CATALOG The world will generate 160 zettabytes of data in 2025. That’s more bytes than there are stars in the observable universe. Conventional storage media like flash-drives and hard-drives do not have the longevity, data density, or cost efficiency to meet the global demand. CATALOG is building the world’s first DNA-based platform for massive digital data storage.
Interpretable AI The company is bringing interpretability to machine learning and artificial intelligence and was co-founded by Professor Dimitris Bertsimas of MIT Sloan School of Management’s Operations Research Center (ORC).
Osaro Advanced imaging AI for robotics that can identify objects others cannot.
Digital Health mobile apps and connected medical devices are rapidly changing how patients learn, monitor, diagnose and treat disease. Even in these early days of the digital transformation of healthcare, connected medical devices and digital services are winning reimbursement as “digiceuticals” by payors and insurers. However, the critical need going forward is how to measure, compare and prove these new tools and digital biomarkers are safe, effective and valuable at scale, not just in the USA but globally, across geographies, cultures and health systems.
As a strategy to save the cost of expensive substrates in semiconductor processing, the technique called “layer-transfer” has been developed. In order to achieve real cost-reduction via the “layer-transfer”, the following needs to be insured: (1) Reusability of the expensive substrate, (2) Minimal substrate refurbishment step after the layer release, (3) Fast release rate, and (4) Precise control of a released interface. Although a number of layer transfer methods have been developed including chemical lift-off, optical lift-off, and mechanical lift-off, none of those three methods fully satisfies conditions listed above. In this talk, we will discuss our recent development in a “graphene-based layer-transfer” process that could fully satisfy the above requirements, where epitaxial graphene can serve as a universal seed layer to grow single-crystalline GaN, III-V, II-VI and IV semiconductor films and a release layer that allows precise and repeatable release at the graphene surface. We will further discuss about cost-effective, defect-free heterointergration of semiconductors using graphene-based layer transfers.
Lastly, I will introduce our new research activities in developing advanced RRAM devices. Resistive switching devices have attracted tremendous attention due to their high endurance, sub-nanosecond switching, long retention, scalability, low power consumption, and CMOS compatibility. RRAMs have also emerged as a promising candidate for non-Von Neumann computing architectures based on neuromorphic and machine learning systems to deal with “big data” problems such as pattern recognition from large amounts of data sets. However, currently reported RRAM devices have not shown uniform switching behaviors across the devices with high on-off ratio which holds up commercialization of RRAM-based data storages as well as demonstration of large-scale neuromorphic functions. Recently, we redesigned RRAM devices and this new device structure exhibits most of functions required for large-array memories and neuromorphic computing, which are (1) excellent retention with high endurance, (2) excellent device uniformity, (3) high on/off current ratio, and (4) current suppression in low voltage regime. I will discuss about the characterization results of this new RRAM device.
Platform firms are coming and will impact you in ways that you cannot control. The successful business models of the last generation are no longer sufficient and corporations must adapt to the multi-sided markets that are the hallmark of the platform business model. How quickly an industry adapts to and utilizes platforms depends on regulation, cost, and risk. Join Geoff Parker to explore why platform firms are a threat, how they will affect your business, and how you can transform your business model to compete.
Visual object detection and recognition are needed for a wide range of applications including robotics/drones, self-driving cars, smart Internet of Things, and portable/wearable electronics. For many of these applications, local embedded processing is preferred due to privacy or latency concerns. In this talk, we will describe how joint algorithm and hardware design can be used to reduce the energy consumption of object detection and recognition while delivering real-time and robust performance. We will discuss several energy-efficient techniques that exploit sparsity, reduce data movement and storage costs, and show how they can be applied to popular forms of object detection and recognition, including those that use deep convolutional neural nets (CNNs). We will present results from recently fabricated ASICs (including our deep CNN accelerator named “Eyeriss” which is 10x more energy efficient than a mobile GPU) that demonstrate these techniques in real-time computer vision systems.
How can you protect yourself against threats you don’t know about? What measures can you take to assess your risk before a breach? How can you protect yourself against an attack that originates in an innocuous object like a toaster? Professor John Williams will discuss how organizations can prepare themselves to defend against cybersecurity threats to protect their enterprises. He will discussrisk a modeling and data analytics tool (Saffron), that helps to identify risk tolerance and strategies for assessing, responding to, and monitoring cyber security risks.
Twenty years ago the idea of speaking with a chatbot to resolve a problem was unheard of. Today we can ask Siri to make us a reservation for a nearby restaurant with the touch of a button. Artificial intelligence, wearables, virtual reality, and the Internet of Things are rapidly changing the world around us. From clothing that can track your fatigue to the changes in the process of booking a hotel room, Professor Casalegno will discuss the future of these technologies and where they will take us.