Artificial intelligence is not only for multi-billion-dollar corporations. Even small and medium-sized companies are beginning to take advantage of the support services provided, and computer software can now be even more integrated into the work processes of even small businesses to increase their efficiency. In this article, we will share with you five simple ways your Business can benefit from Artificial Intelligence.
The task of CRM is to collect and manage customer-specific data. Every company's support and customer service can benefit enormously from leaving the maintenance and organisation of this data in large parts of the software so that employees can devote themselves to more important tasks. An example is the CRM platform of SalesForce which began using Einstein ArtificiaI Intelligence in 2016 to analyse the e-mails, telephone calls, social media posts, product reviews and customer feedback. From this, Einstein Artificial Intelligence draws a picture of the customer and makes it possible to gain new insights from the customer data and to formulate marketing strategies more precisely.
Intelligent Customer Service
In the meanwhile, Artificial Intelligence can take some of the work out of customer support. Automated answers to frequently asked questions provide time-saving support services for customer support, which should focus on more critical tasks that are not yet covered by software. In this case, people can suggest answers or even give them themselves. Artificial Intelligence here acts as an intermediary, thus ensuring free resources in customer support and optimising processes.
Intelligent software can also help companies keep marketing costs low. The analysis of user behaviour in advertising offers the possibility to determine precisely which advertising is useful and where the placement of ads does not recoup the expenditure. Companies use their budgets in a targeted manner. With the help of customer behaviour analysis, marketing strategies can be planned even better in the future.
Artificial Intelligence can provide valuable support when collecting competitor data and analysing content. AI can monitor the behaviour of a competitor on the Internet. It observes its activities in the social media or any changes in its products prices. In this way, the strategies of an opponent can be predicted and adequate. Thus counter-strategies can be developed.
Artificial intelligence can deliver results in various areas of data analysis. Algorithms are already able to make forecasts efficiently. The application is not even that difficult. AI here analyses Google spreadsheets, CSV and Excel files without the need for programming skills during implementation. The user-friendliness grows, and the usage is also possible for non-specialists. Small companies in particular, which do not want to afford a specialist in every area, benefit from such programmes.
It can be said that machine learning will have a significant impact on the business world and due to its vast development businesses can already benefit from Artificial Intelligence optimisation and simplification of any tasks in the area of online marketing and data management.
aiso-lab is at your disposal as a competent partner for all challenges in the field of software and hardware.
Counterfeiting artwork is a highly profitable business. One talented art forger can earn millions of dollars by convincing the art world that the painting is real. However, scandals can shake the art market when it turns out that collectors have fallen for deceptively good imitations and have spent vast sums of money on forgeries. Experts estimate that around 50% of the art traded on the market are counterfeit. But this could change soon - since researchers at Rutgers University are currently introducing a computational approach for the analysis of strokes in line drawings that can determine original work beyond doubt and spot a fake just by looking at the strokes used to compose a piece.
Identifying fake art is a difficult undertaking. Experts analyse pigments and binders, determine the age of wood and textiles by using the radiocarbon method and infrared reflectography, X-rays to prevent counterfeiters from using glue. Since the actual artists cultivate an inimitable style, scientists at the Rutgers University are using image recognition methods to track down most of the counterfeiters. In the meanwhile, Artificial Intelligence art recognition has reached an almost 100% hit rate with individual artists. Through Machine Learning, it is possible to draw the strokes of famous artists such as Pablo Picasso, Amedeo Modigliani, Egon Schiele, Henry Matisse and many others.
Machine Learning was trained on specific features, the difference between it and the RNN can point to the characteristics the neural network was looking at to detect forgeries.
In this case, 300 line drawings of famous artists were shown, and scientists only had to give the information of which pictures belong to whom. These drawings were then divided into 80,000 single strokes, and the algorithm learned the style of each artist. Later, art recognition was able to assign almost all of the unknown work to the right artists. In the case of forgeries, the hit rate was 100%. As good as the counterfeiters can deceive the human eye, the image recognition learned to distinguish the fakes from the originals by the very own stroke of the artists.
This experiment shows that the proposed methodology can classify individual strokes with accuracy 70%-90%, and aggregate over drawings with an accuracy above 80%, while being robust to be deceived by fakes with accuracy 100% for detecting counterfeits in most settings.
Up to now, only drawings with ink and pencil, woodcuts and etchings are part of the repertoire of art recognition. However, this will change in the future. Artificial Intelligence will even be able to expose counterfeiters who display new work as original in the style of old masters. All that is needed is to learn to recognise artists not only by strokes.
One picture is worth a thousand words, that’s why we have brought Artificial Intelligence essentials into one image.
There are certainly more points of view and approaches. Therefore we would like to receive feedback and suggestions which we will gladly consider in the next release.
Here you can download your high – resolution AI Poster image.
