Intelligence Archives - Business Intelligence Info

Tag Archives: Intelligence

Artificial intelligence and the antitrust case against Google

October 22, 2020 Big Data

Following the launch of investigations last year, the U.S. Department of Justice (DOJ) together with attorney generals from 11 U.S. states filed a lawsuit against Google on Tuesday alleging that the company maintains monopolies in online search and advertising, and violates laws prohibiting anticompetitive business practices.

It’s the first antitrust lawsuit federal prosecutors filed against a tech company since the Department of Justice brought charges against Microsoft in the 1990s.

“Back then, Google claimed Microsoft’s practices were anticompetitive, and yet, now, Google deploys the same playbook to sustain its own monopolies,” the complaint reads. “For the sake of American consumers, advertisers, and all companies now reliant on the internet economy, the time has come to stop Google’s anticompetitive conduct and restore competition.”

Attorneys general from no Democratic states joined the suit. State attorneys general — Democrats and Republicans alike — plan to continue on with their own investigations, signaling that more charges or backing from states might be on the way. Both the antitrust investigation completed by a congressional subcommittee earlier this month and the new DOJ lawsuit advocate breaking up tech companies as a potential solution.

The 64-page complaint characterizes Google as a “monopoly gatekeeper for the internet” and spells out the reasoning behind the lawsuit in detail, documenting the company’s beginning at Stanford University in the 1990s alongside deals made in the past decade with companies like Apple and Samsung to maintain Google’s dominance. Also key to Google’s power and plans for the future is access to personal data and artificial intelligence. In this story, we take a look at the myriad of ways in which artificial intelligence plays a role in the antitrust case against Google.

Search

The best place to begin when examining the role AI plays in Google’s antitrust case is online search, which is powered by algorithms and automated web crawlers that scour webpages for information. Personalized search results made possible by the collection of personal data started in 2009, and today Google can search for images, videos, and even songs that people hum. Google dominates the $ 40 billion online search industry, and that dominance acts like a self-reinforcing cycle: More data leads to more training data for algorithms, defense against competition, and more effective advertising.

“General search services, search advertising, and general search text advertising require complex algorithms that are constantly learning which organic results and ads best respond to user queries; the volume, variety, and velocity of data accelerates the automated learning of search and search advertising algorithms,” the complaint reads. “The additional data from scale allows improved automated learning for algorithms to deliver more relevant results, particularly on ‘fresh’ queries (queries seeking recent information), location-based queries (queries asking about something in the searcher’s vicinity), and ‘long-tail’ queries (queries used infrequently).”

Search is now primarily conducted on mobile devices like smartphones or tablets. To build monopolies in mobile search and create scale insurmountable to competitors, the complaint states, Google turned to exclusionary agreements with smartphone sellers like Apple and Samsung as well as revenue sharing with wireless carriers. The Apple-Google symbiosis is in fact so important that losing it is referred to as “code red” at Google, according to the DOJ filing. An unnamed senior Apple employee corresponding with their counterpart at Google said it’s Apple’s vision that the two companies operate “as if one company.” Today, Google accounts for four out of five web searches in the United States and 95% of mobile searches. Last year, Google estimated that nearly half of all search traffic originated on Apple devices, while 15-20% of Apple income came from Google.

Data at scale

Exclusive agreements that put Google apps on mobile devices effectively captured hundreds of millions of users. An antitrust report referenced these data advantages, stating that “Google’s anticompetitive conduct effectively eliminates rivals’ ability to build the scale necessary to compete.”

In addition to the DOJ report, the antitrust report Congress released earlier this month frequently cites the network effect achieved by Big Tech companies as a significant barrier to entry for smaller businesses or startups. The incumbents have access to large data sets that give them a big advantage, “especially when combined with machine learning and AI,” the report reads. “Companies with superior access to data can use that data to better target users or improve product quality, drawing more users and, in turn, generating more data — an advantageous feedback loop.”

Network effects often come up in the congressional report in reference to mobile operating systems, public cloud providers, and AI assistants like Alexa and Google Assistant, which improve their machine learning models through the collection of data like voice recordings.

One potential solution the congressional investigation suggested is better data portability to help small businesses compete with tech giants.

Google Assistant

One part of maintaining Google’s search monopoly, according to the congressional report, is control of emerging search access points. While Google searches began on desktop computers, mobile is king today, and fast emerging are devices like smartwatches, smart speakers, and IoT devices with AI assistants like Alexa, Google Assistant, and Siri. Virtual assistants are using AI to turn speech into text and predict a user’s intent, becoming a new battleground. An internal Google document declared voice “will become the future of search.”

The growth of searches via Amazon Echo devices is why a Morgan Stanley analyst previously suggested Google give everyone in the country a free speaker. In the end, he concluded, it would be cheaper for Google to give away hundreds of millions of speakers than to lose its edge to Amazon.

The scale afforded by Android and native Google apps also appears to be a key part of Google Assistant’s ability to understand or translate dozens of languages and collect voice data across the globe.

Search is primarily done on mobile devices today. That’s what drives the symbiotic relationship between Apple and Google, where Apple receives 20% of its total revenue from Google in exchange for making Google the de facto search engine on iOS phones, which still make up about 60% of the U.S. smartphone market.

The DOJ suit states that Google is concentrating on Google Nest IoT devices and smart speakers because internet searches will increasingly take place using voice orders. The company wants to control the next popular environment for search queries, the DOJ says, whether it be wearable devices like smartwatches or activity monitors from Fitbit, which Google announced plans to acquire roughly one year ago.

“Google recognizes that its ‘hardware products also have HUGE defensive value in virtual assistant space AND combatting query erosion in core Search business.’ Looking ahead to the future of search, Google sees that ‘Alexa and others may increasingly be a substitute for Search and browsers with additional sophistication and push into screen devices,’” the DOJ report reads. “Google has also harmed competition by raising rivals’ costs and foreclosing them from effective distribution channels, such as distribution through voice assistant providers, preventing them from meaningfully challenging Google’s monopoly in general search services.”

In other words, only Google Assistant can get microphone access for a smartphone to respond to a wake word like “Hey, Google,” a tactic the complaint says handicaps rivals.

AI like Google Assistant also features prominently in the antitrust report a Democrat-led antitrust subcommittee in Congress released, which refers to AI assistants as efforts to “lock consumers into information ecosystems.” The easiest way to spot this lock-in is when you consider the fact that Google prioritizes YouTube, Apple wants you to use Apple Music, and Amazon wants users to subscribe to Amazon Prime Music.

