Tag Archives: artificial intelligence

Tech

CISCO launches cloud-based AI data platform

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California-based technology conglomerate CISCO announced on 10 June that it would launch a new cloud-based AI and machine learning analytics platform that can draw data from any portion of the enterprise to provide network and application visibility and insight via a subscription service.

The company introduced the ambitious project during the opening keynote at its CISCO Live user and partner conference held in San Diego from 9-14 June this year. The analytics service would collect and analyze aggregated data from subscribers as well as metrics from a single network.

In a press release, CISCO said the new platform would allow for more visibility, greater insights and “guided actions” by using “machine reasoning algorithms and automated workflows” to perform the steps an engineer would use to resolve an issue. This will theoretically help companies to “detect issues and vulnerabilities, analyze the root cause and execute corrective actions faster than ever”.

The platform will also allow CISCO to correlate data continuously collected from local networks against an “aggregate deidentified data set to create highly individualized network baselines”. According to CISCO, these baselines then “constantly learn and adapt as the number of devices, users and applications evolves, and as environments change”.

Finally, CISCO will also use machine learning to correlate data coming from a network against such baselines” to “uncover the issues that will have the greatest impact on the network”. This will improve “issue relevancy” by alerting IT departments to the “issues that matter most”, the company said, while using “trends and patterns” to pre-emptively identify some issues before they become problems.

“As the pace of change and diversity of the environment continues to rapidly evolve, Cisco is committed to continually simplifying our solutions,” Scott Harrell, Senior Vice President and General Manager of Cisco’s Enterprise Networking Business, said in a statement.

“Artificial intelligence and machine learning can enable businesses to efficiently discern which issues to prioritize, becoming more nimble and proactive,” he added. “This will have a profound effect on network operations and the IT teams that run them. At Cisco, we’re future proofing our networks and the workforce through automation and intelligence.”

Cisco AI Network Analytics will be a standard part of Cisco DNA Assurance and will be available in the next version of Cisco DNA Center, generally available summer of 2019, and Cisco AI Network Analytics capabilities will be included in the Cisco DNA Advantage software licensing tier.

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Microsoft launches AI Digital Labs in India

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American multinational technology company Microsoft said on 13 June that it would partner with colleges and universities across India to open AI (artificial intelligence) digital labs in an effort to boost technology infrastructure and educator capability, and help students to acquire skills in the field.

As part of the three-year program, Microsoft will collaborate with ten higher education institutions in India, including BITS Pilani, BML Munjal University, ISB, Kalpataru Institute of Technology, KL University, Periyar University, Karunya University, SRM Institute of Science & Technology, SVKM (NMIMS) and Trident Academy of Technology.

Microsoft plans to give the selected institutions support with infrastructure, curriculum and content, alongside access to cloud and AI services, and developer support. The company said it would facilitate the setting up of AI infrastructure and an Internet of Things (IoT) hub at the institutions as well as access to its AI developmental tools and Azure AI Services.

Training programs for the faculty of the institutions would include workshops on cloud computing, data sciences, AI and IoT, and faculty would receive assistance in strategizing content and curricula for project-based and experiential learning, the company said.

Microsoft believes that the program will serve almost 1.5 lakh (a lakh is a unit in the Indian numbering system equal to one hundred thousand) students as part of its commitment to building a “future-ready workforce”.

It said it hoped that with the edge of the company’s Intelligent Cloud Hub program, the selected institutions will become “learning centers of intelligent technologies and innovation hubs of path-breaking solutions”.

Citing a recent Microsoft and IDC Asia/Pacific study, the company suggested that “lack of skills, resources and continuous learning programs emerged as one of the top challenges faced by Indian organizations in adopting AI to accelerate their businesses”.

Microsoft’s goal with this program is to “amp up institutional setup along with educator capability, and provide relevant educational choices for students, helping them acquire the skills needed to fill the wide skills gap emerging across India and the global economy”.

“As AI becomes mainstream, organizations will require talent with skillsets that are very different from what exist now,” Anant Maheshwari, President of Microsoft India, said in a statement. “Educators and institutions are integral to the skilling revolution taking root in the country. With the right technology infrastructure, curriculum and training, we can empower today’s students to build the India of tomorrow.”

