More practical applications of AI are on the horizon. Those applications will include helping the transportation industry to solve some of our biggest challenges in getting from place to place. AI is getting smarter thanks to innovative programmers—but it’s also making our transportation system better by building on the building blocks these programmers provided. AI can assist with urban design and traffic control in several ways, including adjusting variable speed zones based on traffic, traffic light timing, and smart pricing for vehicle tolls. Here are 4 important ways AI will help improve our transportation system.
The biotech field has been held back by the technological limitations, but ML and AI programs have programs have broken through the barriers into new possibilities impacting recent biotech and healthcare developments. Security and healthcare trends suggest future generations will utilize biotech on a daily basis, either to thwart identity theft or to cure cancer. We may still ask the big questions, but AI programs are finding the solutions. Public opinion is mixed about biotech. The manipulation of organic materials at the microscopic level has already produced new drugs and treatments while ethics committees discern what is acceptable.
From a business perspective, knowing your ideal customer is important to the success of your business. This is because it is important to understand who you are trying to market your products to. Understanding your customer’s needs, wants and desires is the best thing you can do for your business. Knowing your customers is important as it helps market your products or services accurately when you are aware of their likes and dislikes. The customers who are satisfied with your business, product, and services are your true and loyal customers. Here’s how to know your potential customers.
It’s a pivotal time for those involved in Artificial Intelligence, as many within the community are looking at how it can improve the state of the world we live in. A new approach to AI research by MIT IQ is the latest initiative to want to tackle some of these concerns. Whilst there are many initiatives actively researching Artificial Intelligence applications for the good of society, MIT aims to take a different approach to the very foundations of the field. Through this, they hope to answer some of the most burning questions that will help to drive the development of AI.
The artificial intelligence field is rapidly changing and maturing. The field is well-suited for candidates that are interested in computers, think analytically and enjoy reading and learning about the latest technological developments. Artificial intelligence specialists work with massive amounts of information, so analytical thinking is especially important for success in the field. In the future, artificial intelligence specialists will automate not only minute task, but entire business processes. Tomorrow’s AI leaders will man the helm of change initiatives designed to make better use of existing enterprise benchmarks, metrics and data stores.
Despite the mounting demand for Data Science professionals, it’s still an extremely difficult career path to break into. The most common complaints from candidates who have faced rejection are lack of experience, education level requirements, lack of opportunities for Freshers, overly demanding and confusing job role requirements. There are also challenges for people with heaps of experience getting rejected due to lack of applicable experience to the role they’re applying for. Make sure your skills, experience, and projects tell the hiring manager that you have the tools necessary to make an impact on their business and how when applying these techniques in the past, you’ve had x y z results.
Machine Learning does not change your role as a Product Manager and your role remains to talk to customers and communicating their problems to the technical team and business. Don’t build ML just for the sake of it as it is a large commitment and business investment. ML isn’t magic and it’s a customer delight feature. If you take the time to do it right, it can be game changing! Test with customers on weekly basis, run experiments, test hypothesis so you can learn quickly. Remember, the quicker you learn, the less risk you will be taking when launching to the wider customer base.
Computer Vision is one of the hottest research fields within Deep Learning at the moment. As Computer Vision represents a relative understanding of visual environments and their contexts, many scientists believe the field paves the way towards Artificial General Intelligence due to its cross-domain mastery. Why study Computer Vision? The most obvious answer is that there’s a fast-growing collection of useful applications derived from this field of study. Here are the 5 major computer vision techniques as well as major deep learning models and applications using each of them. They can help a computer extract, analyze, and understand useful information from a single or a sequence of images.
Central Securities Depositories (CSDs), in the post-trade environment, are changing their mind on new technologies and on their future position in the blockchain world. Increasing regulation, legacy systems, and costs pressures, are drivers for CSDs to at least embrace some aspects of the blockchain. They are increasingly considering them as an enabler of more efficient processing of existing and new services, instead of a threat to their existence. CSDs are likely to play an integral role in any blockchain environment. But they will look quite different from we know them today. Yes. CSDs are not expected to disappear in a blockchain world.
Apache Spark is now becoming the big-data platform of choice for enterprises. It is a powerful open source engine that provides real-time stream processing, interactive processing, graph processing, in-memory processing as well as batch processing with very fast speed, ease of use and standard interface. Sooner or later, your company or your clients will be using Spark to develop sophisticated models that would enable you to discover new opportunities or avoid risk. Spark is not hard to learn, if you already know Python and SQL. learn how to build a binary classification application using PySpark and MLlib Pipelines API.
There is a bounty of business use cases from which the business can choose in order to monetize their IOT efforts. The best approach is to build out your IOT Business Strategy with one use case at a time. In this manner, not only do you incrementally build out your IOT analytic, data, technology and architecture capabilities, but this enables the organization to build upon the work of previous use cases – to capture, share and refine the IOT data and analytic assets that are key drivers to IOT monetization.
Success requires a direct link between analytics and positive process outcomes. This requires a closed-loop process. One that enhances application intelligence based on analytic insights. This is what I call Prescriptive Applications. Prescriptive Applications integrate analytics to dynamically change application content, access, and workflow. If something broke, fix it. But be aware that the process can be a complex journey. Prescriptive Applications enable a closed loop process to optimize outcomes based on integrated insights from analytics-descriptive, diagnostic, predictive, and prescriptive. So, Prescriptive Applications provide a complete solution. While IIoT faces many challenges, could Prescriptive Applications be a key missing piece?
