Blog Post

Next-Generation Compute Architectures Enabling Artificial Intelligence - Part I of II

Thomas A. Campbell, PhD, FutureGrasp, LLC & Robert Meagley, PhD, nR, LLC • Feb 02, 2018
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In this first of two installments, we explore the emergence and impact of compute architectures behind artificial intelligence (AI) now in the marketplace. In Part II (see https://www.futuregrasp.com/next-generation-compute-architectures-enabling-artificial-intelligence-p... ) we look at emerging compute technologies that might influence the next generations of commercial AI.

Death of Moore’s Law

In 1965, Dr. Gordon E. Moore wrote an article based on a trend he noticed: the number of transistors in an integrated circuit (IC) doubles approximately every two years [1]. Fueled by unrelenting demands from more complex software, faster games and broadband video, Moore’s Law has held true for over 50 years. [2] It became the de-facto roadmap against which the semiconductor industry drove its R&D and chip production, as facilitated by SEMATECH’s pre-competitive agreements among global semiconductor manufacturers. Recently, that roadmap has faltered due to physics limitations and the high cost-benefit economics incurred by the incredibly small scales that chip manufacturing has reached. Electron leakages and difficulties shaping matter at the single-digit nanometer scales of the transistors fundamentally limit further miniaturization. So many electrons are being moved though such tight spaces so quickly that there is an entire field in the semiconductor industry devoted just to chip cooling; without thermal controls, the ICs simply fry and fail. A new fabrication plant (fab) can cost more than $10 billion, severely limiting the number of companies able to produce denser ICs.

Despite the looming end of Moore’s Law, computationally-intensive artificial intelligence (AI) has exploded in capabilities in the last few years – but how, if compute is slowing down? The solution to exceeding compute limitations of traditional von Neumann style central processing units (CPUs) [3] has been to invent and leverage wholly new architectures not dependent on such linear designs [4].

A veritable zoo of compute architectures – including GPUs [5] , ASICs [6] , FPGAs [7] , quantum computers, neuromorphic chips, nanomaterial-based chips, optical-based ICs, and even biochemical architectures - are being researched and/or implemented to better enable deep learning and other instantiations of AI. Here we review the latest / greatest [8] of non-CPU computer architectures relevant to AI. In each section, we describe the hardware, its impact to AI, and a selection of companies and teams active in its R&D and commercialization.

GPU

Graphics processing units (GPUs) have taken the computing world by storm in the last few years. Fundamentally, GPUs operate differently than CPUs. “A simple way to understand the difference between a GPU and a CPU is to compare how they process tasks. A CPU consists of a few cores optimized for sequential serial processing, while a GPU has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously.” [9]

As deep learning requires high levels of computational power, AI is a particularly strong application for GPUs. Neural network implementation on GPU-based systems accelerates the training process dramatically compared to traditional CPU architecture; this saves substantial time and money. [10]

Nvidia is one of the earliest companies to apply GPUs to AI; its latest TITAN V PC GPU is designed specifically for deep learning in a desktop platform. [11] A US-based company, Nvidia is now a powerhouse that in Q3, 2017 controlled 72.8% of the discrete GPU market share. [12] As more OEMs [13] invest in R&D, we can expect continuous improvements in the rapidly advancing space of GPUs.

ASIC

An application-specific integrated circuit (ASIC) is an IC customized for a particular use, rather than intended for general-purpose use. [14] The advantage for AI is that an ASIC can be designed explicitly for fast processing speed and/or massive data analysis via deep learning. While ASIC processors typically implement traditional von Neumann architecture, foundry-produced ASIC chips with neural net architecture have found niche applications commercially.