The congressional report also documents the recent history of Big Tech companies acquiring startups. It alleges that in order to avoid competition from up-and-coming rivals, companies like Google have bought up startups in emerging fields like artificial intelligence and augmented reality.

Finishing thoughts

If you expect a quick ruling by the DC Circuit Court in the antitrust lawsuit against Google, you’ll be disappointed — that doesn’t seem at all likely. Taking the 1970s case against IBM and the Microsoft suit in the 1990s as a guide, antitrust cases tend to take years. In fact, it’s not outside the realm of possibility that this case could still be happening the next time voters pick a president in 2024.

What does seem clear from language used in both US v Google and the congressional antitrust report is that both Democrats and Republicans are willing to consider separating company divisions in order to maintain competitive markets and a healthy digital economy. What’s also clear is that both the Justice Department and antitrust lawmakers in Congress see action as necessary based in part on how Google treats personal data and artificial intelligence.

The audio problem:

Learn how new cloud-based API solutions are solving imperfect, frustrating audio in video conferences. Access here

Let’s block ads! (Why?)

Big Data – VentureBeat

The Social Impact of Artificial Intelligence and Data Privacy Issues

September 8, 2020 BI News and Info

The current era of AI and Big Data is already considered by many as the start of the fourth industrial revolution that will reshape the world in years to come. Google searches, Map navigation, voice assistants like Alexa, personalized recommendations on portals like Facebook, Netflix, Amazon, and YouTube are just a few examples where artificial intelligence is already playing an important role in our day to day lives. Perhaps people do not even realize this. In fact, a report suggests that the AI market will reach a whopping $ 169.41 Billion by the year 2025.

But there is a negative aspect of AI as well, which poses great privacy and social risk. The risk is associated with how some organizations are collecting and processing a vast amount of user data in their AI-based system without their knowledge or consent, which can lead to concerning social consequences.

(Source)

Is your Data Private?

Every time you go to the internet for searches, browsing websites, or when you use mobile apps, you do not even realize that you are giving away your data either explicitly or without your knowledge. And most of the time you allow these companies to collect and process your data legally since you would have clicked “I agree” button of terms and conditions of using their services.

Apart from your information that you explicitly submit to your websites like Name, Age, Emails, Contacts, Videos, or Photo uploads, you also allow them to collect your browsing behavior, clicks, likes and dislikes. Reputed companies like Google and Facebook use this data to improve their services and do not sell this to anyone. Still, there have been instances where third party companies have scrapped sensitive user data by loopholes or data breaches. In fact, the sole intention of many companies is to collect user data by luring them into using their online services, and they sell this data for vast amounts of money to third parties.

The situation has worsened with the surge in malicious mobile apps whose primary purpose is to collect even that data from the phone for which it did not seek permission. These are primarily data collection apps disguised as a game or entertainment app. In today’s world, smartphones contain very sensitive data like personal images, videos, GPS location, call history, messages, etc., and we do not even know that our data is getting stolen by these mobile apps. Every now and then, such malware apps are removed from Play Store and Apple Store but not before they have already been download millions of times.

Why are Artificial Intelligence and Data Privacy a Big Concern?

People are becoming increasingly aware that their data is not safe online. However, most of them still do not realize the gravity of the situation when AI-based systems process their social data in an unethical manner.

Let us go through some well-known incidents to understand what type of risk we are talking about.

The Cambridge Analytica-Facebook Scandal

(Source)

In 2018, news broke out that a data analytics firm Cambridge Analytica had analyzed the psychological and social behavior of users through their Facebook likes and targeted them with Ad campaigns for the 2016 US Presidential election.

The issue was that FB does not sell its users’ data for such purpose. It was revealed that a developer created a FB quiz app that utilized the loophole in a FB API to collect data of users and their friends as well. He then sold it to the Cambridge Analytica firm who was accused of playing an important role in the outcome of the 2016 US Presidential elections, by unethically obtaining and mining users’ data from Facebook. And the worst thing is that users who had used the quiz app would have blindly given all permission to the app without knowing they are exposing their friends’ data also.

Clearview Face Recognition Scandal

(Source)

Clearview is an artificial intelligence company that created a face recognition system to help police officers identify criminals. They claim that their software has helped law enforcement agencies to track down many pedophiles, terrorists, and sex traffickers.

But in January 2020, The New York Times covered a long story about how Clearview made the mockery of data privacy by scraping around three billion photos of users from social media platforms like Facebook, YouTube, Twitter, Instagram, and Venmo to create the AI system. Its CEO, Mr. Ton-That, claimed that its system only scraped public images from these platforms. Shockingly in an interview with CNN Business, the software fetched the images from the Instagram account of the show’s producer.

Google, Facebook, YouTube, and Twitter sent cease and desist letters to prevent Clearview from scrapping photos from their platform. However, the images that you uploaded online may be included in that AI software without your knowledge. If this software gets into the hands of a rogue police officer, or if the system itself gives false positive, many innocent people might fall prey to police investigations.

DeepFakes

DeepFake puts the face of a person to another’s body (Source)

Images and videos that are created using deep learning and contain a real person acting or saying things they didn’t do or say are called Deepfakes. If used for entertainment purposes, Deepfakes are fun, but people are creating Deepfakes for fake news and information and, worse, Deepfake porn.

In 2019, an application called DeepNude was launched where you could upload any image of women, and it would generate a real-like nude image from it. It is quite disturbing that anyone could exploit woman images available online by creating nude images from DeepNude. After too much controversy, it was shut down. Still, it is just a matter of time that someone can again misuse Deepfake technologies by taking your publicly available videos or photos.

(Source)

Mass Surveillance in China

(Source)

In recent years, China has received severe criticism due to mass surveillance of its people without their consent. They use over 200 million surveillance cameras and facial recognition to keep a constant watch on their people. China also mines their behavioral data captured on the cameras.

To make it worse, China implemented a social credit system to rate the trustworthiness of its citizens and give them ratings accordingly based on their surveillance. People with high credit get more benefits and low credits loose benefits. But the worse part is that all this is being determined by AI-based surveillance without people’s knowledge and consent.

How to prevent misuse of data with AI

The above case studies should have made it clear that the unethical processing of private data with artificial intelligence can lead to very dangerous social consequences. Let us see how we can play our part to stop this malpractice of private data misuse with artificial intelligence.

