Les technologies d'intelligence artificielle telles que l'apprentissage machine et le Deep Learning permettent d'obtenir des informations et de la précision à deux marques majeures dans des secteurs très différents : la santé et les assurances.
Des théories sur les futures incidences de l'intelligence artificielle (IA) sur les entreprises et la société vont florissantes. Mais la réalité du terrain aujourd'hui pour les entreprises et les dirigeants appliquant des technologies comme l'apprentissage machine et l'apprentissage profond à leurs enjeux majeurs est déjà très enthousiasmante. Les modèles de fonctionnement sont refondés en se basant sur les informations obtenues de puissantes capacités cognitives. De nouveaux produits et services améliorent l'expérience client, voire la condition humaine. D'une façon très concrète et significative, l'IA change le monde pour le meilleur.
AI-Technologien wie Machine Learning und Deep Learning liefern zwei großen Marken in zwei sehr unterschiedlichen Branchen – Gesundheit und Versicherungen – Insights und Genauigkeit.
Theorien zu den zukünftigen Auswirkungen künstlicher Intelligenz (AI) auf die Geschäftswelt und die Gesellschaft sind allgegenwärtig. Aber die Realität, wie Unternehmen sie kennen, die Technologien wie Machine Learning und Deep Learning anwenden, ist schon aufregend genug. Geschäftsmodelle werden anhand der Insights umgestaltet, die durch die leistungsstarken kognitiven Funktionen generiert werden. Neue Produkte und Dienstleistungen verbessern das Benutzererlebnis – um nicht zu sagen die menschliche Existenz. Auf sehr reale und bedeutsame Weise verbessert AI die Welt.
Published By: SAP SME
Published Date: Apr 10, 2018
La industria de los productos de consumo está experimentando una gran transformación. Los consumidores demandan experiencias que ofrezcan confianza, alegía y protección – aunque son menos tangibles– estos son los impulsores que definirán el exito. Es por eso que las compañías de productos de consumo necesitan reimaginar todo, desde el compromiso del consumidor y las operaciones hasta la innovación de los productos. Las tecnologías emergentes como el Internet de las Cosas, Machine Learning e Inteligencia Artificial, pueden ayudar a guiar el rumbo de este cambio e impulsar el éxito del mismo.
In this six-step guide, we aim to help you solve your data challenges to prepare for advanced analytics, cognitive computing, machine learning and the resulting benefits of AI. We’ll show you how to get your data house in order, scale beyond the proof of concept stage, and develop an agile approach to data management. By continually repeating the steps in this guide, you’ll sharpen your data and shape it into a truly transformational business asset. You’ll be able to overcome some of the most common business problems, and work toward making positive changes:
• Improve customer satisfaction
• Reduce equipment outages
• Increase marketing campaign ROI
• Minimize fraud loss
• Improve employee retention
• Increase accuracy for financial forecasts
Published By: MobileIron
Published Date: May 07, 2018
The types of threats targeting enterprises are vastly different than they were just a couple of decades ago. Today, successful enterprise attacks are rarely executed by the “lone wolf” hacker and instead come from highly sophisticated and professional cybercriminal networks. These networks are driven by the profitability of ransomware and the sale of confidential consumer data, intellectual property, government intelligence, and other valuable data. While traditional PC-based antivirus solutions can offer some protection against these attacks, organizations need highly adaptive and much faster mobile threat defense (MTD) for enterprise devices.
Read why Forrester ranked Microsoft a leader in SFA Solutions
You have bold ambitions for your sales team. You want to—and need to—reinvent productivity.
But the success of your sales team is stifled by silos. It’s no wonder they’re struggling when their transactional systems, social networking, and productivity tools are separate.
With the Microsoft Relationship Sales solution, you can scale the power of one-on-one relationship selling by unifying the sales experience. And you can help empower your sellers with savvy insights that engage and delight customers. It combines Dynamics 365 for Sales with LinkedIn Sales Navigator to help sellers identify the right customers—and the right time and way to engage with them.
Read The Forrester Wave™: Sales Force Automation Solutions, Q2 2017 report that says Microsoft is a best fit "...for those companies that are bullish and looking to disrupt their peers with AI and machine learning."
Empower your team to reinvent the way they sell—and he
They were mistaken. Research conducted by the Economist Intelligence Unit (EIU) and written in discussion with SAP shows
that many organizations are moving ahead now, some
aggressively, to integrate ML into their operations.
As digital business evolves, however, we’re finding that the best form of security and enablement will likely remove any real responsibility from users. They will not be required to carry tokens, recall passwords or execute on any security routines. Leveraging machine learning, artificial intelligence, device identity and other technologies will make security stronger, yet far more transparent. From a security standpoint, this will lead to better outcomes for enterprises in terms of breach prevention and data protection. Just as important, however, it will enable authorized users in new ways. They will be able to access the networks, data and collaboration tools they need without friction, saving time and frustration. More time drives increased employee productivity and frictionless access to critical data leads to business agility. Leveraging cloud, mobile and Internet of Things (IoT) infrastructures, enterprises will be able to transform key metrics such as productivity, profitabilit
Big data and analytics is a rapidly expanding field of information technology. Big data incorporates technologies and practices designed to support the collection, storage, and management of a wide variety of data types that are produced at ever increasing rates. Analytics combine statistics, machine learning, and data preprocessing in order to extract valuable information and insights from big data.
Business users expect immediate access to data, all the
time and without interruption. But reality does not always
meet expectations. IT leaders must constantly perform
intricate forensic work to unravel the maze of issues that
impact data delivery to applications. This performance
gap between the data and the application creates a
bottleneck that impacts productivity and ultimately
damages a business’ ability to operate effectively.
We term this the “app-data gap.”
