterson. And so, I think thatll be the next thing thatll confront us in Canada.

Sheren replied that theres something really troubling and concerning that needs to be discussed concerning MAiD (Medical Assistance in Dying) in Canada that no one understands.

What I found out that really, really bothered me was the mechanism of the actual procedure, she said. So theres this drug called sodium thiopental and its made in Italy so its used by anesthesiologists. And the anesthesiologist that came forward with this he came forward with this for the Senate subcommittee talking about this with MAiD and his concerns with it because MAiD is being seen as compassionate and empathetic care.

That anesthesiologist, Dr. Joel Zivot, penned an op-ed discussing this drug back in 2021, which Live Action News reported on at the time. He looked at a handful of autopsies of executed criminals who were given sodium thiopental, and he found their deaths had not been peaceful. Their lungs were full of fluid. Called pulmonary edema, it makes a person feel as though they are drowning or suffocating. MAiD also uses sodium thiopental.

NPR then used the Freedom of Information Act to examine the autopsies of more than 300 individuals executed in America using sodium thiopental. They learned that 84% of those individuals who had information about their lungs in their paperwork showed a 2x level increase of water in the lungs.

Reporting for NPR in 2020, Noah Caldwell said, Now lawyers are also bringing autopsies to federal courts around the country, claiming that the pain of pulmonary edema amounts to cruel and unusual punishment. And they say this explains why weve seen some inmates in recent years gasping for air and choking as theyre being executed.

READ: Faces of MAID social media campaign opposes Canadas euthanasia program

The effect of this drug is akin to dying by waterboarding or drowning said Sheren. She added, [T]he reason the people who are given MAiD seem peaceful is because they are given a paralytic first so they are completely paralyzed.

Waterboarding falls under torture and cruel and unusual punishment yet pro-euthanasia advocates call MAiD peaceful. Sheren also told Peterson about the millions of dollars the Canadian government is saving on health care costs by killing people instead of caring for them and promoting it as a persons right.

In 2020, Canadas Parliamentary Budget Officer released a report showing that its MAiD program created a net cost reduction of $86.9 million a year for the Canadian government. The report noted that the planned expansion of eligibility for MAiD would create an additionalnet savings of $62 million per year.

In an article forThe Spectator, author Yuan Yi Zhu explained, Health care, in particular for those suffering from chronic conditions, is expensive; but assisted suicide only costs the taxpayer $2,327 per case. And, of course, those who have to rely wholly on government-provided Medicare pose a far greater burden on the exchequer than those who have savings or private insurance. There is already talkof allowing mature minors access to euthanasia toojust think of the lifetime savings.

From 2021 to 2022, deaths by MAiD in Canada grew by 31.2%. Since assisted death was legalized in Canada in 2016, 44,958 have died by it. Each of them were sold the idea of a peaceful death that may have actually been torture.

The DOJ put a pro-life grandmother in jail for protesting the killing of preborn children. Please take 30-seconds to TELL CONGRESS: STOP THE DOJ FROM TARGETING PRO-LIFE AMERICANS.

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Jordan Peterson exposes the 'drowning' effect of assisted suicide drug - Live Action

The Devil and Karl Marx | Dr. Paul Kengor – The Daily Wire

June 17, 2024 | Author: admin

The Jordan B. Peterson PodcastJun 13, 2024

Dr. Jordan B. Peterson sits down in-person with author, historian, and professor of political science, Dr. Paul Kengor. They discuss the lifestyle, writings, and religious ideations of Karl Marx, how communist dogma evolved through modern day, and why equal outcome is wrong on the level of malevolence.

Paul Kengor, Ph.D., is a professor of political science at Grove City College in Grove City, Pennsylvania, and editor of The American Spectator. Hes a New York Times bestselling author of more than 20 books, including The Devil and Karl Marx and The Crusader: Ronald Reagan and the Fall of Communism, which is the basis of the new movie Reagan, starring Dennis Quaid. Kengor is a renowned historian of the Cold War, communism, and Reagan presidency.