Government Responsibility

Now many countries have created their own data regulation policies to bring more transparency between these online platforms and the users. Most of these policies are centered around giving users more authority in what data they can share and be informed about how the platform would process their data. A very well known example of this is the GDPR law that came into existence a couple of years back for the EU countries. It gives EU people more control of their personal data and how it is processed by the companies.

Company Responsibility

Large companies like Google, Facebook, Amazon, Twitter, YouTube, Instagram, and LinkedIn literally own a majority of users’ social data across the world. Being, reputed giants, they should be extra careful not to leak any data to malicious people either intentionally or unintentionally.

AI Community Responsibility

The people of AI communities, especially the thought leaders, should raise their voices against the unethical use of AI on the personal data of the users without their knowledge. And also they should educate the world that this practice can lead to such a disastrous social impact. Already many institutes are teaching AI ethics as a subject and also offering it as course.

User’s Responsibility

Finally, we should remember that, despite government regulations, these are just policies and the responsibility lies with us as individuals. We have to be careful about what data we are uploading on social platforms and mobile apps and always inspect what permissions we are giving them to access and process our data, let us not merely “accept” anything blindly in “terms and conditions” that comes our way on these online platforms.

Conclusion

There are many concerns around the ethics in AI within the artificial intelligence community due to the social biases and the prejudices it can create. But processing personal data with AI without people’s consent and further misusing it raises the concerns of AI ethics to the next level. AI emergence in the real world is still in nascent age, and we all have to step up to make sure that our creating AI by misusing personal information should not become a regular occurrence in the future.

Let’s block ads! (Why?)

SQL – Simple Talk

Artificial Intelligence, Real Enhancements, Better Insights

August 6, 2020 Sisense

Sisense News is your home for corporate announcements, new Sisense features, product innovation, and everything we roll out to empower our users to get the most out of their data.

Ashley Kramer is our new chief product and marketing officer. She began her career as a software engineering manager at NASA, worked at Oracle, Amazon, and elsewhere before moving to Alteryx, serving as senior vice president of product. She recently spoke with SearchBusinessAnalytics about her vision for the Sisense platform and what it’s like being one of the few women helping shape software development at a major business intelligence and analytics vendor.

Artificial Intelligence, Real Enhancements, Better Insights

SearchBusinessAnalytics: What led you to the decision to leave Alteryx and move to Sisense to head the development of its analytics platform?

Ashley Kramer: What was really interesting to me about Sisense was the approach they took from the beginning, which was to give agility to customers. That means being able to do analytics the way that they need — the Periscope acquisition means it can be code first or drag-and-drop — and being able to allow people to leverage analytics wherever they need, whether that’s embedded deeply within a product, whether that’s in an internal portal, or whether that’s a dashboard that you deploy.

In addition, I have a lot of cloud experience, and I understand what works and what doesn’t, and Sisense took a really unique approach here a year ago to say, “Let’s rewrite our products to be more of a Linux microservices-based architecture,” so that as companies scale in the cloud and as they leverage different complex data scenarios, we can grow with them. We can meet them where they are today, and we can grow with them as they go into the future, and that was truly unique to me and super exciting.

SBA: What is the dynamic between you as chief product officer and chief technology officer Guy Boyangu?

AK: He deeply understands the architecture, both where it was and where it’s come, and what we collaborate on is around the AI space. We have something called the Knowledge Graph that gathers all kinds of intelligence so we can give our customers smartness out of the box when we deliver our analytics, so what Guy and I collaborate on is on the under-the-hood side of where Sisense goes next.

I’m the product strategist and visionary working with the entire team, and Guy is able to take that and say, “OK, technology-wise, this can be our strategy.” It’s a really good partnership that we have together.

SBA: As you come to Sisense, what is your vision for where the analytics platform will be in two or three years?

AK: There are a lot of places we can go, and we need to focus those efforts so we can make sure we’re delivering what customers need. But I can really break this down into further investments in three different areas.

First, how will we continue to scale across clouds? If you think about [many] of the other vendors in this space, they’re mostly part of a cloud ecosystem — Looker and Google, Tableau and Salesforce, Power BI always being part of Microsoft — and we’re a cross-cloud platform, we can work with data analytics cross-cloud, so we want to continue innovating there and scaling to make sure we work seamlessly with all of the different services that all of the different clouds provide. The second is more intelligence. How do we continue to do deeper smartness within the platform, so automated recommendations and automated decisions, alerting, those types of things. And of course we have natural language querying so you type a sentence and get your results. There are many more things we can do to make analytics easier for everybody involved and get more people involved in analytics.

And that brings me to the last one, which is if you look at statistics on the different usage and adoption of analytics it tends to be pretty low in general within organizations, and I truly believe that’s for one reason — most BI platforms assume people will unnaturally leave their everyday workflows and processes and look at a dashboard and then come back. With our API-driven platform and approach, we can bring analytics to the salesperson that spends their entire day in whatever sales platform or CRM (customer relationship management) platform they use, and for someone like me that’s always on the go, send it to my cellphone. By continuing to innovate in that area, I think we can start to see adoption go up and really start seeing organizations make data-driven decisions, and then of course as an end result have better business outcomes.

si whitepaper banner data deduplication 092720191 Artificial Intelligence, Real Enhancements, Better Insights

SBA: What are some trends you see in BI and analytics that Sisense is responding to and are helping drive Sisense’s roadmap?

AK: The first trend is we’re finally starting to see people migrate their data to the cloud. It’s been something people have been talking about, but we’re really seeing it come to life. The second is adoption — different statistics say that only about a third of an organization are actually using the analytics to make decisions. And the next one is a huge trend around AI and augmented analytics, so how do we not just allow people to make a decision about what’s happening right now, build a dashboard and see sales are down in the west, but how do we actually start to help them predict what to do next and prescribe how to take the action. A lot of the things we’re doing with our Knowledge Graph and a lot of the features we’ve released and have planned as part of our roadmap will start getting people out of just descriptive and taking them to those next two steps of predictive and prescriptive analytics.

SBA: How did your time at Oracle, Tableau, Alteryx, and other organizations prepare you for your new role at Sisense?

AK: My early career at Oracle and NASA was valuable because I was a programmer, so I understand technology pretty deeply, and I understand how sometimes a product team can dream up something but it might actually be a hard engineering challenge. That was helpful for me to understand how to be a better product leader. My time at Amazon helped me understand the crazy importance of the cloud. They were the early innovators in that area. Tableau was really interesting because Tableau started coming into the market with cloud analytics right at the cusp of when it was happening, so I got to learn a lot about the early market and then see how it evolved. And of course when you’re one of the earliest to arrive you don’t get everything right, and that’s okay. You learn lessons and figure out how to do it better next time. And at Alteryx it was trying to understand what the more data-science part of the world needs.

