Rena is a Director of Custom Research at Current Analysis, specializing in Delivery Management. She is responsible for ensuring the delivery of actionable recommendations and guidance to clients to assist them in formulating their market development and execution strategies. Her expertise is in telecommunications and IT services, including business networking , communications, data center, security, and business continuity services.
During its AI developer conference, Baidu made several announcements that demonstrate how it is moving the Chinese AI market forward, but the release of its Kunlun chip stands out as a key move that repositions it in the not only the Chinese market, but also globally
With Kunlun, Baidu joins the ranks of a select few companies that not only offer an AI platform that helps enterprises deploy AI-infused solutions, but that have also developed their own hardware to maximize AI processing.
Baidu is hot on the heels of the likes of Microsoft and Google. Although already known as an ambitious player in the AI realm, primarily in China, the search engine provider hasn’t managed to establish itself as a major force in the space, until now. Earlier this month, Baidu announced that it is bringing to market an AI-optimized chip, called Kunlun. With the move, Baidu joins the ranks of a select few companies that not only offer an AI platform that helps enterprises deploy AI-infused solutions, but have also developed their own hardware to maximize AI processing. Continue reading “Release of AI-optimized Kunlun Chip a Game Changer for Baidu”→
Successful AI projects take a village; project teams that include members from groups across the company are more likely to uncover the ‘what-if’ and ‘then what’ questions that are best addressed early.
GlobalData’s 2018 survey found that close to 40% of businesses include all affected parties in decisions related to big data and analytics solutions.
We’ve all heard that not only are machine learning (ML) algorithms time-consuming to develop and train, but that they also need access to vast data lakes and specialized data scientists. With these requirements, it’s no wonder that businesses tend to focus on identifying the skilled IT-centric resources required for undertaking an AI deployment. But AI isn’t just the playground of data specialists, successful outcomes take a village. Project teams that include members from different organizations across the company are more likely to uncover the ‘what-if’ and ‘then what’ questions that are best addressed early on. HR, legal, finance, customer service, operations, and other business units have much to contribute to a successful AI deployment. Continue reading “With AI Decisions, It Takes a Village”→
• Huawei showcased its Video Cloud Platform at its recent analyst event, touting its application for public safety.
• The company pointed to widespread adoption and success in China, but can it find a market for its solution overseas?
During Huawei’s Analyst Summit in Shenzhen, China, executive keynotes emphasized the role of artificial intelligence (AI) in the company’s vision to create a more connected, more intelligent world. The company’s vision is to use AI to improve people’s daily lives and to benefit society as a whole. Unlike some competitors, who often showcase the application of AI to improve the customer experience, or point to use cases that incorporate natural language processing or natural language generation, Huawei was keen to highlight its video strategy. The company has roughly 5,450 members of its staff involved in developing video solutions and eight research and development centers that focus on video technology (three in China, as well as sites in the US, France, Ireland, Russia, and Japan). Huawei envisions several use cases for the application of AI and video, including identification of abandoned objects, intrusion detection, crowd density monitoring, facial control/admission processing, and vehicle, facial, and physical attribute identification. Continue reading “Is Public Safety China’s New Export?”→
• During IBM Think, IBM made several AI-related announcements, some designed for enterprises with complex requirements, and others geared towards helping businesses deploy their first AI solution.
• Although IBM’s new capabilities and tools in support of deep learning are impressive, and position IBM as a thought leader, it’s the steps IBM is taking to help companies just getting started with AI that truly move the market forward.
IBM Think was promoted as an event that would bring together the greatest minds in AI. It featured technologies such as virtual assistants, machine learning (ML), and deep learning (DL), and also touched on hot button issues such as ethics and AI. During her keynote, CEO Ginni Rometty discussed the transformational role that AI will have on the IT market going forward, and she introduced Watson’s Law, describing it as a follow on to Moore’s Law and Metcalfe’s Law. Continue reading “IBM Think 2018: Big Blue Looks to Help Companies Adopt Their First AI Project”→
Businesses looking to adopt AI must not only evaluate the technology’s implications on job displacement and data security, but also consider that algorithms may unintentionally undermine the organization’s ethical standards.
Customers are quick to pass judgement; if unintentional biases become public, a company’s brand reputation may suffer significantly.