This episode was recorded on June 7th, 2024

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The Devil and Karl Marx | Dr. Paul Kengor - The Daily Wire

Moving Brands’ identity for Eppo sets data science apart with evolving graphic systems and shifting forms – It’s Nice That

June 17, 2024 | Author: admin

When software company Eppo reached out to Moving Brands, the team quickly realised that the platform needed an identity and website that reflects their difference in the data market: Eppo is an experimentation platform that was founded by a data scientist in a landscape dominated by engineering-founded competitors, says Jordan Heber, strategy director at Moving Brands. The A/B testing platform that allows companies to successfully compare two versions of a web page or app against each other to determine which one performs better, gives companies the chance to grow, by using their own data as a tool for business. Moving brands aimed to reimagine a visual identity for Eppos model that celebrates transformation, creating new ideas from disparate inputs, and the brands dynamic, athletic nature, all whilst leaving room for flexibility, for the brand to expand in future.

Delivered by a multidisciplinary team across London and the US, the independent design studio reimagined Eppos brand's identity to constantly evolve, with a system of modular visuals that allow for endless iterations and expressions, says Joel Smith, design director at Moving Brands. The data company challenged us to lean into the weird, he adds, in order to help them differentiate their unique background in data science and showcase their experimental spirit. From a palette of bold and unusual colour pairings designed to evoke the discomfort of embracing a culture of experimentation, to a modular new Eppo logo that shapes and rearranges itself: it becomes more experimental as it scales up just like the best tech companies do, says Joel, all aspects of the user experience are constantly in motion, with an experimental feel across print and digital.

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Moving Brands' identity for Eppo sets data science apart with evolving graphic systems and shifting forms - It's Nice That

South River president on reverse mortgage business, data science and priorities – HousingWire

June 17, 2024 | Author: admin

Chris Clow/RMD: How has reverse business this year been going, and how do you foresee business progressing by the time we get to December?

Tyler Plack: We just finished out May, and I thought the May results were very good. Im excited about where were headed. Some of our corporate goals have been to increase the size of our sales floor and to focus on bringing on some of these remote loan officers in the feet in the street model. The reception weve received from some of those loan officers has been very warm and exciting.

What were seeing in terms of our in-person and call-center model is that it continues to grow as well. The results have been increasing month over month for every month this year. Our expectation is that this trend will continue through the end of the year. To say that I am bullish on reverse mortgages is probably an understatement.

Plack: Yeah, we are very focused on our direct-mail model. Weve done a lot of work in the data science department. This is such a crucial corporate goal that I probably spend about half of my day focused on data science. It is that important to what we do. Some of the increase in results youre seeing is due to gains were making in the statistics world.

What that is exactly is a statistical model, but its done well and continues to improve. Were really excited about that and expect to continue growing originations, largely through our own marketing and this new external sales model that were building.

Plack: I think theres a box of consumers who want the product but dont qualify for it, and theres a box of consumers who qualify but dont want the product. What we really want to focus on is the consumers who want the product and qualify for it. Thats where the data modeling comes in.

Were looking at the overall credit picture and the overall property picture, applying a ton of math and statistical models to find who we can help the most. This results in us helping more people at a lower cost and making sure that the people were talking to can actually be helped, rather than people who would love our help but unfortunately will never qualify for it. So, its about the intersection of both the level of interest of the consumer and our projections on their qualification.

Plack: Its a mixture of everything. Theres some data we take from other sources, and theres a ton of data we have internally that were using as well. We throw all that into a model that helps us decide, on a week-to-week basis, who is going to have the highest propensity to take the loan, close and fund.

Plack: I think there are certainly gains to be made through better modeling, but I dont know that its the key for everyone. I know its worked well for me due to our analytically minded team, but for the average loan officer or sales manager, I wouldnt focus on the data. I would focus on making connections with financial advisers and planners, and using traditional methods. With scale, it makes sense the way we do it, but there is more than one way to originate loans.

Any company should take a multifaceted, multimodal approach. We might be really good at direct mail, but we could be missing out on referral networks and that style of origination. Were working to build into that. For those who dont have exposure to direct-mail marketing, perhaps they should spend some time in it. Just like an investment portfolio, your marketing strategy should be diversified.

Look for more from Tyler Plack and South River on RMD soon.

Excerpt from:

South River president on reverse mortgage business, data science and priorities - HousingWire

Analytics and Data Science News for the Week of June 14; Updates from Databricks, Power BI, Qlik & More – Solutions Review

June 17, 2024 | Author: admin

Solutions Review Executive Editor Tim King curated this list of notable analytics and data science news for the week of June 14, 2024.