As a product leader it’s super important to understand what is possible for engineering and what is not, understanding the cloud is important because we’re cross-cloud here at Sisense, and then understanding where people want to go next with analytics by actually scaling it across the organization and taking that next step to be predictive. So, this really was the perfect role.

SBA: As chief product officer at Sisense you are now one of the few women helping shape the product development of a leading BI vendor. Did you face barriers on your path to Sisense?

AK: I was always interested in technology growing up — even in the early days, the Oregon Trail days. Fast-forward to high school and I was in the very first computer science class taught by my physical education teacher, which is crazy when you think about it. It doesn’t make any sense, but at least my school was thinking about offering computer science. I was a senior and the only woman in a class of 10 people. I was very fortunate to have people to encourage me to continue on this path. Move on to college, and in my graduating class there were three to five women in the class. Since then, I think what the world has done a better job of is have things like Girls Who Code, different programs to push the importance of STEM and make girls and women be a part of that. The world has changed and evolved, but it wasn’t really there when I was coming to my professional stage of my career.

SBA: What have you found following school in your professional life?

AK: As far as the glass ceiling, I feel like I’ve had a lot of great mentors along the way, some of them were women and some of them were men, and I think sometimes we undervalue the importance of what we can do for each other. Women can get together and I can go to every women leadership CPO dinner that exists, but we need men there too supporting us. We need them to be part of the conversation. Something that’s super attractive to me about Sisense is that not only is 50% of our core operational team reporting to the CEO women, but also [CEO] Amir Orad’s wife is a CEO [of LimitScreen Inc.]. He definitely understands the value of women in these leadership roles. Everyone has an equal voice, and I think that’s really powerful.

It’s been interesting. I’ve seen a trend with my generation and there are other women CPOs — there aren’t many, but even at my last company there was a woman chief strategy officer and we were really big supporters of each other. There was a historic thing in the past that there could only be one woman in the room, but that didn’t exist at any of the companies I’ve been. I feel fortunate to not have to have lived in that world. The more women in the room at leadership levels, the better, so I don’t feel like I have to speak up or be the alpha woman because that doesn’t exist anymore.

Editor’s note: This Q&A has been edited for clarity and conciseness.

packages CTA banners Cloud Data Teams Artificial Intelligence, Real Enhancements, Better Insights

Let’s block ads! (Why?)

Blog – Sisense

Isima emerges from stealth with $10 million to apply intelligence to real-time data

August 4, 2020 Big Data

Isima today emerged from stealth with $ 10 million in funding to launch a data convergence platform called BiOS. The company asserts its solution can reduce or even eliminate disparate databases while improving overall speed and reliability.

IDC expects the global big data analytics market to be worth $ 274.3 billion by 2022, and Forbes found in 2017 that over 50% of companies were adopting big data. This trend is likely attributable to the return on investment (ROI) adopters report. Entrepreneur notes that businesses using big data experience a profit increase of 8-10%, on average. But obstacles remain, with 40.3% of respondents to a Statista survey suggesting big data usage was held up by a lack of organizational agility.

With BiOS, organizations can collate business intelligence and AI-driven applications into a single platform to derive insights from real-time data. The “telco-grade” platform enables developers to build apps by adding data sources and deploying to production in a semi-automated fashion.

BiOS embeds software development kits (SDKs) within microservices, or systems that structure apps as collections of maintainable, testable, loosely coupled, and independently deployable services. It replaces third-party telemetry trackers with Isima’s first-party tracker to assure users that brands aren’t sharing their data without permission, and it offers “replica-free” insights that enable data analysts across a range of departments to collaborate on interdepartmental data without burdening relational database management systems. Off-the-shelf change-data-capture mechanisms wrap SDKs provided by BiOS to ingest changes.

Early adopters of the BiOS platform include PharmEasy, an Indian online pharmacy and medical supply store. Isima also claims to have subscription revenue from several Fortune 500 customers.

Isima was founded in 2016 by Monish Suvarna, who previously headed product development and portfolio licensing at private equity firm Intellectual Ventures. Cofounder Darshan Rawal is the former senior director of products at DataStax, where he led product management for DataStax’s commercial offerings based on open source projects like Apache Cassandra, SOLR, Spark, Titan, and OpsCenter.

Sway Ventures and Engineering Capital led the $ 10 million seed round in Silicon Valley-based Isima.

Isima competes with companies like Dremio, which has raised about $ 115 million for tools that help streamline and curate data, and Incorta, whose hyperconverged analytics platform helps companies garner insights by crunching vast swathes of data from across their cloud-based applications. Isima has another rival in Quantexa, which uses AI and machine learning tools to uncover risk and opportunities by providing a view of data in a single place, solving challenges across financial crime, customer intelligence, credit risk, and fraud.

Let’s block ads! (Why?)

Big Data – VentureBeat

Yann LeCun and Yoshua Bengio: Self-supervised learning is the key to human-level intelligence

May 3, 2020 Big Data

Self-supervised learning could lead to the creation of AI that’s more human-like in its reasoning, according to Turing Award winners Yoshua Bengio and Yann LeCun. Bengio, director at the Montreal Institute for Learning Algorithms, and LeCun, Facebook VP and chief AI scientist, spoke candidly about this and other research trends during a session at the International Conference on Learning Representation (ICLR) 2020, which took place online.

Supervised learning entails training an AI model on a labeled data set, and LeCun thinks it’ll play a diminishing role as self-supervised learning comes into wider use. Instead of relying on annotations, self-supervised learning algorithms generate labels from data by exposing relationships among the data’s parts, a step believed to be critical to achieving human-level intelligence.

“Most of what we learn as humans and most of what animals learn is in a self-supervised mode, not a reinforcement mode. It’s basically observing the world and interacting with it a little bit, mostly by observation in a test-independent way,” said LeCun. “This is the type of [learning] that we don’t know how to reproduce with machines.”

But uncertainty is a major barrier standing in the way of self-supervised learning’s success.

VB Transform 2020 Online – July 15-17, 2020: Join leading AI executives at VentureBeat’s AI event of the year. Register today and save 30% off digital access passes.

Distributions are tables of values — they link every possible value of a variable to the probability the value could occur. They represent uncertainty perfectly well where the variables are discrete, which is why architectures like Google’s BERT are so successful. Unfortunately, researchers haven’t yet discovered a way to usefully represent distributions where the variables are continuous — i.e., where they can be obtained only by measuring.

