There is an explosion of interest in data science today. One just needs to insert the tag-line ‘Powered-by-AI’, and anything sells. But, that’s where the problems begin. Here we’ll talk about the 8 most common myths I’ve seen in machine learning projects, and why they annoy data scientists. If you’re getting into data science, or are already mainstream, these are potential grenades that might be hurled at you. Hence, it would be handy knowing how to handle them.
DDOS attacks are a growing threat to website operators and online publishers around the world. It is important to realize that a growing number of DDOS attacks are launched against application layers. These types of attacks can be most difficult to prevent, so it is important for cybersecurity experts to do their due diligence. However, AI cybersecurity solutions are also the best line of defense against DDOS attacks. New cybersecurity solutions will depend heavily on machine learning.
Unsupervised learning may sound like a fancy way to say “let the kids learn on their own not to touch the hot oven” but it’s actually a pattern-finding technique for mining inspiration from your data. It has nothing to do with machines running around without adult supervision, forming their own opinions about things. Unsupervised learning helps you find inspiration in data by grouping similar things together for you. There are many different ways of defining similarity, so keep trying algorithms and settings until a cool pattern catches your eye. Let’s demystify!
Hiring a data scientist actually can be excruciatingly painful for companies. It's an equally big deal for aspirants to bag that perfect offer in core data science, one which is not just a glossed-up, namesake role. One evolves through various incremental stages of expertise to become a productive data scientist. For companies trying to identify one, it’s like finding a needle in the haystack. For any aspiring data scientist or one looking to move up jobs, these are clear pitfalls to be avoided.
IoT devices are nothing new, but the attacks against them are. They are evolving at a rapid rate as growth in connected devices continues to rise and shows no sign of letting up. Today, there are solutions for everyone and we continue to see more items that are always on and do not have to store or process data locally. Those in security would be quick to say that this rapid rise in connected devices has also increased the attack landscape as there is a lack of oversight and regulation of these devices.
Now that you know what machine learning is, let’s meet the easiest kind. My goal here is to get humans of all stripes and (almost) all ages comfy with its basic jargon: instance, label, feature, model, algorithm, and supervised learning. You’re dealing with supervised learning if the algorithm has the correct label handy for every instance. Later, it will use the model, or recipe, to label new instances.
Enterprise leaders need big data specialists who are creative, can collaborate with others, are skilled in research and possess exceptional writing skills. Enterprise leaders go about fulfilling their daily responsibilities, monitoring and evaluating organizational progress, coming up with new ideas – then, they do it all again. As this process continues its cycle and the business landscape continues to evolve, the role of written communication for big data projects will remain a mission-critical asset.
We continue our exploration of the key trends shaping up the customer interaction management market. In the first part of this article, we looked at the accelerating transition to the cloud and the impact of digital transformation initiatives. In this second part, I would like to explore four other driving forces. The coming of age of Artificial Intelligence (AI) and the viral adoption of messaging apps are enabling conversational experiences. Self-service has become an investment priority for enterprises and the number of virtual customer assistant providers has surged to a whopping 80.
While A/B testing might be a relatively easy-to-execute practice, most marketers will continue to faithfully serve a “winner takes all” approach in absence of being able to handle the heavy-duty analysis required despite knowing it will compromise the experience for a portion of their visitors. By using ad serving-like techniques for changing the onsite experience, instead of doing an A/B test of five different banners or five different call-to-actions, marketers can create all the variations they need and let a real-time machine learning engine do the work.
A common view in the press and in artificial intelligence research is that sentient and intelligent machines are just on the horizon. After all, machines diagnose and treat illnesses, drive cars, grow our food, manufacture and deliver new products, distinguish pictures, and play games better than we do. How much longer can it be before they surpass our intelligence and take our jobs? Before we decide if machines can surpass our intelligence, let us first define two terms that will help us getter a better handle on this topic: Weak AI and Strong AI.
With a boatload of visualisation tools at disposal and fancy data scientists to play with them, impactful use of data visualisation is still a rarity in enterprises. Visualization should be seen as a medium of story telling using data. A visual story is a perfect blend of art and science. Practitioners must hone their skills to fuse the right aesthetic ingredients with scientific elements. This creates an output that is relevant for users, solves a specific business challenge and delivers ROI for enterprises.
