SHRM CEO, Hank JacksonBill Gates once said, “Microsoft is always two years away from failure.” This statement is a reminder of the competitive reality that businesses face and a powerful motivator to innovate. Judging from the company’s perch at the top of industry lists—second most valuable brand in the world, according to Forbes, and No. 34 on the Fortune 500—it’s safe to say the tech company has done its share of innovating since it was founded 40 years ago.Today, as HR professionals, we are called on to create and embrace this culture of imagination, while fostering smart risk-taking and developing a staff that stays one step ahead of the next disruption. To do this, we must continually re-examine some of the old ways of doing business. We must improve what is working and take a critical look at what is not. In an environment where we must innovate or fail, adapt or die, we must rethink even some of the most basic people management practices.This month, you will see this theme of looking at old things in new ways throughout HR Magazine. Click here to read more.
A new Israeli startup hopes to upend the dominance of mega-companies like Amazon and Wal-Mart over local businesses. The idea has been to give small organizations free, consumer-grade tools to manage themselves and connect with each other. Starting Wednesday, SohoOS’ new App Store will bring third-party paid apps to the mix as well. At first glance, SohoOS may seem like yet-another Software-as-a-Service (SaaS) startup offering business management and collaboration web applications to small businesses. Yawn.But this is more than just a few editing and CRM apps packaged together on a pretty website: SohoOS is a highly automated system aimed at the microbusiness segment, a channel that doesn’t always get the attention it deserves. And with the new App Store, it’s also a marketplace for additional small business services.Targeting MicrobusinessesSohoOS’s mission is aimed right at that small business sector, delivering business management tools to try to automate as much as possible for busy small business owners and entrepreneurs. And SohoOS means small: businesses with maybe one or two computers, rather than the typical SMB (small and medium business) model of 10-50 machines.Why target the really little guys? SohoOS founder and CEO Ron Daniel argues that micro-businesses often don’t have the luxury to relegate business management tasks to one specific employee.“It can be someone like a single mom, running a designer business out of her home,” Daniel explains. “She needs to be a designer, not taking up her time managing the business.”To address this problem, SohoOS provides the prerequisite business management tools: document management, contact management, collaborative tools and the like. But then it goes a step further, automating the basic steps that happen during certain events.Create an invoice for a new customer, for example, and the customer’s information is added to the contact system and any items on the invoice are automatically removed from the business inventory. “We are trying to consumerize the management platform,” Daniel said.SohoOS’ model has had some traction: the Tel Aviv-based vendor has almost 850,000 customers signed up since it started in December 2011. Daniel expects that number to top a million soon. brian proffitt A Web Developer’s New Best Friend is the AI Wai… A New App Store To expand the platform’s appeal, Wednesday the company announced the addition of a new app store designed to let users buy add ons to SohoOS, as well as tap into subscriptions to business-oriented sites and services tailored for micro-businesses.Apps available at launch time include personalized data backup controls; a leads widget to push leads to users; and additional document, invoice and Web themes.Differentiating between “free” and “paid” was a critical distinction for SohoOS, and was the vendor’s primary reason for moving to the App Store model. The company did consider affiliations and similar models with partner vendors, but ultimately opted away from those plans because they worried those approaches could lead to fewer choices for its customers.“We wanted customers to have the widest available options, and be able to cherry pick what they needed from that selection,” Daniel explained.Creating a CommunityUltimately, SohoOS hopes to turn the SohoOS customer base into a community resource, allowing participating businesses to share information, expertise and resources to create powerful B2B connections.For example, “small yoga shops could sell combined discounts for customers who travel,” Daniel said. Or a retail store could help sell another company’s excess inventory. combine expertise, resources. These scenarios would give independent owners more leverage against larger brands, Daniel said. Tags:#apps#biz Why Tech Companies Need Simpler Terms of Servic… Related Posts Top Reasons to Go With Managed WordPress Hosting 8 Best WordPress Hosting Solutions on the Market