Keeping tabs on all the most relevant analytics and data science news can be a time-consuming task. As a result, our editorial team aims to provide a summary of the top headlines from the last week, in this space. Solutions Review editors will curate vendor product news, mergers and acquisitions, venture capital funding, talent acquisition, and other noteworthy analytics and data science news items.

Amplitude also launched a new integration portal to make developing integrations easier and faster. Technology partners can now access a range of tools and resources, including documentation, code samples, and best practices, to guide teams through necessary integration steps.

Read on for more

Databricks AI/BI features a pair of complementary experiences: Dashboards, an AI-powered, low-code interface for creating and distributing fast, interactive dashboards; and Genie, a conversational interface for addressing ad-hoc and follow-up questions through natural language.

Read on for more

The integration allows enterprises to access advanced DatabricksMosaic AI functionalitieswithout extensive infrastructure changes or specialized training, facilitated by the advanced features of the newly launched Qlik Talend Cloud and empowering companies to unlock new levels of efficiency and innovation.

Read on for more

Now ready for deployment, Quantexa customers will be able to operationalize Gen AI for transformative gains without additional investment in infrastructure, tooling, and any additional skilled resources.

Read on for more

This new report format offers source control-friendly file structures, facilitating co-development and improving development efficiency for Power BI reports. Together withTMDL for the semantic model, Power BI Projects now have a great source control experience for both report and semantic model.

Read on for more

The technology potentially allows Snowflake SQL users to get more projects into production faster, accelerate time-to-value, and generate more accurate business insights for better decision-making.

Read on for more

Parsables AI-Powered Analytics provides customizable, real-time data visualization tools that enable frontline operators to make informed decisions instantly, addressing issues as they arise and improving overall efficiency and cost-effectiveness.

Read on for more

Watch this space each week as our editors will share upcoming events, new thought leadership, and the best resources from Insight Jam, Solutions Reviews enterprise tech community for business software pros. The goal? To help you gain a forward-thinking analysis and remain on-trend through expert advice, best practices, predictions, and vendor-neutral software evaluation tools.

Join Doug Atkinson and David Loshin as they break down recent forfeiture orders by the FCC involving location data violations by major telecom companies. They discuss the complexities of data sharing, the importance of governance, and the implications of information misuse.

Watch on YouTube

This month on The Jam Session, host Bob Eve is joined by Robert Seiner, Juan Sequeda, and Austin Kronz to tackle this pressing question. The panel discusses the evolving roles of generative AI and data catalogs, exploring their complementary strengths.

Watch on YouTube

Amplitude Field CTO at Amplitude and gain a better understanding of how Amplitude can help you increase the ROI of existing investments and open up options to replace tools youre looking to sunset. Product demo and Q&A included!

My work with companies all over the world has given me a lot of insights into where businesses get lost in the mire of tech and shiny new toy syndrome! In the race to stay ahead of the pack, ignoring the power of data is a sure fire way to doom your business. Guess what: with little benefits and outcomes its not working!

Read on Solutions Review

For consideration in future analytics and data science news roundups, send your announcements to the editor: tking@solutionsreview.com.

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Analytics and Data Science News for the Week of June 14; Updates from Databricks, Power BI, Qlik & More - Solutions Review

Top AI Execs to Watch in 2024: Redhorse Corp.s Brian Sacash – WashingtonExec

June 17, 2024 | Author: admin

Brian Sacash, Redhorse Corp. Director of Data Science, Redhorse Corp.

Brian Sacashs biggest recent achievement was engaging with technology partners to bring the most cutting-edge AI capabilities to the mission space. But rather than simply implementing the latest off-the-shelf solutions, his team is pioneering custom approaches that seamlessly blend this advanced technology to redefine the role of AI as an enabler, not a replacement, for human potential.

While Brian has the technical skills expected of a senior engineer, what sets him apart is his ability to make an impact in other ways: mentoring junior staff, presenting and discussing technical topics, and continually looking for ways to improve Redhorse, said Matt Teschke, Redhorse chief technology officer.

Why Watch

In 2024, Sacash is heavily focused on working with the Redhorse technical teams to help customers intelligently adopt transformative technologies. By combining traditional AI/ML and cutting-edge generative AI, they empower people and organizations to access new potential and excel at what they do, with greater speed and efficiency.

Generative AI has opened doors to new worlds of possibilities, he said. While we are still exploring and navigating these new areas, we must move forward, understanding that generative AI is here to stay, bringing both challenges and opportunities.