LeCun notes that one solution to the continuous distribution problem is energy-based models, which learn the mathematical elements of a data set and try to generate similar data sets. Historically, this form of generative modeling has been difficult to apply practically, but recent research suggests it can be adapted to scale across complex topologies.

For his part, Bengio believes AI has much to gain from the field of neuroscience, particularly its explorations of consciousness and conscious processing. (It goes both ways — some neuroscientists are using convolutional neural networks, a type of AI algorithm well-suited to image classification, as a model of the visual system’s ventral stream.) Bengio predicts that new studies will elucidate the way high-level semantic variables connect with how the brain processes information, including visual information. These variables are the kinds of things that humans communicate using language, and they could lead to an entirely new generation of deep learning models.

“There’s a lot of progress that could be achieved by bringing together things like grounded language learning, where we’re jointly trying to understand a model of the world and how high-level concepts are related to each other. This is a kind of joint distribution,” said Bengio. “I believe that human conscious processing is exploiting assumptions about how the world might change, which can be conveniently implemented as a high-level representation. Those changes can be explained by interventions, or … the explanation for what is changing — what we can see for ourselves because we come up with a sentence that explains the change.”

Another missing piece in the human-level intelligence puzzle is background knowledge. As LeCun explained, most humans can learn to drive a car in 30 hours because they’ve intuited a physical model about how the car behaves. By contrast, the reinforcement learning models deployed on today’s autonomous cars started from zero — they had to make thousands of mistakes before figuring out which decisions weren’t harmful.

“Obviously, we need to be able to learn models of the world, and that’s the whole reason for self-supervised learning — running predictive models of the world that would allow systems to learn really quickly by using this model,” said LeCun. “Conceptually, it’s fairly simple — except in uncertain environments where we can’t predict entirely.”

LeCun argues that even self-supervised learning and learnings from neurobiology won’t be enough to achieve artificial general intelligence (AGI), or the hypothetical intelligence of a machine with the capacity to understand or learn from any task. That’s because intelligence — even human intelligence — is very specialized, he says. “AGI does not exist — there is no such thing as general intelligence,” said LeCun. “We can talk about rat-level intelligence, cat-level intelligence, dog-level intelligence, or human-level intelligence, but not artificial general intelligence.”

But Bengio believes that eventually, machines will gain the ability to acquire all kinds of knowledge about the world without having to experience it, likely in the form of verbalizable knowledge.

“I think that’s a big advantage for humans, for example, or with respect to other animals,” he said. “Deep learning is scaling in a beautiful way, and that’s one of its greatest strengths, but I think that culture is a huge reason why we’re so intelligent and able to solve problems in the world … For AI to be useful in the real world, we’ll need to have machines that not just translate, but that actually understand natural language.”

Let’s block ads! (Why?)

Big Data – VentureBeat

Business Intelligence and Dynamics 365

April 8, 2020 Microsoft Dynamics CRM

In this blog, we will be discussing two of Microsoft’s top products, Power BI and Dynamics 365. We will touch upon what Power BI is, how we can integrate these two products and the many benefits that come from this integration! So, let’s begin!

What is Power BI?

In layman’s terms, Power BI is a newer term coined by Microsoft to put a name to all their business intelligence (BI) endeavors. It is a collection of business analytic tools that enable organizations and individuals to organize and display data in a meaningful way.

Microsoft’s Power BI is designed to enhance an organization’s data reporting and analysis capabilities, and it supports multiple sources. For this post, however, we will be solely discussing Dynamics 365. The integration of Power BI and Dynamics 365 was indeed inevitable, as together, these two services drastically increase an organization’s competitive advantage!

How can we use these two together, you ask? Well, there are many ways to use Power BI and Dynamics 365 simultaneously. In the case of Power BI, use the following steps:

Log in to your Power BI
Go to your data sources
Add Dynamics 365

Your data will be synced automatically, and you will now have multiple visualizations readily available.

In the case of Dynamics 365, use the following steps:

Open the Dynamics 365 settings
Navigate to the Reporting Tab
Check Allow Power BI visualization embedding.

Now you have embedded your Power BI tiles to your Dynamics 365 dashboard! That is some cool stuff going on there!

Why integrate Dynamics 365 with Power BI?

Now, you may wonder why you need to integrate your Dynamics 365 with Power BI. There are numerous benefits of doing so and we will be going over each one of them in detail.

Powerful and User-Friendly Workspace

Dynamics 365 allows users to execute routine tasks more effortlessly and effectively by empowering them with action hubs. They can use action hubs to create new activities, manage priorities, and analyze relationship insights. The user-friendly interface makes it easier for employees to understand and move about the workspace, which boosts employee productivity.

Furthermore, the combination of Dynamics 365 and Power BI allows companies to interact with customer data on the go and get meaningful insights. These insights then help companies develop and maintain positive business relationships with clients.

This powerful workspace digs deeper into analytics with empowering data features and allows for better task management.

It also helps develop better communication via email messages and shows the reactions and responses of contacts.

Additionally, it allows for customization through out-of-the-box features, thus helping employees and organizations create their own tailored analytics. With these powerful workspaces and the multiple insights, you can nurture and enhance all business relations.

Power BI integration allows you to augment your operations

The fact that Dynamics 365 and Power BI allow seamless integration makes it quite popular amongst other analytics options. There are multiple (pre-built and custom) content pack options that you can choose from in order to enhance your reporting capabilities. You can choose to make your custom content packs available for your customers as well!

You will be enjoying a complete view of all business users and important metrics through one unified dashboard. This data will be updated in real-time and will be easily accessible from all connected devices. Power BI online will allow you to build dashboards which means that you will always enjoy complete visibility into your data and reports. Furthermore, you will have many options to explore and analyze data in the dashboard using the many tools Power BI integration will bring you!

For example, you can use the SQL server database to build analytics and publish them into content packs!

Personalized Customer Experiences

You know what they say, getting new customers is far more costly than retaining old ones! Dynamics 365 contains multiple targeted applications and is also equipped with deep integration of Cortana Intelligence and Azure Machine Learning. All this allows for positive user-experience and highly nurtured relationships with clients.

With this integration, companies can focus on their customer relations by receiving reminders on their personal devices regarding meetings. Companies can also track sales orders, view information on invoices in real-time, and stay up to date on all customer operations. Additionally, intelligent customer analytics and predictive analytics allows companies to better understand their customers through relevant insights and trends. On top of that, all of this is maintained in one single dashboard! With this, you can put your customer first and ensure that none of their needs are ignored. Having happy, loyal customers was never this easy!