Tech

Artificial Intelligence at Google’s I/O 2019

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Artificial intelligence (AI) plays a key role at almost every technology conference these days and Google annual developer conference, held over three days between 7 May and 9 May in San Francisco this year, was no different.

I/O 2019 saw the ubiquitous search engine provider announce updates and launches across its portfolio, including the latest beta release of Android Q, Google’s cross-hardware operating system; the Pixel 3a and Pixel 3a XL smartphones; augmented reality in Google Search; Duplex on the web; enhanced walking directions in Google Maps; and more.

On the AI-focused side of things, the company announced the winners of its $25 million AI Impact Challenge, some six months after it was first launched. Coming from twelve different nations, the winners will use a Google grant of up to US$2 million each to apply machine learning to fight some of the world’s biggest challenges.

The company also unveiled three separate accessibility projects designed to help people with disabilities, including Project Euphoria, to assist people with speech impairments; Live Relay, to help those with hearing challenges; and Project Diva, which aims to help people give Google Assistant commands without using their voice.

Elsewhere, Google told attendees that Google Assistant will soon become ten times faster than its current speed with “on-device” machine learning and plans to introduced a turbocharged version of the Assistant to Pixel phones later this year.

It claimed that the updated version won’t require repeatedly triggering with a repeated hotword – e.g. “hey Google” – and will be able to complete tasks like transcription, file searches, and selfie-snapping offline, without an internet connection, thanks to smaller speech recognition model than that of the current version.

For voice app creators, Google announced a number of upgrades to its Actions on the Google platform, allowing developers, for example, to tether an action to “how to” questions using a newly introduced “how-to markup language”. This means that Google Assistant-powered apps should theoretically be better equipped to respond to commonly asked questions with relevant text, images and instructional videos.

Google Lens, the company’s visual search and computer vision tool, will soon be able to surface top meals in a restaurant when users point their smartphone camera at a menu, using its ability to recognize all manner of real-world objects. Google said that Lens will also soon be able to read translated text aloud if users point their camera at printed content and will be able to help spilt a bill or calculate a tip following a meal.

It also revealed that it has plans to expand Google Duplex, a verbal chat agent that can make appointments for you over the phone (it started rolling out to smartphone users last year), to the web, where it will be able to handle relatively complex matters such as car rental bookings for you.

The company’s cloud unit announced that it would be making pods with 1,000 tensor processing unit (TPU) chips available in public beta. Google has be developing its own TPUs — programmable, custom chips designed to power extreme machine learning tasks — for some time, and researchers and developers can use them to train AI models.

Unsurprisingly, Google also focused on the role of AI and machine learning as it relates to privacy, detailing its work in federated learning, a distributed AI approach that looks to facilitate model training by aggregating samples that are sent to the cloud for processing only after they’ve been anonymized and encrypted. The company claims that it’s Gboard keyboard for Android and iOS already uses federated learning to improve next-word and emoji encryption across “tens of millions” of devices.

On the second day of I/O, Google published a list of privacy commitments regarding its hardware products, detailing how personal data is used and how it can be controlled. The document notes, for example, that the new Nest Hub Max, which uses an on-device facial recognition feature to spot familiar people and surface contextually relevant information, doesn’t send facial recognition data to the cloud.

Tech

Samsung Electronics Expands AI Lab in Canada

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Samsung Electronics announced on 2 May the expansion of its ‘Samsung Advanced Institute of Technology (SAIT) artificial intelligence (AI) Lab Montreal’ in Canada. The lab will help the company to “strengthen its fundamentals in AI research and drive competitiveness in system semiconductors”.

The AI Lab is located in Mila – the Montreal Institute for Learning Algorithms – a research centre in the field of deep learning founded by Professor Yoshua Bengio at the University of Montreal. SAIT AI Lab Montreal has an open workspace with the aim of working closely with the AI research communities in Mila.

The lab will focus on unsupervised learning and Generative Adversarial Networks (GANs) research to develop “disruptive innovation and breakthrough technologies, including new deep learning algorithms and next generation of on-device AI”.

To drive the effort, the AI Lab has !actively recruited leaders in deep learning research”, including Simon Lacoste-Julien, Professor at the University of Montreal, who recently joined as the leader of the lab.

Also read: Where are Samsung Phones Made? It’s More than One Country

In addition, Samsung is planning to dispatch R&D personnel in its Device Solutions Business to Montreal over time, and to utilize AI Labs as a base for training AI researchers and for collaborations with other advanced AI research institutes.