Fun fact: Sacash is passionate about writing and enjoyscombining his background in physics and expertise in technology with the science of crafting stories.

AI In Data Analytics: The 10 Best Tools – AiThority

June 17, 2024 | Author: admin

Google, Intel, IBM, NVIDIA, Amazon, PwC, and the list can go on for the big brands adopting AI in data analysis.

The term artificial intelligence data analysis refers to the application of data science and AI methods to improve data cleansing, inspection, and modeling. The ultimate aim is to find useful data that can back up conclusions and decisions.

AI streamlines operations by automating repetitive tasks. Companies can save time and effort by training a computer program to do repetitive tasks instead of humans. Artificial intelligence (AI) can be programmed to mimic human intellect, which allows it to recognize patterns and produce reliable results.

While learning about this issue, its crucial to understand that data analytics and analysis are not the same thing. Data analytics, a branch of BI, is all about mining data for hidden patterns and trends using machine learning.

Read: 10 AI In Energy Management Trends To Look Out For In 2024

Read: Ranking of Software Companies with the Best and Worst Data Security Perception for 2024

Here are some of the best AI tools to analyze data that are trending in 2024.

With PolymerSearch.com, an easy-to-use business intelligence (BI) tool, you can make professional-quality data visualizations, dashboards, and presentations. And all that without ever touching a piece of code. Many different types of data sources can be easily integrated with Polymer. Integrate data sources such as Google Analytics, Facebook, Google Ads, Google Sheets, Airtable, Shopify, Jira, Stripe, WooCommerce, BigCommerce, and more with ease. You may also upload datasets using XSL or CSV files. After youre linked, Polymers AI will automatically evaluate your data, provide insightful suggestions, and create visually appealing dashboards.

With Tableau, customers can engage with their data without knowing how to code, thanks to its analytics and data visualization capabilities. The user-friendly platform facilitates the real-time creation, modification, and seamless sharing of dashboards and reports among users and teams. As one would expect from a tool of its kind, it supports databases of varying sizes and provides users with several visualization choices to help them make sense of their data.

Another tool that doesnt require coding is MonkeyLearn, which allows customers to see and reorganize their data with AI data analysis features. Depending on the users requirements, the platforms built-in text analysis capabilities may quickly assess and display data. Automatic data sorting by topic or intent, feature extraction from products, and user data extraction are all within the users control with text classifiers and text extractors.

Read: 10 AI In Manufacturing Trends To Look Out For In 2024

One well-known business intelligence product, Microsoft Power BI, also lets users visualize and filter their data to find insights. Users can begin making reports and dashboards right away after importing data from almost any source. In addition to using AI-powered features to analyze data, users can construct machine learning models. Despite its higher price tag, the platform offers native Excel integration and a user interface that is quicker and more responsive than competing options. It also comes with many integrations.

Another data analytics software that helps developers and analysts organize and display data is Sisense. The platforms dynamic user interface and many drag-and-drop capabilities make it simple to use. When working with huge datasets, Sisenses In-Chip technology makes calculation faster by letting users pick between RAM and CPU to handle the data. Users with basic reporting and visualization needs who are working with smaller datasets may find the platform to be a decent fit, despite its restricted visualization features.

Back when it was first released, Microsoft Excel stood head and shoulders above the competition when it came to data analysis. Quickly process and analyze data, make various basic visualizations, and filter data with search boxes and pivot tablesall with Excels Data Analysis Toolpak. Machine learning models, cluster data calculations, and complicated neural networks can all be built in Excel using formulas, and the program even lets users avoid coding altogether. Even without the requirement to code, Excels spreadsheet paradigm and steep learning curve limit its potential.

To help businesses make informed decisions, Akkio provides a platform for data analytics and forecasting. You can qualify, segment, and prioritize your lead lists with the help of this no-coding platforms lead-scoring tools. Using the data at their disposal, users can access future forecasts on nearly any dataset thanks to the forecasting features. Quick and easy to use, the tool has a small but helpful set of connectors for transferring data to and from other programs.

Both technical and non-technical users will appreciate the platforms adaptability and the many data exploration options it comes with. Teams may work together on the platform with ease, utilizing workflows and drag-and-drop editors to customize their data. Despite its robust functionality, QlikView is only a good fit for users who can make full use of the platform due to its costly price and relatively limited AI feature set.