The modern smartphone era which began 10 years ago with the introduction of the first iPhone, has now matured. When Google's Pixel-2 phones were introduced in San Francisco, CEO Sundar Pichai said that the smartphone's features have "weakened" thus it was difficult to develop exciting new hardware-based products. According to Google, there was a transition from a mobile-first company to an Artificial Intelligence first business, as the field of machine learning is one of Google's strongest assets.
Today, the translation of neural machines encompasses 96 languages and provides 2 billion translations per day. The live translation of a woman speaking Swedish while wearing wireless Google Pixel Buds headphones to an English speaking person holding a Google Pixel smartphone is considered as a best-illustrated demonstration proving the power of Google’s new integrated AI phone - services.
Google's latest open source software TensorFlow Lite for machine learning developers pre-release, is an exciting change in the area of AI. The company's commitment to the development of AI that can run algorithms on a mobile device - with no internet connectivity - is the foundation for the Artificial Intelligence Of Things (AIOT) of the future.
As far as consumer products are concerned, Google’s assistants Alexa and Siri are among the most popular AI mainstream applications. For 30 or 40 dollars, a person can get their own interactive AI assistant – provided that they have WiFi and charging accessibility.
TensorFlow Lite represents the first comprehensible steps in order to make Artificial Intelligence - powered devices not only accessible but also disposable. That results in the death of buttons. Developers can now preview Tensor Flow Lite for Android and iOS. Instead of providing new features for AI applications, the existing hardware - such as the Snapdragon processors - was used to execute algorithms that are normally not possible for mobile devices without connecting to the cloud.
With Google's new Lite artificial intelligence platform, you can run AI models on a smartphone and after adding new data, run these algorithms to get new results. It is on-the-go machine learning with no need for internet connectivity.
If you're one of those people who are afraid that hundreds of devices in your house can spy on you via your Internet connection, you'll be happy to know that Google’s researchers are designing the Tensor Flow Lite specifically to address these kinds of concerns.
According to the Tensor Flow Lite website, the software was developed fulfilling the following criteria:
It is interesting to see what's next on Google's AI Platform Miniaturization project. It paves the way for voice-controlled disposables based on cheap chips and AI-powered devices that won't expose your entire network to hackers attacks.
If Google continues to bring more value to less powerful devices, we will eventually live in a world where Artificial Intelligence could affordably be used in any device, even the disposable ones. Google’s engineer and technical director of TensorFlow, Pete Warden, told MIT,"What I want is a 50-cent chip that allows easy speech recognition and runs on a button battery for a year."
Tensor Flow Lite takes the company one step closer to Warden's vision.
The main topics of this workshop include interactive presentations of AI but also open discussion about technology, applications, social impact and more.
I am looking forward to seeing you there!
A group of researchers at the UCL (University College London) Knowledge Lab and Pearson have published an interesting AI paper regarding Education and the overall transformation of learning and teaching through technology. This paper aims at two things: the first is to explain what AIEd is and how it is built, and the second is to find out what the benefits of AIEd and how artificial intelligence will positively transform education in the coming years.
This approach describes how adequately designed and well-thought-out AIEd implementation can successfully contribute to the classroom environment. Importantly, the researchers do not see AIEd replacing teachers; instead, the future role of teachers continues to evolve and is eventually transformed such that their time is used more efficiently.
The researchers’ conclude that AIEd should be implemented in mixed learning environments where computerised advances and customary classroom exercises supplement each other. Understanding this implies tending to the 'chaos' of genuine classrooms, colleges, or workplace learning conditions, and including teachers and students in the application of AIEd with the goal that the outline would resemble this:
On 23rd November 2017, the conference "IoT Future Trends" will take place, hosted by the eco-Verband and IHK Köln.
The central topic is how to intelligently use and evaluate the enormous amounts of data generated in the “Internet of things” with the help of artificial intelligence, deep-learning, and other methods.
I am delighted to give a lecture regarding the use of artificial intelligence there - while I have already given an interview in advance.
The Eco-Association has provided us two free tickets, which we would like to offer you as part of a raffle.
Please send us a short e-mail to Tickets@aiso-lab.de
See you soon in Cologne!
A year ago Google published a paper on " Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs" at The Jama Network. It demonstrated that one of the main reasons for blindness is Diabetic Retinopathy (DR), a medicinal condition in which harm occurs to the retina because of diabetes.
Google Brain, the company’s AI team, has worked with specialists to enable them to analyse DR. The group has gathered more than 128,000 pictures that were each assessed by 3-7 ophthalmologists. These images were processed by a deep learning algorithm for making a model to recognise Diabetic Retinopathy. The execution of the calculation was tested on two distinctive datasets totalling to 12,000 images.
The use of Machine Learning (ML) in Diabetic Retinopathy is a leap forward in the fields of AI and health care. Robotized diagnosis of DR with higher exactness can help eye doctor's facilities in evaluating more patients and prioritising the treatment. This innovation can fill the existing deficiency in ophthalmology divisions.