Bengio is one of the world’s greatest experts on deep learning, machine learning, and AI. SAIT has collaborated with him on deep learning algorithm research since 2014, successfully publishing three papers on academic journals.

SAIT has actively pursued research collaboration with other top authorities in the field, including Yann LeCun, Professor at New York University and Richard Zemel, Professor at University of Toronto. Bengio and LeCun, along with computer scientist Geoffrey Everest Hinton won the 2018 Turing Award which is known as the “Nobel Prize in computer science”.

“Samsung’s collaboration with Mila is well established already and has been productive and built strong trust on both sides,” Professor Bengio said in a statement. “With a new SAIT lab in the midst of the recently inaugurated Mila building and many exciting research challenges ahead of us in AI, I expect even more mutually positive outcomes in the future.” “SAIT focuses on research and development – not only in next generation semiconductor but also innovative AI as a seed technology in system semiconductors,” added Sungwoo Hwang, Executive Vice President and Deputy Head of SAIT. “SAIT AI Lab Montreal will play a key role within Samsung to redefine AI theory and deep learning algorithm for the next 10 years.”

Tech

Artificial intelligence technologies for smart healthcare

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Would you trust a computer to correctly diagnose a health problem? Most of us would probably prefer to leave it in the hands of our highly trained general practitioner, emergency room doctor or surgeon. The narrative concerning the intersection between artificial intelligence (AI) is often grossly distorted towards one extreme or another: either the robots are coming to kill us and steal our jobs or they herald some new utopian era and represent the only possible source of future prosperity for the human race. Reality – as in most instances – is far more nuanced and probably lies somewhere in between these two extremes.

We’re a long way from developing Star Trek-esque androids that can perfectly simulate human behaviour and supplant your current, fully human doctor. However, there are a few ways in which AI has already begun to supplement your friendly neighbourhood doctor’s practice and a few more in the pipeline…

Wearables

Consider the humble FitBit. We’re not entirely sure that they track our steps correctly all of the time or get our heartbeat right but they’re increasingly popular and there is evidence that they do work. They monitor our fitness levels, warn us when we need to get more exercise and can also record abnormalities such as heart palpitations, potentially saving lives.

The information they record can be shared with healthcare professionals and AI systems to be analysed, giving doctors a more accurate picture of the habits and needs of their patient, especially when supplemented with medical histories and other useful patient information. This allows doctors to more carefully and accurately tailor treatments, rendering them increasingly more effective.

However, critics are concerned that this information could also be used by companies to discriminate against their employees should the data be used unethically. Experts have also voiced concerns about invasion of privacy if the data collected and stored by manufacturers of fitness trackers is either hacked or sold.

Machine learning

Healthcare professionals have already begun to use machine learning-based applications, support vector machines and optical character recognition programs such as MATLAB’s handwriting recognition technology and Google’s Cloud Vision API to assist in the process of digitising healthcare information. This helps to speed up diagnosis and treatment times as healthcare professionals are able to more quickly access complete sets of records on their patients.

The Massachusetts Institute of Technology (MIT) Clinical Machine Learning Group is leading the pack in developing the next generation of intelligent electronic healthcare records by developing applications with built-in AI – specifically machine learning capabilities – that can help with the diagnostic process. In theory, this will allow healthcare professionals to quickly make clinical decisions and create individual treatment plans tailored to their patients.

According to MIT, there is an ever growing need for “robust machine learning [that is] safe, interpretable, can learn from little labelled training data, understand natural language, and generalize well across medical settings and institutions”.

Smart algorithms

The term “AI” is somewhat misleading as it implies something more than the technology that we currently use it to describe. We don’t literally mean artificial intelligence – no true AI has been invented yet – but advanced algorithms that run on ever more powerful computers and can recognise patterns, pick information out of complex texts or even derive the meaning of an entire document from just a few sentences. This is known as artificial narrow intelligence (ANI) and comes nowhere close to artificial general intelligence (AGI) – aka the next step in developing a fully conscious AI or “superintelligence” – that can abstract concepts from limited experience and transfer knowledge from one place to another.