Looker is an additional no-code tool for data analysis and business intelligence that is part of the Google Cloud. It has significant features and integrates with numerous services. Looker can consolidate all of a users data sources into one location, handle massive databases, and let users create numerous dashboards and reports. In addition to having Googles support, the platform has powerful data modeling capabilities. The site is user-friendly, however it lacks customization options and makes report creation a tedious process.

SAP BusinessObjects integrates well with the rest of the SAP suite and enables less technical users to analyze, visualize, and report on their data. It gives people access to AI and ML tools, which they may use for things like data visualization and modeling, better reporting, and dashboarding. Users can also get predictive forecasting features to go further into their data with this tool. Despite the platforms price cuts, the solutions overall costespecially when purchasing platform licensescan be too high for some. Users who are currently customers of SAP and can make use of an AI data tool that integrates with their existing SAP capabilities will find this tool to be more suitable.

Read: Intels Automotive Innovation At CES 2024

We had exclusive commentary from one of our AiThority guest in his byline from Arvind Rao is the Chief Technology Officer, Edge Platforms, EdgeVerve.

Companies are increasingly usingRobotic Process Automation (RPA), easily among the most widely applied tools, to streamline all insurance processes, including marketing, renewals, and sales. A notable instance from the industry demonstrates that Connected Automation can significantly enhance operational efficacy, with one major insurance firm in the US reportedly achieving around 95% efficiency in its processes.

While admittedly RPA has its embedded advantages, it is also critical to leverage cognitive capabilities withAI and analyticsfor a greater degree of efficiency. The inclusion of cognitive software solutions, like natural language processing, can contribute to the transformation of the insurance business from a purely human-oriented domain to an intelligent business landscape.

Clearly, the technological options available at present can only address part of the challenge. Leaders of connected enterprises have the task of persuading insurance firms to move away from traditional methods, and also further raise the level of intelligent technology adoption. While AI is being used in the process, data of low relevance can have a debilitating impact on the decision-making process. Contextual data, incorporation of the organizations policies, and historical interpretation of policy decisions, together with AI, can help throw up more intelligent and accurate recommendations to underwriters in terms of what kind of risk is acceptable.

An estimated $154 b****** was spent worldwide on AI research and implementation in 2023, marking the fastest-ever growth in AI expenditure.

Among artificial intelligence subfields, generative AI is booming. With the rise of chatbots and other forms of direct user interaction with AI, AI systems are rapidly becoming more collaborative.

Read: How to Incorporate Generative AI Into Your Marketing Technology Stack

According to reports, three b****** individuals utilize Googles AI assistant for email assistance and collaboration within the Google Workspace suite. Separately, in just a few months, ChatGPT (a joint venture between OpenAI and Microsoft) amassed more than 100 million users. Another development in artificial intelligence is the displacement of huge corporations by smaller generative models that may be run on desktop computers. Companies no longer need to depend on a third party to develop their AI applications; new approaches in deep learning and neural networks greatly improve the efficiency of running AI models on local devices. This is in contrast to traditional AI models, which consume a lot of resources.

AI uses natural language processing (NLP) and other techniques to analyze unstructured data like text, images, and audio, extracting valuable insights.

Supervised learning involves training an AI model on a labeled dataset, whereas unsupervised learning involves finding patterns and relationships in data without labeled outcomes.

Yes, AI can process and analyze data in real time, enabling immediate insights and timely decision-making.

Neural networks are a type of machine learning model inspired by the human brain, used in tasks like image recognition, speech processing, and complex pattern recognition in data analytics.

AI can automatically generate insightful visualizations, highlight key trends and anomalies, and personalize dashboards based on user preferences and behaviors.

AI models can identify deviations from normal patterns in data, which is useful for detecting fraud, network security breaches, and other irregular activities.

AI helps analyze customer data to understand behavior, predict future actions, personalize marketing, and improve customer satisfaction.

Ethical considerations include ensuring data privacy, avoiding biases in AI models, maintaining transparency in AI decisions, and preventing misuse of AI insights.

Businesses can start by identifying use cases, ensuring data quality, selecting appropriate AI tools, and hiring or training staff with the necessary skills.

Deep learning is a subset of machine learning that uses multi-layered neural networks to analyze large and complex datasets, enabling high-level abstraction and insights.

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

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