Real estate is a field that might sound like a strange fit for AI applications. It might even sound like a possible threat to your business model or livelihood. As it turns out, there's quite a lot of room for overlap here — and more than a few exciting opportunities along the way for humans to work alongside AI in the complex, fast-moving real estate world. There's still no substitute for the human touch and intuition, but with a helping hand from AI, tomorrow's real estate agents will be even more effective, knowledgeable and organized.
You’ve probably heard about “machine learning” and “artificial intelligence”. But what’s the difference between the two? We break down everything you need to know.
Will artificial intelligence bring a utopia of plenty? Or a dystopic hellscape? Will we, jobless and destitute, scavenge for scraps outside the walls of a few techno-trillionaires? Or will we work alongside machines, achieving new levels of productivity and fulfillment? The tech world has no lack of prognosticators. Over the next 5–10 years and beyond, we’ll see in exactly which ways AI revolutionizes industry and business. One thing, however, is clear: It’s happening, and it’s going to be big.
Enterprises are looking at how they interact with customers in a more holistic way. It includes the resolution of the issues that triggered customers reaching out in the first place as well as providing proactive service. The transition to the cloud of contact centers has created a “gold rush” in a market where incumbent vendors had traditionally a leg up.
Keeping the retail business, employees, and customers safe is the biggest priority of a business owner. Your products aren't the only things at risk of being taken. If you have customers using credit and debit cards or shopping online, you're collecting their information and creating a new target for thieves. Threats come from all sides, not the least of which being theft. The sad truth is that cyber attacks are inevitable. The best thing you can do is put up defenses to guard against hackers and comply with laws to protect your employees and customers alike.
You’ve probably heard of machine learning and artificial intelligence, but are you sure you know what they are? If you’re struggling to make sense of them, you’re not alone. There’s a lot of buzz that makes it hard to tell what science is and what’s science fiction. Starting with the names themselves…machine learning is just a thing-labeler, taking your description of something and telling you what label it should get.
Technology evolves at an alarming rate, so much so it seems almost pointless to make a push for the leading edge. By the time you adopt new technologies and systems into your business environment, there’s something new and more efficient coming along right behind it. Applications or devices receive an official update, and you just have to take the time to configure. It’s easy to get caught up in either scenario, resulting in the regular use of outdated, inefficient technologies. But what you may not know is that this issue can have a profound impact on your business, productivity and workflow.
The business world has started to recognise that for AI and IoT to be successful, they must be mutually beneficial ecosystems. An IoT device is connected directly or indirectly to a data-transmitting network such as the internet. The optimum value one can get from connected devices is some level of automated, enriched, intelligent insights. This “artificial intelligence” must logically become more valuable than the sum of its constituent, connected parts just like in an ecosystem.
Artificial Intelligence (AI) is finally making headways in the broader Customer Interaction Management space. Customer service departments have a lot of technology options to choose from to better their productivity and the customer experience. It incentivizes them to invest in software allowing incremental improvement of performance indicators. This has led to a conservative approach with breakthrough technologies such as AI. This is changing through the state of AI adoption.
We know the data is there, and we want to leverage it. By the time we get our hands on it, the opportunity has passed, and the information is too out-of-date to lend any real relevance. So the data sits there, in the dark, never becoming the insight we need to make critical business decisions. All the while, the volumes of data grow, threatening to shroud dark data in deeper, permanent obscurity. For this reason, AI gives us both access to information and the time to leverage it for business advantage.
Manufacturers and utilities are already tracking millions of data streams and generating terabytes a day. But everyone wants different cuts of data. A lot of those users sit near the data asset, so it makes sense to keep the data there. The best bet is to look at the use case scenario first. Chances are, every workload will require both cloud and edge technologies, but the size of the edge might be larger than anticipated.
Blockchain technology can address current inefficiencies within the supply chain space, bringing new levels of traceability to logistics processes hindered by current paper-based solutions. Using blockchain’s distributed and decentralized ledger, records of transactions can’t be erased, boosting overall transparency, increasing efficiency and improving cash flow for logistics operators. It would be wise to focus on use cases that involve multiple parties using transactions to synchronize ledger information and use cases that requires immutable records — both exciting challenges that demands the major industry players to collaborate and create a solid foundation for a community.