Safeguard Continuous Progress

Dynamics 365 is a suite of apps that are built with a plethora of powerful and intelligent tools. From the Cortona Intelligence suite to Power Apps, from Power BI to Machine Learning, Dynamics 365 has got you covered. With Dynamics 365, you get to truly understand your business and make data-driven decisions. By combining Power BI and Dynamics 365, companies empower themselves to make every touchpoint relevant so that they can respond with preemptive insights into the needs of their clients.

This allows for cost-saving and time efficiency which is a plus for all businesses! Resources are freed up so that the organization can focus on scaling thus safeguarding continuous growth for the company.

The purpose of this post was to enlighten you on the benefits of integrating your Dynamics CRM software with Power BI so that your company can gain a competitive edge and achieve all your goals easily. The combination of these two products is indeed powerful and allows companies to surface to the top with intelligent tools and many features. It is indeed a must-have! If you would like to know more about the pricing and technicalities of these products, visit the official websites! And if you have any questions for us, feel free to leave a comment below! We are more than happy to answer them!

Let’s block ads! (Why?)

CRM Software Blog | Dynamics 365

DAX Time intelligence and the 29th of February – #PowerBI

February 3, 2020 Self-Service BI

Yesterday I visited a client and was asked – how do the time intelligence functions handle the fact that February has 29 days in 2020.

Well – in fact there was a few surprises depending on what you select from you date table.

Let’s look as some examples – I will use the following Internet Sales Amount from the years 2011-2013 from Adventure Work Database where we in February 2012 have 29 days.

DAX Time intelligence and the 29th of February – #PowerBI

As you can see, we have the year 2012 where we have 29 days.

SAMEPERIODLASTYEAR()

In order to calculate Internet Sales Amount LY – I use the following

Internet Sales Amount LY = CALCULATE([Internet Sales Amount];SAMEPERIODLASTYEAR(DimDate[Date]))

Which works fine

DAX Time intelligence and the 29th of February – #PowerBI

But notice the behavior if we put dates or days numbers on the rows

DAX Time intelligence and the 29th of February – #PowerBI

SURPRISE – Internet Sales Amount LY will show the value for the 28^th of February 2011 instead of a blank value as you perhaps would expect

If you select year 2013 we will see this

DAX Time intelligence and the 29th of February – #PowerBI

The 29 of feb 2012 will “disappear” but the total for February will include the number.

DATEADD() – last year

If we use the function DATEADD instead – it will work exactly the same way.

IAS LY = CALCULATE([Internet Sales Amount];DATEADD(DimDate[Date];-1;YEAR))

DAX Time intelligence and the 29th of February – #PowerBI

DATEADD() – same day last year

If you want to compare the same Saturday (the 29^th of feb 2020 is a Saturday) last year – which is the 2nd of march we can do this by using the same DATEADD function but with different parameters

IAS LY same weekday = CALCULATE([Internet Sales Amount];DATEADD(DimDate[Date];-364;DAY))

DAX Time intelligence and the 29th of February – #PowerBI

This will compare the same day 52 weeks ago (52 * 7 = 364) and there by giving us the value from the 29^th of feb 2012 on the 27^th of feb 2013.

DATESMTD()

Now what about the function DATESMTD()

ISA MTD = CALCULATE([Internet Sales Amount];DATESMTD(DimDate[Date]))

ISA MTD LY = CALCULATE([Internet Sales Amount LY];DATESMTD(DimDate[Date]))

These functions will calculate the running total for the month for the given day number

DAX Time intelligence and the 29th of February – #PowerBI

Notice that the ISA MTD works fine in 2012 for the 29^th and the LY measure will show the same result for the 28^th and 29^th in 2012 – and in 2013 it will actually for the 28^th show the sum of both the 28 and 29^th

Conclusion

You might find that some users find it difficult to understand how the calculations works when the look at dates instead of month totals especially in the case where they will get the same value for LY on both the 28 and 29^th in 2012/2020.

If you compare cross years on calendar dates I find the result that SAMEPERIODLASTYEAR() returns makes better sense than leaving it empty/blank but what do you or your users think. Let me know in the comments.

Hope you find this little walkthrough useful.

And remember to ALWAYS use a datetable in your model if you do time intelligence calculations in DAX.

Let’s block ads! (Why?)

Erik Svensen – Blog about Power BI, Power Apps, Power Query

Weaving Business Intelligence into the fabric of the organization with Microsoft Power BI

November 5, 2019 Self-Service BI

Our customers recognize that in today’s digital economy, data is everywhere – enabling every single employee in their organization to harness the power of all of that data is critical to their competitive advantage.

The Power BI team has a single goal: to enable organizations to embrace a data culture by allowing everyone to make data-driven decisions through beautiful, friendly, and easy to use analytics.

As we kickoff Ignite in Orlando this week, I’m really excited to announce a set of new capabilities in Power BI that help our customers weave business intelligence deep into the fabric of their organizations.

World-class Microsoft security capabilities help protect data

Power BI plays a key role in bringing data insights to everyone in an organization. However, as data becomes more accessible to inform decisions, risk of accidental oversharing or misuse of business-critical information can increase.

Microsoft has world-class security capabilities to help protect customers from threats. Over 3,500 security researchers along with sophisticated AI models reason every day over 6.5+ trillion signals globally to help protect customers against threats at Microsoft.

We are excited to announce new data protection capabilities in Power BI that build on Microsoft’s strengths in security and enable customers to empower every user with Power BI and better protect their data no matter how or where it is accessed.

Classify and label sensitive Power BI data using the familiar Microsoft Information Protection sensitivity labels used in Office.
Enforce governance policies even when Power BI content is exported to Excel, PowerPoint, or PDF, to help ensure data is protected even when it leaves Power BI.
Monitor and protect user activity on sensitive data in real time with alerts, session monitoring, and risk remediation using Microsoft Cloud App Security.
Empower security administrators who use data protection reports and security investigation capabilities with Microsoft Cloud App Security to enhance organizational oversight.

Weaving Business Intelligence into the fabric of the organization with Microsoft Power BI — New protection metrics in the Power BI admin center

Now in public preview, the capabilities are engaged when Power BI is paired with Microsoft Information Protection and Microsoft Cloud App Security.

These updates, combined with the just announced data lineage view, show the strategic importance of incorporating Enterprise Information Management (EIM) capabilities into Power BI.