Much has been written about ethics and artificial intelligence (AI), and rightly so. With many organizations looking to adopt some form of AI technology in 2018, business leaders are wise to stay on top of emerging ethical concerns.
Job displacement is still a key consideration, as is safeguarding data. In a recent GlobalData survey, 23% of organizations indicated they had cut or not replaced employees because of AI; 57% indicated security as a top concern.
However, looking ahead, the question of ethics is the real challenge the AI community will need to tackle. And it is a challenge that is far more controversial than security or privacy. What happens when a self-driven car needs to decide between hitting a child that has run into the road, or swerving and risking the injury of its passenger? How proactive should a personal assistant be when it detects wrongdoing? What should be done when a personal assistant believes that a user’s usage pattern points to having committed a serious offense – should it alert authorities?
Probably more relevant to business leaders is the concern that they may not know if an AI infused application will perform up to their organization’s ethical standards. It may contain unintentional racial bias – say a financial algorithm that is biased against a specific race, or an application that demonstrates a preference towards one gender over another. What should be done when a phrase that is acceptable when said by one demographic is completely unacceptable when uttered by another – can an algorithm be trained to reliably make this distinction? Maybe, but what happens when it makes a mistake?
On the one hand, unintentional results are not the fault of the organization using the AI solution. The responsibility may lie in the data used to train the underlying machine learning model. However, customers are quick to pass judgement. If and when these unintentional biases become public, customers will quickly assign blame to the company using them, potentially with enormous impact to a brand’s reputation.
Just as CEOs may take the blame for customer data breaches, and as a result may lose their jobs, senior leaders are also at risk of taking the fall when an AI solution implemented by their organization crosses an ethical line. It’s in their best interest to ensure that doesn’t happen – their reputation depends on it.
The announced IBM and Unity partnership has the potential to expose a larger audience to the world of AI.
The implications of the deal go beyond gaming; it could change both the way consumers expect to interact with software – at home and at work – and the way developers design software in the future.
Last week, Unity and IBM announced a partnership that could have significant implications for the adoption of artificial intelligence (AI) in both consumer and business applications. The two companies launched IBM Watson Unity SDK, which enables developers to integrate Watson’s cloud-based AI features into Unity applications. Developers can include features such as Watson’s speech-to-text, visual recognition, language translation, and language classification capabilities in their programs, changing how users issue commands and how software responds to them. Continue reading “The Unity and IBM Partnership Is as Much About Business Apps as It Is About Gaming”→
• Many organizations are unsure of how to best incorporate AI to meet their industry-specific challenges – often because the use case options are so vast and so varied.
• Organizations – particularly mid-sized businesses, companies starting out on their analytics journeys, or those rolling out IoT solutions – should explore the services available from their telecom provider, many of which have built out their professional services capabilities around digital transformation.
Recent survey results reveal that companies have high expectations when it comes to artificial intelligence; close to half expect the technology to bring new capabilities to their organization.
Vendors are bringing new solutions to market to help companies implement their AI vision, offering solutions that speed and ease adoption of artificial intelligence, machine learning, and deep learning in particular.
Businesses are looking at artificial intelligence (AI) as a truly disruptive technology, with the potential to change the way they run their organizations. Unlike many other new solutions, which are often adopted because of their promises to cut costs, companies are embracing AI so they can to bring new capabilities to their teams or to improve the support they provide to their customers and partners. Continue reading “New Capabilities, Not Cost Savings, Are Biggest Driver of AI Adoption”→
Cloud collaboration brings together two of the hottest trends in the IT industry. It marries the productivity enhancements of collaborative solutions with the benefits of a cloud-based consumption model.
A recent Current Analysis survey shows that 64% of organizations use cloud services; of those, 26% already use cloud collaboration and another 15% plan to within two years.
Cloud collaboration is taking off – these services foster effective communication by bringing together real time communication such as voice, chat, video, and presence with persistent collaboration such as groups, messaging, activity streams, file editing/sharing, blogs, wikis, etc. To be most effective, solutions must be easy to use, provide business analytics/intelligence capabilities, and integrate with line of business applications. They should support the creation of a vendor ecosystem and deliver an integrated experience across applications and workflows. Continue reading “Overcoming the Obstacles to Cloud Collaboration”→