There have been several press releases about companies going their own route with ASICs to become independent of general GPUs. [15] Realizing their loss of market share and wishing to not be wholly dependent upon Nvidia, numerous software firms and OEMs are designing and/or implementing their own ASICs. [16] Google is already on its second version of its TPU (tensor processing unit), designed to accelerate processing speeds in their vast server farms. [17] Intel has been working on its own AI-dedicated chip for several months. [18] Intel claims its Nervana Neural Network Processor family, or NNP for short, is on track to increase deep learning processing speeds by 100 times by 2020. [19] Tesla recently announced its efforts to build from scratch its own AI chips to have better control and faster processing speed in its electric cars. [20]

FPGA

A field-programmable gate array (FPGA) enables greater customization after manufacturing—ergo, field-programmable . “The FPGA configuration is generally specified using a hardware description language (HDL), similar to that used for an application-specific integrated circuit (ASIC). FPGAs contain an array of programmable logic blocks, and a hierarchy of reconfigurable interconnects that allow the blocks to be ‘wired together’, like many logic gates that can be inter-wired in different configurations.” [21]

While somewhat larger in scale than analogous ASIC processors, a key advantage offered by FPGAs for AI is in their application of deep neural networks requiring analysis of large amounts of data. The flexibility of programming circuitry rather than instructions enables complex neural nets to be configured and reconfigured seamlessly in a standardizable platform. [22] Intel applies FPGAs toward such deep data problems. [23] [24] Their FPGAs were shown to be competitive to GPUs earlier this year:

“While AI and DNN research favors using GPUs, we found that there is a perfect fit between the application domain and Intel’s next generation FPGA architecture. We looked at upcoming FPGA technology advances, the rapid pace of innovation in DNN [deep neural network] algorithms, and considered whether future high-performance FPGAs will outperform GPUs for next-generation DNNs. Our research found that FPGA performs very well in DNN research and can be applicable in research areas such as AI, big data or machine learning which requires analyzing large amounts of data. The tested Intel Stratix 10 FPGA outperforms the GPU when using pruned or compact data types versus full 32 bit floating point data (FP32). In addition to performance, FPGAs are powerful because they are adaptable and make it easy to implement changes by reusing an existing chip which lets a team go from an idea to prototype in six months—versus 18 months to build an ASIC.” [25] [26]

Beyond the traditional semiconductor OEMs, numerous software companies are developing FPGAs to enable faster processing speeds within their data centers. In August 2017, Amazon , Baidu and Microsoft announced manufacturing of FPGAs targeted toward machine learning. “Taken together, these initiatives point to increased opportunities for other large AI consumers and providers to have their cake and eat it too; they can optimize custom chips for their applications while avoiding the cost and potential technology obsolescence of going down the custom ASIC approach.” [27]

Quantum

Traditional computers operate with bits that are either ‘on’ or ‘off’ depending on their electrical state; quantum computers work differently. Quantum computers use qubits , in which an individual bit can be in one of three states: on, off, and, uniquely, both on and off simultaneously. As with other advanced computational architectures, quantum computing (QC) is still in early stages of development. QC has unique hardware issues – for example, the need to cool the processing chip to fractions of a single digit Kelvin to avoid disturbing processing in the qubits. QC also requires a wholly new approach to software since the binary on/off paradigm is no more, and instructions do not load sequentially, but rather execute simultaneously , from which the speed originates. Potential areas where QC could have significant impact include energy (by accelerating discovery in high energy plasma manipulation, materials chemistry, and other optimization modeling problems), medicine (by modeling complex biochemical systems to accelerate drug discovery and understanding of disease), information (by enabling big data analytics and new encryption approaches), aerospace (by modeling hypersonic aerodynamics), and materials (by simulating complex materials systems).

Speed is the leverage that QC offers AI, especially in machine learning. QC has the potential to spot patterns extremely quickly within large data sets. “It might be possible for the quantum computers to access all items in your database at the same time to identify similarities in seconds.” [28] For an AI that must analyze billions of images or data points, to execute this analysis in seconds using the power of the simultaneous on-off qubit is huge compared to a sequential CPU that might take weeks or months for the same action.

Critical challenges to all QC strategies include reducing noise and maintaining system coherence. Improved materials and architectures will continue to drive QC development. [29]

The Canadian company D-Wave Systems is one of the leaders in QC. Although there is some controversy on whether D-Wave actually makes quantum computers (their systems are marketed as more limited quantum annealers [30] [31] ), their multi-million dollar systems have been purchased by Google , Lockheed Martin and others in the past few years. D-Wave recently installed their latest 2000Q System at NASA Ames. [32] Because it is so different than traditional computing, a core challenge with QC is the dearth of software dedicated to operating in both qubit states (on and off) simultaneously. D-Wave partnered with 1QBit to meet this challenge; for example, initial solutions of previously intractable protein folding problems are being explored by 1QBit using D-Wave’s systems.