Power BI integrated with Azure Synapse Analytics and large models public preview

Today, Microsoft announced Azure Synapse Analytics, the next generation of Azure SQL Data Warehouse. With Azure Synapse, big data and SQL data professionals can collaborate, manage, and analyze your most important data with ease—all within the same service.

We’re excited to announce that Power BI is natively integrated into Azure Synapse Analytics.

Customers can link Power BI and Azure Synapse workspaces together providing them with a single pane of glass to develop and manage their end to end analytics infrastructure.
BI professionals can create enterprise grade semantic models from the Synapse workspace through integration with Power BI Desktop – which helps customers define their KPIs and business logic, apply role based security, and provides a blazingly fast in-memory cache.
Customers can build Power BI reports directly from within the Synapse workspace and make these reports available to their end users.

We’re also announcing the availability today of the public preview of large in-memory semantic models. As customers work with massive data volumes they will be able to create data models that go up to 400GB of compressed data, taking advantage of all the memory they have in their Power BI Premium capacity. This increase in model sizes unleashes endless possibilities for enterprise models and in conjunction with certified and promoted datasets, enables organization to have one, enterprise-grade version of the truth.

New AI driven augmented analytics and machine learning experiences

Power BI is a pioneer when it comes to bringing AI experiences into business intelligence with capabilities that empower end users, business analysts, and data scientists. Today we are announcing new experiences directly built into Power BI and Power BI Premium with no additional purchase necessary.

Decomposition Tree

Our community voted and we’ve acted – the decomposition tree idea has over 4,500 votes on ideas.powerbi.com. We are super excited to announce the preview of the decomposition tree visual will ship in the November Power BI Desktop release next week. This visual helps users understand the root cause that contributes to a high or low KPI value broken down by one or more dimensions. The AI component of the decomposition tree is the choice to automatically find the most interesting splits and conduct the root cause analysis for you, instead of splitting manually by dimensions.

Automated ML General Availability

Last week, Automated ML in Power BI was made generally available. With Automated ML, business analysts can build machine learning models to solve business problems that once required data scientist skillsets. Power BI automates most of the data science workflow to create the ML models, all while providing you full visibility into the process.

Cognitive Services in Power BI Desktop (Preview)

The integration with Azure Cognitive Services allows users to better engage with unstructured data by detecting language, scoring sentiment, extracting key phrases, and identifying objects in images. Cognitive Services is available today in Power BI Premium and can be accessed from Power BI dataflows. As of next week, these capabilities will accessible directly from Power BI Desktop.

Power BI integrated more deeply than ever with Microsoft Teams

Effortless communication using data enables everyone to make fast and accurate decisions. Today, we’re excited to announce deeper integrations with Microsoft Teams are coming soon. They’ll help your organization harness data effectively as individuals work together to achieve shared goals.

Starting in late 2019, we will ship a new tab for Microsoft Teams that supports the new workspace experience and reports in organizational apps. This gives all your team members access to data without leaving Microsoft Teams
In 2020 we’ll go further by releasing rich previews of Power BI reports directly within Teams channels and chats. We’ll include action button so everyone can quickly act on the data.

SQL Server 2019 Analysis Services and Reporting Services

With the general availability announcement of SQL Server 2019, SQL Server 2019 Analysis Services and Reporting Services are now generally available. This release is packed full of new features for both products, including calculation groups in SSAS for calculation reusability, governance settings to protect server resources, and enhanced accessibility options for SSRS. The full list of features in the SQL Server 2019 release, including those for both SSRS and SSAS, are available in the What’s New in SQL Server 2019 documentation online.

See you at Ignite!

With all these exciting innovations, the Power BI team and I are looking forward to seeing many of our customers and community members at Ignite this week. If you are attending Ignite, please attend the “Microsoft Power BI: Business intelligence strategy, vision, and roadmap update”. You can find the full set of Power BI sessions here.

Let’s block ads! (Why?)

Microsoft Power BI Blog | Microsoft Power BI

The Future Of Transportation Relies On Intelligence

October 15, 2019 BI News and Info

With each passing day, the transportation and logistics industry grows more and more complex. Customer expectations are changing constantly, workforces are shifting, and technology is advancing. In response, successful transportation companies are relying on intelligence to provide them with the tools and tactics they need to create security during uncertain times.

If you’ve already undergone a digital transformation, it’s likely that you have already integrated some form of intelligent technology. Now is the time to take it a step further – when you embed intelligence into your business processes across the enterprise, you gain the vision and ability you need to withstand change. And that’s vital in this digital age, where nothing stays the same for long.

Intelligence meets evolving customer demands

Customers today are empowered by technology. As they increasingly use intelligent technology in their own personal lives, they expect those same capabilities to extend everywhere else. That means it’s not enough for deliveries to be on schedule – now, customers need to know the status and location of their order at all times. Intelligence into your own processes and supply chain can support the always-on mentality of modern customers, and you can gain that with the help of intelligent technology. Today, innovative transportation companies are using IoT-powered devices to track orders from placement to delivery.

Intelligence supports customers throughout their entire experience with your company. For instance, intelligent service ticket applications, powered by AI and machine learning, can automatically process and classify social media posts, emails, and other customer interactions, and then route them to the correct resource. This cuts down on the amount of time customers spend waiting for help, as well as the amount of time your employees spend manually routing and rerouting customer inquiries.

Intelligence enables improved employee management

A well-managed workforce has always been a key to success, and that hasn’t changed in this digital era. In fact, intelligent technology makes employee management and enablement easier than ever. Intelligent resume-matching applications are automating the talent-screening process, so recruiters spend less time matching candidates to the job description. This increases workforce productivity and reduces potential hiring biases.

You can also develop flexible applications that can meet the evolving needs of employees, providing them with on-the-go access to employee portals and learning content. And the same AI-powered chatbots and technology that you provide to your customers can be extended to your own employees.

Intelligence supports key business goals

By better serving customers and increasing employee enablement, you are likely to come a step closer to meeting some of your key business goals, be they reduced costs or greater productivity. But you can also use intelligence to gain insight into how your business processes are performing and whether you’re meeting your goals. From there, you can embed intelligence throughout your business processes to achieve them.

Most transportation businesses have masses of business insights stored away in lines of text, code, work orders, or notifications, especially after undergoing a digital transformation. Intelligence can help you process these overwhelming volumes of data, turning them into actionable insights that you can use to make real decisions about the business.

When you embed and combine intelligent technologies such as AI, machine learning, the IoT, and blockchain, you gain the power to meet customer and employee demand, as well as your key business goals.