What it Takes to Build a Highly Secure FinTech … Related Posts Perhaps the best thing that has ever happened to photography is the advent of digital technology. The world has long since left analog and chemical photo editing procedures behind for digital photo editing software. And digital photography has brought the craft to the hands of amateurs. But, how is technology helping reshape photo editing software?While Photoshop remains the household name and even synonym for photo editing, many other applications, especially top photo editing software for beginners have risen to the challenge as photography gets increasingly advanced. Following digital technology, the next big thing appears to be Artificial Intelligence. An AI-powered photo editing software uses machine learning to enhance images and manipulate them in various regards. Therefore, the ways described in this article are related to AI in one way or the other. Even innovations from the digital revolution are being improved upon. One-Touch Editing. Photoshop seems cumbersome and complex for many, even professional photographers. This might have been the idea behind alternative Adobe photo editing software like Lightroom. Nevertheless, many software has arisen to rival Photoshop in offering one-touch editing where adjustments that would have taken hours are made in seconds and minutes, of course, with AI at play. This is not unexpected, the world is running faster. Luminar by Skylum is a good photo editing software that gives its users an edge with its Accidental AI. It enables users to adjust a wide range of features by using a slider. Its preset styles make this possible too. The app is also available as a plugin in Photoshop, Lightroom and Apple Photos. Movavi Photo Editor is an easy photo editing software that prides itself on one-touch photo enhancement. It conveniently combines its simplicity with high-end sophistication, ensuring that quality is not compromised. Photolemur is an easy image editor that comes with an auto image editing feature. It does this with the aid of algorithms. Face Beauty Effects.Since the rise of selfies, photo editing software has been heavily invested in the face beauty trend. Most of the applications, especially mobile ones, are only for amateurs. However, there are professional photo editing software that relies on strong AI-driven facial recognition technology to create more realistic effects. Skylum recently released an update to the Luminar photo editing software that featured a new Accent AI 2.0 technology dubbed ‘human-aware’. Using facial and object recognition technology, it is capable of creating better photo effects. It removes the hassle of individually adjusting effects like contrast, shadows, highlights, color correction and so on. Its strong point is that it still affords photographers a lot of flexibility with editing. Photolemur has what is called Face Finish Technology. It uses AI (too) to analyze image details and corrects imperfections at once. Typical face editing features like teeth whitening and eye correction are embedded in this photo editing software. Prisma, one of the top photo editing software designed Lensa as an app to retouch selfies. It has a lot of face beauty features such as skin tone smoothing, eye/eyebrow effects, texture and so on. It also features innovative features like Bokeh that is similar to the iPhone’s portrait mode. Style Effects.Most photo editing software now offers customized styles that apply various effects for you, especially when you do not have much time for editing. These quick effects make it faster and easier to edit a photo and are great for amateurs who don’t want all the complexity of typical photo editing apps. Prisma photo editing software (on Android and iOS) gave rise to artistic effects in photos. It uses AI to transform typical photos even artworks that they appear like paintings, mosaics sketches, etc. Since its release in 2016, it has gained widespread adoption among many and has come to be known as a primary art app. GoArt by Fotor transforms photos into artistic masterpieces with AI. It can make your photos imitate mainstream art styles such as impressionism, abstract, fauvism, dadaism, etc. and even make photos look like they are straight out of Van Gogh’s studio. Painnt is another photo editing software that is art-driven. It features several styles that are further divided into categories for easy use. Photo Upscaling. Humans are insatiable. And while our cameras have read unrealistic resolutions and keep going higher, we still need more. This desire has birthed photo upscaling apps that use AI to increase photo resolution while preserving quality and is great in the case of photo printing. Topaz Labs claims that its Gigapixel AI which is not exactly a full-fledged photo editing software can enlarge photos and upscale them to 600% while preserving detail, using machine learning to analyze the images pixel-by-pixel.Bigjpg uses the latest Deep Convolutional Neural Networks technology to reduce noise and increase the quality of images with the photos themselves retaining their excellent quality. Letsenhance.io is a web application that does as its name says: enhances pictures. It promises it can upscale images 16x the original. It rivals many mainstream photo editing software in this