Archive for the ‘Artificial intelligence’ Category

Can Artificial Intelligence (AI) Enrich Content Collaboration? Or Is It Just a Lipstick?

Artificial Intelligence (AI) enrich Content Collaboration

Is Artificial Intelligence (AI) the new lipstick? Sure, it is being put on many pigs. Can artificial intelligence improve Enterprise File Sharing and Sync (EFSS), Enterprise Content Management (ECM) and Collaboration?  We want to explore if we could find some obvious collaboration use cases that can be improved using machine learning. In this article, we will not venture into AI techniques or impact of AI or evolution of AI. We are interested in exploring how EFSS benefits from “machine learning” – a technique that allows systems to learn by digesting data. The key is ‘learning’ – a system that can learn and evolve vs. explicitly programmed.  Machine learning is not a new technology; many applications, such as search engines (Google, Bing), already use machine learning.

In the past year, many large players, such as Google, Amazon, and Microsoft, have started offering AI tools and infrastructure as service. With many of the basic building blocks available, developers can focus on building right models and applications. Here are a few scenarios in Enterprise File Sharing and Sync (EFSS), and Enterprise Content Collaboration, where we can apply machine learning soon.

Search

Search is a significant part of our everyday life. Google and Amazon have made the search box the center of navigation. For instance, a decade ago, the top half of the Amazon homepage was filled with links, which is now replaced by a search box at the top.  However, search hasn’t taken a significant role in enterprise collaboration, yet. Every day, we search for files that don’t fit in a simple search criteria. Think of search that goes ‘looking for a design proposal from a vendor x I received six months back.’ Today, we manually sort through files to find an image that satisfies the above search criteria.  We could use a simple query processing,  a crawler, and a sophisticated ranker to surface file search results, based on estimated relevance. Such a search feature can continue to learn and improve to provide better results each time. Already, we have many such machine learning algorithms and techniques available to index files, identify relevance, and rank search results per relevance. Hence, applying to enterprise scenarios requires a focused effort from the solution providers.

Predict and organize relevant content

A technique in machine learning, called unsupervised learning, involves building a model by supplying it with many examples, but without telling it what to look for. The model learns to recognize logical groups (cluster), based on certain unspecified factors, revealing patterns within a data set.  Imagine your files are automatically organized, based on the projects you are working on. Any file will have a set of related files just one click away. Won’t such a feature have a significant productivity boost?

Collaboration

Collaboration across different languages will be simplified with many advanced translation tools available today. Google Cloud Translation API provides a straightforward API to translate a string from and to many languages. Translation of user comments and meta data, such as tags, image information, can be very useful for any large organization that involves working with partners and vendors across the globe. With translation combined with machine learning, translation within an enterprise can improve by learning domain knowledge (medical, law, technology etc.) and internal jargon. Systems can extract right meta data, apply domain knowledge, and translate them for employees, partners, and customers, so they easily communicate and collaborate.

User Interface

Interaction with EFSS applications need not be just clicks and texts.  Users can have more engaging user experiences that include conversational interactions, e.g., users could just say “open the sales report that I shared with my manager last week.” Personal assistants, such Siri, Cortana, and Alexa, already provide such conversational interfaces for many personal and home scenarios. Though it sounds complex, some of the technology, such as automatic speech recognition for converting speech to text and natural language understanding, are available in Amazon APIs. Converting the conversation into an actual query might not be as complex as it sounds.

Security and Risk Assessment

Machine learning has an excellent application in monitoring network traffic patterns to spot abnormal activities that might be caused by cyber-attack or malicious activities. Solutions like FileCloud use some of these techniques to identify ransomware and highlight potential threats. Similar techniques can identify compliance risks to analyze if any documents being shared have any personal identifiable information (credit card) PII or personal health information PHI. Systems can predict and warn security risks before the breach happens.

These ideas are just a linear extrapolation of the near future. Even these simple linear extrapolations look promising and interesting. Many predict that, within a few years, almost every device and service will have intelligence embedded in them. In future, the concept of file and folders might be replaced by some other form of data abstraction. As AI and collaboration continue to evolve, resulting applications evolve exponentially better than our linear extrapolations, and our current thoughts could appear naïve. Hope it doesn’t evolve, as Musk puts it, “with artificial intelligence, we’re summoning the demon.”