Enterprises are experiencing a rapid evolution in technology that is challenging traditional security systems and infrastructures. The arrival of business-oriented IoT devices creates even more demand for secure connectivity. To avoid security breaches, IT departments must secure connections for more users and devices than ever before. Providing encryption for the communications of many thousands of devices, virtual private networks (VPNs)are a tried-and-tested way to ensure sensitive company information remains secure and private. When deploying VPNs for a multitude of devices, a large-scale management solution is essential that can greatly enhance the productivity of IT departments and keep corporate networks secure.
Existing data architectures are at the breaking point with a large amount of data, velocity of data ingestion, and variety of data they need to process and store. Industry analysts are predicting that up to 80% of the new data will be semi-structured and unstructured. Modern Data Architecture addresses the business demands for speed and agility by enabling organizations to quickly find and unify their data across hybrid data storage technologies. The Modern Data Architecture stores data as is; it does not require pre-modeling. It handles the volume, velocity, and variety of big data.
What are the common mistakes made in building ML products? The goal of the note is to provide someone with limited ML understanding a general sense of the common pitfalls so that you can have a conversation with your data scientists and engineers about these. Many companies wanted to use ML and had built up ‘smart software’ strategies but didn’t have any data. You cannot use machine learning if you have no data. You can apply ML on small data sets too, but you have to be very careful so that the model is not affected by outliers and that you are not relying on overcomplicated models.
Of late, IoT as a technology has seen quite a few changes, and Internet of You (IoY) could be the latest. With IoY, you will no longer have to stop, think, and decide to take your phone out of your pocket, find and launch the app you want, then take action. Instead, our data and services will come to us when we need them and in the context in which we are currently in. ‘Internet of Things’ is relevant to the users only when it is centred around them, which combined with semantic web technology transforms into IoY.
A Blockchain Gateway platform should serve as an extra layer between existing enterprise applications and blockchain(s). The Blockchain Gateway offers seamless, easy, fast and secure integration of their existing applications and devices with one or more blockchain networks. The intuitive user interface makes it easy for corporates to configure all business critical configurations with low development efforts without the need of specific blockchain expertise. The Blockchain Gateway may bring real value to multiple parties within blockchain networks by simplifying integration of existing applications with one or more blockchain networks, speeding onboarding of participants and delivery of blockchain projects.
Some of the fastest applications that will be dominated by IoT are healthcare devices, wearable devices to enhance currently restricted capabilities, energy saving devices and smart devices for inter-connectivity in real time use. We will soon have smart homes, connected mobility (transportation), smart cities, and even smart forest zones and marine life to create better air, water and live a healthier life. We need devices that will bridge the gap between physical and digital world to improve the quality and productivity of life, society and support the eco-system.
Data-driven culture is about setting the foundation for the habits and processes around the use of data. Data-driven companies establish processes and operations to make it easy for employees to acquire the required information, but are also transparent about data access restrictions and governance methods. So, why is it important to build a data-driven culture in your organization? The data can only take an organization so far. The real drivers are the people and hence building the culture around data is important. An organization can work upon to build data-driven culture.
IIoT project failures are rampant. For many, failures are a direct result of poor information sharing and inefficient collaboration. Frustration builds with customer and partners. Leading to cost increases, limited adoption, and reduced potential ROI. The promise of IIoT requires frictionless (but secure) ecosystem access to information. When information flows within digital ecosystems, each business entity wins. The new digital world is complex. It challenges many long held beliefs about competitors, data sharing, and value propositions. Your new digital relationships will be different than prior relationships. Be open and transparent. Engage your customers and partners, and everybody wins.
What many businesses forget is that IoT devices are not typically secure. In fact, they are designed to remain cheap and lightweight, which makes them difficult to manage once they are connected to the network. There are several reasons why IoT devices create a visibility challenge. As device manufacturers aren’t required by consumer protection laws to integrate security features, now is the time for enterprises to focus on gaining complete IoT visibility. It starts with understanding the inventory of connected devices and ends with segmenting those devices into areas of the network with limited access according to their needs.
Adoption of IoT is going to continue to drive an adoption of hybrid cloud. As a result, the solution to these problems cannot simply be providing a managed service for an incredibly complex product. The product itself needs to be simple and easy to use, yet highly scalable, so that it can be deployed and managed directly on the edge and in the cloud with the same level of effort and skill set. In order to continue to drive and support innovation, the data management industry needs to design and build the traditional big data architectures required to consume IoT data and transform that data into valuable insights, identify patterns and make it actionable.
IIoT is truly ubiquitous. It impacts everything from factories to construction equipment to the plants in our office. It turns out the commercial landscaping maintenance management market is a big business. Some might ask, why all this greenery? Studies show interior office plants contribute to employee productivity and satisfaction, not to mention the fact that greenery can increase commercial property value up to 15%. Green walls and roofs, commercial landscapes, and interior plantscapes are like other commercial assets in that they need regular maintenance.
There’s a lot of data, and with more on the way, lots of data scientists are needed to make use of it all. Data science is only growing more critically important as our technology advances. It will be key in all businesses in the future and there looks to be a massive shortage of data pros, a job that pays well and is fun. It helps us tell important stories. I don’t need to produce an infographic to make this tale any more compelling. Data science deserves a spot in every STEM education curriculum.