regard. Conclusion As mentioned earlier, the current wave of photo editing software trends is motivated by AI and is bound to continue so. Keep in mind that all the software mentioned are just for illustration purposes: they may not necessarily be the best but show how technology has impacted photo editing. As the niche evolves, digital photography is set to take a turn around. What will we would do with pictures in 2020? Will they be radically different than what was obtainable decades ago. Tags:#AI#Digital Photography Michael Usiagwu AI: How it’s Impacting Surveillance Data Storage Follow the Puck Michael Usiagwu is the CEO of Visible links Pro, a premium Digital Marketing Agency committed to seeing your brands/company and products gain the right visibility on the search engine. He can be reached via [email protected] Why IoT Apps are Eating Device Interfaces
How Data Analytics Can Save Lives AI: How it’s Impacting Surveillance Data Storage Related Posts Leveraging Big Data that Data Websites Should T… Today we navigate our way across cities, pull up electronic tickets, purchase items, monitor our health, and, of course, stay connected with friends and family on our smartphones. The smartphone is one of those innovations that make us think, “how did I ever function without it?” Smartphones revolutionized our personal lives, but there’s a megatrend set to disrupt the business world; it’s called augmented analytics.Augmented analytics is on the cusp of becoming the business world’s next significant evolution.Gartner identified augmented analytics as to the number 1 top trend for data and analytics technology in 2019, and market leaders are already starting to invest in this burgeoning industry.SAP recently acquired augmented people analytics company Qualtrics for $8 billion, shelling out a price equivalent to over 20x the company’s current revenue. A newcomer to the game, Denver based startup Nodin raised $5 million in funding this past March, a month before even launching its platform.The global market for augmented analytics is forecasted to reach $29.86 billion by 2025. But just what is augmented analytics, and what makes it such a hot new trend?Data or dieAccording to a recent study by Forbes Insights and Treasure Data, only 13% of companies can be considered “leaders” in leveraging the full potential of their customer data. The full potential of the customer data is significant, as 55% of executives think these insights to be valuable in achieving disruptive innovation.Companies must now collect, clean, and translate their raw data into insights they can use to build better products and reach target audiences.In today’s fast-paced business world, data-driven decisions are no longer a nice to have; they’re a necessity to stay competitive and on top of market volatility. To get ahead, significant players from Booking.com to PepsiCo are relying on teams of data analysts to collect, clean, and analyze the surge of data now being generated.SME’s are also leveraging their data to gain a competitive advantage in a sea of new competitors popping up every day. The problem is that data analysts are not only scarce in number; they’re also costly, especially for SMEs.Even for companies that do have data scientists on board, the sheer volume of the data we’re now collecting through various platforms and tools means that they spend more of their time on activities like data preparation and visualization, leaving less time for actual analysis.Augmented analytics harnesses the power of AI and machine learning to automate these tasks and generate insights.Let’s say you’re an ecommerce store that’s seen a sudden decrease in sales on your Shopify account. To find out why you’d have to comb through your company’s data and find insights by:Logging in to Google Analytics to analyze patterns in your website traffic.Checking out the performance of your social media accounts and ad campaigns.Reassessing your keywords on Google Adwords.Investigating new competitors or changes in the market.Instead, augmented analytics tools collect and analyze all your data together to identify potential causes and automatically generate reports with actionable insights.Here are three significant ways augmented analytics will disrupt the business world:We’re in a data race – the winner takes the money.With most businesses adopting artificial decision-making capabilities, we’re now in a race to see who can make the faster, better business decisions. Our businesses are like data-guzzling V12 engines that need data to fuel growth. Automating this process, and using augmented analytics to spot growth opportunities in your data, before your competitors, means you win the race.Gartner believes that by 2020, over 40% of data science tasks will be automated. The automation will allow data scientists to spend less time on repetitive tasks and more time on strategic analysis and decision-making. Not only does it take the manual labor out of their job, but it also does it faster and eliminates the potential for human error.Bring together the whole picture.At the moment, most company’s data lives on several different platforms – isolated. Only 34% of executives agreed they have one aggregated view of all their customer data points. Not only is this inefficient, but it also blocks businesses from making informed decisions. We shouldn’t be looking at how each part of the engine works separately but how it all works together.Having data points integrated into a rapid reporting system, such as Aerialscoop or DataBox, allows you to track the entire customer journey on one platform, all the way from lead generation until earning your first Dollar from the client. It also provides for better cohesion and collaboration across the organization. It’s not just ‘how is my marketing team doing on their KPIs?’ — but how are the marketing team’s results directly impacting my revenue growth and retention rates?Democratize your data analytics.Meanwhile, for smaller companies that don’t have the means to hire a team of data scientists (currently the global average salary is $90k), augmented analytics will make data-driven insights accessible to the masses. The accessibility is expected to be a major wave of development for the next five years.According to Gartner, through 2020, the number of citizen data scientists will grow five times faster than professional data scientists. This means everyone from executives to marketeers will have the power to make data-driven decisions, without having to rely on data science professionals to provide the information they need.Having the information easily accessible to all opens doors for SME’s to accelerate their growth at an exponential rate across departments. If there was ever a time that smaller, more nimble start-ups were able to pose a real threat to major companies, the democratization of data analytics ought to be the catalyst.Much like smartphones have become the tool we can’t imagine our lives without, augmented analytics will set a new standard for business growth.Those who start to leverage this technology early on will reap the benefits that faster, aggregated, and accessible data can bring. Where will your company stand in the data race of the future? Tags:#Augmented Analytics#Machine Learning Natan Pollack A Web Developer’s New Best Friend is the AI Wai…
To stay competitive in a digital economy, businesses increasingly need to move beyond simple reporting and descriptive analytics to a more predictive approach that puts artificial intelligence (AI) strategies to work to engage with customers in new ways.So how can you find a practical way to start applying AI in your business? One path forward follows three steps: leverage predictive models to improve how you engage with customers, put machine learning to work to improve those models, and then validate your models. In an earlier blog, I explored the dynamics of predictive analytics and machine learning. In this post, I will focus on the validation of predictive models First let me provide a quick overview of predictive analytics and machine learning, and explain why validation is important when you apply these approaches.Predictive analyticsPredictive analytics is about using algorithms to predict the result of a measurement that you can’t make, based on measurements that you can make. Why can’t you make the measurements you need? Perhaps you are trying to predict what’s going to happen next. Unless you have a time machine (in which case you are probably wasting your time on analytics!), you can’t measure something that hasn’t happened yet. Forecasting customer behavior, business trends and future events is always of value in running a business.Often, it is not possible to measure something in the present because of practical considerations. For example, suppose you want to present a lunch ordering application for your chain of lunchtime food delivery restaurants. Your best chance of getting someone’s attention is when they are hungry and online. Obviously, it’s not possible to ask everyone who is online whether they are hungry, so you need to infer their hunger status from their behavior: Is it lunchtime? Are they looking up lunch options? The goal of your predictive analytics in this case is to infer who is most likely to respond to your product or offer, based on the data you are able to collect.In general, your predictive analytics application can take into account a customer’s past account history, past conversations with the call center, behavior of “similar” customers, the location of the customer, and even what’s trending on social media at any given time. Good predictive analytics will give you the best chance of a mutually beneficial interaction with your customer.The challenge is that, compared with diagnostic and descriptive analytics, predictive analytics is a new world. You are actually making predictions or inferences based on past data. To be successful at this, and to avoid making grossly inaccurate predictions (or at least understand how accurate your predictions may be), you will need to validate your models to ensure that you have discovered useful, generalizable patterns in your data.Machine learning as part of a predictive analytics systemSo how do we build a good predictive analytics application? Two words: Machine learning (ML). Predictive analytics leverages machine learning algorithms that build systems that learn iteratively from data, identify patterns, and predict future results. Machine learning algorithms organize things into meaningful groups, find unusual patterns in data, and can predict the next data point in a time series.There’s an important caveat to call out here. Machine Learning algorithms learn from data, but on their own they are not great at distinguishing between memorizing past data and finding generalizable underlying patterns in data. When learning from past data for predictive analytics the goal is to generalize, not memorize. Poorly constructed ML algorithms can memorize all of the data in a huge data set, resulting in a system that is very poor at predicting the outcome of any situation they haven’t already seen in the past. Instead, you need to train ML algorithms to focus on a limited number of free parameters that enable reliable predictions about the future. ML algorithms that generalize well are called “robust” algorithms.The Importance of ValidationGiven the concerns described above, how do you know if you can trust the results generated by a ML algorithm? You need to validate your predictive models.Validation is the automated process of looking at all the data you have in different ways to determine how robust your predictive models are likely to be. To understand why validation is important, let’s look at the process of creating predictive analytics from data using machine learning models. This will help us see why not all models are created equal.One way to proceed would be to create a model that you think is predictive, try it out for a while, and then see if it actually works. But there’s a big downside here. If the model doesn’t work, you could end up paying a big price in terms of lost opportunities and misguided business strategies. In practice, you will monitor the performance of your model in the “real world,” but you don’t want to only rely on that approach alone.A better way to validate is to split your available data into a training set and a test set. You will use the training set to train your ML algorithm to predict the known outcomes in your training data. You then try out the model on the data in the test set to see how well your predictions match the actual outcomes in the test set.This gives you a number of quantitative performance measures for your ML model. If your model has poor performance on the test set, then you know that the ML model you came up with did not discover a generalizable pattern in your data and you will need to change your assumptions and build another model.Going forward, you can tweak your model to try different training and test set data partitions and repeat the validation sequence multiple times—define your training set, train your algorithm, test your algorithm. Most ML libraries, like Intel® Data Analytics Acceleration LibraryOpens in a new window (Intel® DAAL) can automate this process for you using a variety of methods called “cross validation.” Cross validation enhances the likelihood that your algorithm will be reliable and robust.Tools for the Data ScientistIntel offers various software technologies and hardware products to support the efforts of data scientists and application developers who want to use ML methodologies to extract value from huge datasets. Let’s take a few examples.Intel spearheaded the initiative to create the open source Trusted Analytics Platform. TAP accelerates the creation of advanced analytics and machine learning solutions by streamlining the process of assembling big data tools and by automating the many steps required to build and publish big data applications. It’s a key platform for putting powerful analytics into the hands of every business decision maker. For a fuller explanation, see my earlier post discussing three attributes of TAP.Intel also offers access to a range of open-source frameworks for machine and deep learning, as well as code and reference architectures for distributed training and scoring. You can explore these resources on our Machine Learning site.And then there is also the hardware side of the story. The Intel® Xeon® processor E7 v4 family delivers the processing performance and large memory capacities required for real-time analytics on huge datasets. It’s ready for the largest high-volume workloads, like those in healthcare, energy, financial trading, and logistics applications.Intel non-volatile memory (NVM), including Intel® Optane™ technology, speeds things up even more. It can greatly accelerate machine learning systems by reducing memory latency to a matter of just tens of nanoseconds. These are the kind of breakthroughs you can achieve with new 3D XPoint™ technology, which brings non-volatile memory speeds up to 1,000 times faster than NAND, the most popular non-volatile memory in the marketplace today.The main takeaway for a data scientist to achieve success in predictive analytics, if you’re using a predictive model, you want to be confident that it is both reliable and robust. The process of validating your ML model gives you this confidence.