To find out more about how next-generation technology from SAP supports the unique needs of transportation companies like yours, read the new SAP Leonardo for Transportation eBook.

Let’s block ads! (Why?)

Digitalist Magazine

Why Intelligence Is The New Oil For The Future Enterprise

October 8, 2019 BI News and Info

A perspective from Southern Europe, Middle East, and Africa

This blog is part of the “Digital Platform” series that explores how organizations can rethink their IT infrastructure to take advantage of the increased efficiency, automation, and agility enabled by the latest intelligent technologies.

Much has been said about data being the new oil that will fuel businesses in the future. But the reality is that it’s not about the amount of data that organizations have access to, but how effective their decisions are based on that data. Therefore, data alone will not be the new oil, rather intelligence will fuel the digital economy, and organizations that can create intelligent business processes to deliver contextualized and consent-based experiences will win in this new data-driven world.

As intelligence (and technology in general) increasingly underpins future business models, there is a big question around the role of IT moving forward. The CIO needs to be part of boardroom discussions driving decision-making around the broader business (and digital) strategy. This raises yet another debate about the role of the CIO in this context.

IDC believes that in the emerging set of “tech stakeholders” highlighted in the digital dream team below, the CIO clearly has an important role to play: as an orchestrator of budgets, technologies, and the teams (business and IT) to deliver on the organization’s digital vision.

Lacerda blog Why Intelligence Is The New Oil For The Future Enterprise

Moving toward an obsession with intelligent business processes

IDC’s research shows that 92% of CIOs are aiming to be either “strategic” or “disruptive” in their organizations over the next three to five years in order to deliver an enterprise-wide digital transformation agenda. The biggest challenge on this journey is the fact that the amount of information stored within an enterprise is growing at approximately 40% every year, and external data pipelines are growing exponentially. In this context, it is no surprise that the transformation of information into actionable intelligence will be the difference between winning and losing in the digital era. Therefore, CIOs need to move away from an obsession with technology to an obsession with the intelligent business processes that link to the digital dream team. But the language needs to change. For example:

An IoT project should be about delivering visibility across the design-to-operate business process, led by the head of supply chain.

Blockchain can help build trust in an ecosystem of suppliers in the source-to-pay process, led by the CFO.

Machine learning should focus more on recommendations to increase sales in the lead-to-cash process, driven by the head of sales and customer experience.

Let’s take, for example, the design-to-operate intelligent process highlighted below. In the digital era, there is a requirement for a centralized data management platform to create a single version of truth integrating data from all the systems, business applications, and digital apps underpinning this business process. This, in turn, is fed by data pipelines from external sources that need to be incorporated in real time across the process, driving specific actions, either automated or guided by intelligent technologies (typically AI/ML-based). Finally, that single version of the truth needs to be fleshed out via an end-to-end analytics layer providing real-time, end-to-end visibility to the various members of the digital dream team.

Driving the business-process orchestration strategy

In parallel, there is clearly an emerging role in a digital platform architecture to drive “moments of truth” into these intelligent business processes. The next phase of the application landscape evolution is going to all be about integration, and IDC believes that integration and extension capabilities are critical to drive the business-process orchestration strategy enabling future business processes. This platform will provide a unified set of connection points across multiple systems’ applications and digital apps that might be distributed across on-premises, hosted, or public clouds. Based on these capabilities, the digital platform can be used to fast-track integration and trigger new workflows/business processes, leveraging applications that could be a mix of legacy on-premises and SaaS applications.

Let’s move into a real-life scenario to highlight how this could potentially play out. Imagine for a moment that you are running the operational “cockpit” within an airport. Data triggers are constantly flowing across the operational business processes in the form of flight arrivals or departures, parking lot capacity, movement of assets (e.g., terminal facilities and vehicles), security waiting times, and real-time sales volumes in the retail shops. If incorporated into the “moments of truth” via intelligent technologies for operational managers, they can help drive real-time decision-making, such as preventing a flight delay by informing a gate manager there is a problem with a boarding bridge. This is exactly the sort of example that highlights how technology investments can deliver clear financial outcomes when innovation and agility are brought to the core of the enterprise.

Dramatically shifting mindsets

IDC’s research shows that 65% of CEOs in Europe are under considerable pressure to deliver a successful digital transformation strategy. Over the past few years, organizations have been making massive technology investments to transform their businesses. However, to deliver the associated financial outcomes linked to those investments, there must be a dramatic shift in the mindset. We need to move from technologies, projects, and resources to business outcomes, products, and talent. Technology is just a means to an end. It needs to enable business value in those moments of truth across the intelligent business processes that define scale in the digital era.

Please join this live Webinar Tuesday, Oct. 29 to hear from Mustafa Mustafa of Ferrara Candy Company as he shares his insights and experiences. These include the company’s strategic imperatives, project goals, deployment of SAP HANA and the quantified business value, including a 90% reduction in time to build data models. Register here.

And please learn more about SAP database management offerings and read the “Predictive Analytics and Machine Learning” series.

Let’s block ads! (Why?)

Digitalist Magazine

Tag Archives: Intelligence

Search

Data at scale

Google Assistant

Finishing thoughts

Is your Data Private?

Why are Artificial Intelligence and Data Privacy a Big Concern?

The Cambridge Analytica-Facebook Scandal

Clearview Face Recognition Scandal

DeepFakes

Mass Surveillance in China

How to prevent misuse of data with AI

Government Responsibility

Company Responsibility

AI Community Responsibility

User’s Responsibility

Conclusion

What is Power BI?

Why integrate Dynamics 365 with Power BI?

Powerful and User-Friendly Workspace

Power BI integration allows you to augment your operations

Personalized Customer Experiences

Safeguard Continuous Progress

SAMEPERIODLASTYEAR()

DATEADD() – last year

DATEADD() – same day last year

Conclusion

World-class Microsoft security capabilities help protect data

Power BI integrated with Azure Synapse Analytics and large models public preview

New AI driven augmented analytics and machine learning experiences

Decomposition Tree

Automated ML General Availability

Cognitive Services in Power BI Desktop (Preview)

Power BI integrated more deeply than ever with Microsoft Teams

SQL Server 2019 Analysis Services and Reporting Services

See you at Ignite!

Intelligence meets evolving customer demands

Intelligence enables improved employee management

Intelligence supports key business goals

Moving toward an obsession with intelligent business processes

Driving the business-process orchestration strategy

Dramatically shifting mindsets

Recent Posts

Categories

Archives