However, natural language processing and computer vision – the two main applications for ANI – are developing phenomenally quickly, the latter of which is based on pattern recognition and crucial for diagnostics in healthcare. Algorithms are trained to recognise various patterns seen in medical images and used to help doctors diagnose specific conditions in their patients, such as DNA mutations in tumours, heart disease, and skin cancer. This methodology does have limitations, however, as the medical evidence that the algorithms are programmed to recognise tend to originate in highly developed regions and reflect the subjective assumptions (or biases) of the working team. Furthermore, the forecasting and predictive elements of these algorithms are anchored in previous cases, and may therefore be useless in new cases of treatment resistance or side effects of drugs. Finally, the majority of AI research already conducted has been done on training data sets collected from medical facilities and doctors are provided with the same dataset after the algorithm analyses the images, usually without any attempt to reproduce the clinical conditions.

Tech

European Commission announces pilot program for AI ethics guidelines

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The European Commission (EC) announced on 8 April that it would launch a pilot program to ensure that ethical guidelines for the development and use of artificial intelligence (AI) can be implemented in practice.

This is the second step in the Commission’s three-part approach to the question of ethical AI, following the development of seven key requirements or guidelines for creating “trustworthy” AI developed by the High-Level Expert Group.

These include: human agency and oversight; robustness and safety; privacy and data governance; transparency; diversity, non-discrimination and fairness; societal and environmental well-being; and accountability. The Commission added that any AI that can be considered trustworthy should also respect “all applicable law and regulations”.

Industry, research institutes and public authorities have been invited to test an assessment list drafted by the group to complement the guidelines. The 52-strong panel of independent experts was appointed by the Commission in June 2018, and is comprised of representatives from industry, academia and civil society.

According to the Commission, the third and final step in its plan will be to work on building an “international consensus” on human-centric AI as “technologies, data and algorithms know no borders”.

These plans are a component of the Commission’s overarching “AI strategy”, which aims to increase public and private investments to at least €20 billion annually over the next decade in order to make more data available, foster talent and “ensure trust”.

Members of the group will present their work in detail at the third “Digital Day” in Brussels on 9 April. Following the conclusion of the pilot phase in early 2020, they will review the assessment lists for the key requirements, building on the feedback they receive, after which the Commission plans to evaluate the outcome of the project so far and propose next steps.

The Commission has also pledged to launch a set of networks of AI research excellence centres; begin setting up networks of digital innovation hubs; and together with Member States and stakeholders, start discussions to develop and implement a model for data sharing and making best use of common data spaces; before autumn 2019.

“I welcome the work undertaken by our independent experts,” Vice-President for the Digital Single Market Andrus Ansip said in a statement. “The ethical dimension of AI is not a luxury feature or an add-on. It is only with trust that our society can fully benefit from technologies.”

For Ansip, ethical AI is a “win-win proposition” that could create a “competitive advantage for Europe” should it become “a leader of human-centric AI that people can trust”.

“Today, we are taking an important step towards ethical and secure AI in the EU,” Commissioner for Digital Economy and Society Mariya Gabriel added. “We now have a solid foundation based on EU values and following an extensive and constructive engagement from many stakeholders including businesses, academia and civil society.”

The Commission is looking to put these requirements into practice while simultaneously fostering “an international discussion on human-centric AI,” she said.

AI refers to digital systems that show intelligent, human-like behaviour. By analysing their environment they can perform various tasks with some degree of autonomy to achieve specific goals, learning from data to make predictions and deliver useful insights.

The Commission estimates that the economic impact of the automation of knowledge work, robots and autonomous vehicles on the EU will reach between €6.5 and €12 trillion annually by 2025. The body has already invested what it describes as “significant amounts” in the development of AI, cognitive systems, robotics, big data, and future and emerging technologies in a bid to make Europe more competitive in this area.

This includes around €2.6 billion on AI-related areas and €700 million on research programs studying smart robots. The Commission intends to invest further in research and innovation up to and after 2020, including €20 billion per year in combined public and private investment.

However, Europe is currently behind in private investments in AI having spent €2.4 to €3.2 billion on development in 2016, compared with the €6.5 to €9.7 billion spent in Asia and €12.1 to €18.6 billion in North America.

In a press release, the Commission acknowledged that while AI has the potential to benefit a wide range of sectors – such as healthcare, climate change, law enforcement and security, and financial risk management, among others – it brings new challenges for the future of work, and raises significant legal and ethical questions.