Published By: Dell EMC
Published Date: Oct 13, 2016
Flexibility is important, since many future initiatives—big data, machine learning, emerging technologies, and new business directions—will be built on this cloud structure.
No matter what shape your cloud infrastructure takes, Dell EMC converged and hyper-converged platforms and innovations like Dell EMC VscaleTM Architecture, powered by Intel® Xeon® processors, deliver the pathways to scale-up and scale-out, today and tomorrow.
Published By: Genesys
Published Date: Jun 06, 2017
In this ebook, learn:
- Five trends will have the biggest impact on customer experience
- How to use machine learning to detect patterns and trends to deliver the next great customer experiences
- How to future-proof your contact center and adapt to changing customer needs
Every week InfoSight analyzes more than a trillion data points from
more than 9,000 customers. How does this translate into true
business value? By reducing your business risk with over Six-Nines
of measured availability. By providing you with an infrastructure
that gets “smarter” every single day. By empowering IT staff to
focus on business priorities instead of mundane maintenance.
Competitive advantage from analytics is changing, and for the better. For the first time in four years, MIT Sloan Management Review found an increasing ability to strategically innovate with analytics based on interviews with more than 2,600 practitioners and scholars globally.
Learn more about key findings, including:
Wider use of analytics, better knowledge of its benefits and greater focus on applications have reversed a trend on the benefits of analytics.
Return on investment for analytics stems from the governing and sharing of data throughout the organization.
Machine learning enables organizations to discover more insight from their data, allowing employees to focus on other critical responsibilities.
Published By: Pentaho
Published Date: Nov 04, 2015
Although the phrase “next-generation platforms and analytics” can evoke images of machine learning, big data, Hadoop, and the Internet of things, most organizations are somewhere in between the technology vision and today’s reality of BI and dashboards. Next-generation platforms and analytics often mean simply pushing past reports and dashboards to more advanced forms of analytics, such as predictive analytics. Next-generation analytics might move your organization from visualization to big data visualization; from slicing and dicing data to predictive analytics; or to using more than just structured data for analysis.
What do these market-defining trends have in common?
· Analytics for all
· Analytics as competitive differentiator
· Internet of Things
· Artificial intelligence/Machine learning/Cognitive computing
· Real-time analytics/event management
They all rely on data – timely, accurate data delivered within an insightful context – to deliver value. The question is: who in the enterprise is most qualified and prepared to help deliver on the vision and values of the data-driven enterprise?
It’s going to take a special type of professional to deliver that value to enterprises. Organizations are seeking professionals to step forward and take the lead, provide guidance and lend expertise to move into the brave new world of digital. The move to digital and all that it entails – sophisticated data analytics, online customer engagement and digital process efficiency – requires, above all, the skills and knowledge associated with handling data and turning it into insights. The move to digital i
While many organizations are guarding the front door with yesterday’s signature-based antivirus (AV) solutions, today’s unknown malware walks out the back door with all their data. What’s the answer? A new white paper, “The Rise of Machine Learning in Cybersecurity,” explains machine learning (ML) technology —what it is, how it works and why it offers better protection against the sophisticated attacks that bypass standard security measures. You’ll also learn about CrowdStrike’s exclusive ML technology and how, as part of the Falcon platform’s next-gen AV solution,it dramatically increases your ability to detect attacks that use unknown malware.
Download this white paper to learn:?How different types of ML are applied in various industries and why it’s such an effective tool against unknown malware?Why ML technologies differ and what factors can increase the accuracy and effectiveness of ML ?How CrowdStrike’s ML-based technology works as part of the Falcon platform’s next-generation AV
Marketing leaders will find a host of new vendors in this year's Magic
Quadrant for multichannel campaign management. Vendors are focused on integrating machine learning, personalization and ad tech capabilities into big data foundations for deeper customer engagement.
Published By: Oracle CX
Published Date: Oct 19, 2017
In today’s IT infrastructure, data security can no longer be treated as an afterthought, because billions
of dollars are lost each year to computer intrusions and data exposures. This issue is compounded by
the aggressive build-out for cloud computing. Big data and machine learning applications that perform
tasks such as fraud and intrusion detection, trend detection, and click-stream and social media
analysis all require forward-thinking solutions and enough compute power to deliver the performance
required in a rapidly evolving digital marketplace. Companies increasingly need to drive the speed of
business up, and organizations need to support their customers with real-time data. The task of
managing sensitive information while capturing, analyzing, and acting upon massive volumes of data
every hour of every day has become critical.
These challenges have dramatically changed the way that IT systems are architected, provisioned,
and run compared to the past few decades. Most companies
A fundamental people-process-technology transformation enables businesses to remain
competitive in today’s innovation economy. Initiatives such as advanced security, fraud detection
services, connected consumer Internet of Things (IoT) devices, augmented or virtual reality
experience, machine and deep learning, and cognitively enabled applications drive superior
business outcomes such as predictive marketing and maintenance.
Superior business outcomes require businesses to consider IT a core competency. For IT, an
agile, elastic, and scalable IT infrastructure forms the crucial underpinning for a superior service
delivery model. The more up to date the infrastructure, the more capable it is of supporting the
scale and complexity of a changing application landscape. Current-generation applications must
be supplemented and eventually supplanted with next-generation (also known as cloud-native)
applications — each with very different infrastructure requirements. Keeping infrastructure up
Published By: Datarobot
Published Date: May 14, 2018
The DataRobot automated machine learning platform captures the knowledge, experience, and best practices of the world’s leading data scientists to deliver unmatched levels of automation and ease-of-use for machine learning initiatives. DataRobot enables users of all skill levels, from business people to analysts to data scientists, to build and deploy highly-accurate predictive models in a fraction of the time of traditional modeling methods