The Intelligent Cloud : Artificial intelligence (AI) Meets Cloud Computing

intelligent cloud

If you thought that mobile communications and the Internet have drastically changed the world, just wait. Coming years will prove to be even more disruptive and mind-blowing.  Over the last few years, cloud computing has been lauded as the next big disruption in technology and true to the fact it has become a mainstream element of modern software solutions just as common as databases or websites; but is there a next phase for cloud computing? is it an intelligent cloud?

Artificial intelligence (AI) is the type of technology with the capacity to not only enhance current cloud platform incumbents but also power an entirely new generation of cloud computing technologies. AI is moving beyond simple chat applications like scheduling support and customer service, to impact the enterprise in more profound ways; as automation and intelligent systems further develop to serve the purpose of critical enterprise functions. AI is bound to become ubiquitous in every industry where decision-making is being fundamentally transformed by ‘Thinking Machines’. The need for smarter and faster decision making and the management of big data is the driving factor behind the trend.

Remember Moore’s Law? In 1965, Intel’s co-founder, Gordon Moore observed that the transistors per square inch on integrated circuits had doubled in number each year since their invention. For the next 50 years, Moore’s Law was maintained. In the process, multiple sectors like robotics and biotechnology saw remarkable innovation because machines that ran on computers and computing power all became faster and smaller with time as the transistors on the integrated circuits became more efficient. Now, something even more extraordinary is happening. Accelerating technologies such as big data and artificial intelligence are converging to trigger the next major wave of change. This ‘digital transformation’ will reshape every aspect of the enterprise, including cloud computing.

Artificial intelligence (AI) is expected to burgeon in the enterprise in 2017. Several IT players, including today’s top IT companies, have heavily invested in the space with plans to increase efforts in the foreseeable future.

Despite the fact that AI has been around since the 60’s, advances in networking and graphic processing units, along with demand for big data, have put it back at the forefront of several companies’ minds and strategies. Given the recent explosion of data from Internet of Thing (IoT) and applications, and the necessity for quicker, real-time decision making, AI is well on its way to becoming a key differentiator and requirement for major cloud providers.

AI-First Enterprises

In a market that has for the longest time been dominated by four major companies – IBM, Amazon, Microsoft, and Google –an AI first approach has the potential to disrupt the current dynamic.

“I think we will evolve in computing from a mobile-first to an AI-first world.”

-Sundar Pichai, Chief executive of Google

The consumer world is not new to AI-based systems; products like Siri, Cortana and Alexa have been making our lives easier for a while now. However, the enterprise applications for AI are completely different. An AI first enterprise approach should be designed to allow business leaders and data professionals to organize, collect, secure and govern data efficiently so they can gain the insights they require to become a cognitive business. In order to maintain a competitive advantage, businesses today have to get insights from data; however, acquiring those insights is complex and requires work from skilled data scientists. The ability to predict strategic and tactical purposes has evaded enterprises due to prohibitive resource requirements.

Cloud computing solves the two largest hurdles for AI in the enterprise; abundant, low cost computing and a means to leverage large volumes of data.

AI-as-a-service?

Today, this new breed of Platform as a Service (AIaaS) can be applied on all the data that enterprises have been collecting. Major cloud providers are making AI more accessible “as-a-service” via open source platforms. For enterprises with an array of complex issues to solve, the need for disparate platforms working together can’t be ignored. This is why making machine learning and other variations of AI applications and technology available via open source is critical to the enterprise. By leveraging AI-as-a-service, businesses can innovate solutions that solve infinite problems.

As machine learning becomes more popular as a service, organizations will have to decide the level at which they want to be involved. While the power of cognitive intelligence is undeniably high, wanting to use it and being able to use it are two completely different things. For this reason, most companies will opt to use a PaaS vendor to manage their entire cycle of data intelligence as opposed to an in-house attempt, allowing them to focus on powering and developing their applications. When looking for an AI provider, you have to ask the right questions. The ideal vendor should be in a position to elucidate both how they handle data and how they intend to solve your specific enterprise problem.

There are multiple digital trends that have the potential to be disruptive; the only way to guarantee smarter business processes, more agility, and increased productivity is by planning ahead for the change and impact that is coming. The main differentiating factor between competing vendors in this space will be how the technology is applied to improve business processes and strategies.

Author: Gabriel Lando

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