Several other companies are active in hardware and/or software development for QC. IBM announced recently they reached a threshold of 50 qubits in operation; such a system would be the largest quantum computer to date. However, “…as revolutionary as this development is, IBM's 50-qubit machine is still far from a universal quantum computer.” [33] Microsoft also recently released a preview version of its Q# (pronounced “Q sharp”) software dedicated to quantum computing. “Microsoft's hope is that this selection of tools, along with the training material and documentation, will open up quantum computing to more than just physicists.” [34] Microsoft and IBM both teased major advances in QC, recently. [35] Rigetti Computing is a startup focused on QC, with applications to AI. They are building a 19 qubit quantum computer to run clustering algorithms. [36] [37] While much R&D remains to create a practical application of their computer and algorithms, investors are still interested. “The company [Rigetti Computing] has raised around $70 million from investors including Andreessen Horowitz, one of Silicon Valley’s most prominent firms.” [38]

As quantum computers become more capable with the addition of more qubits and customized software, we can expect more AI experts to seek out their unique capabilities. Visionary venture capital firms such as BootstrapLabs have held special conference sessions on the opportunities that QC presents to AI. [39]

Neuromorphic

For all that they can accomplish, traditional computers still do not mimic the human mind. CPUs operate in a sequential manner, and, at best, GPUs operate via parallel processing. The mind, however, works highly nonlinearly. Each of our roughly 87 billion neurons has up to 10,000 connections to other nerve cells, externally, and internally hosts hundreds of thousands of coordinated parallel processes (mediated by millions of protein and nucleic acid molecular machines). We routinely use almost all of our neurons in countless waves of electrical signals to process information, all while consuming only tens of watts (compared to thousands of watts for a standard computer) [40]. A chip mimicking the brain’s architecture has the potential to be both more efficient and consume far less power than even the most advanced present-day systems. Neuromorphic computing is one approach for pursuing these advantages.

In a 2014 publication in Science , IBM described a new, ‘brain-like’ approach to IC design [41]. This represents a dramatic jump in scale and flexibility beyond the small processors featuring neural net-based circuitry that have been integrated within computer vision systems and other pattern recognition-heavy applications. [42] It is also fundamentally different from neural nets implemented within FPGA, since neuromorphic chips are optimized for neural net architecture. “The new chip, called TrueNorth, marks a radical departure in chip design and promises to make computers better able to handle complex tasks such as image and voice recognition-jobs at which conventional chips struggle…Each TrueNorth chip contains 5.4 billion transistors wired into an array of 1 million neurons and 256 million synapses. Efforts are already underway to tile more than a dozen such chips together.” [43] IBM also coded entirely new software and encourages computer scientists to create new applications with their neuromorphic chip. In very recent research, investigators at NIST have integrated superconducting Josephson junctions [44] (featured in the D-Wave quantum annealers) into neuromorphic chips delivering a blistering 1GHZ firing rate using as little as 1 atto-Joule in a 3-dimensional architecture. [45]

Another device, the memristor, has been demonstrated to have advantages in power and speed for neuromorphic architectures. These 70 nanometer wide devices were integrated to provide 10 trillion (1E12) operations per watt (compared with a CMOS [46] benchmark approximately three orders of magnitude smaller), engaged in solving the Traveling Salesman Problem. [47]

Going to the next level, Intel announced in September 2017 a neuromorphic chip designed explicitly for AI. [48] “Intel's Loihi chip has 1,024 artificial neurons, or 130,000 simulated neurons with 130 million possible synaptic connections. That's a bit more complex than, say, a lobster's brain, but a long ways from our 80 billion neurons…By simulating this behavior with the Loihi chip, it can (in theory) speed up machine learning while reducing power requirements by up to 1,000 times. What's more, all the learning can be done on-chip, instead of requiring enormous datasets. If incorporated into a computer, such chips could also learn new things on their own, rather than remaining ignorant of tasks they haven't been taught specifically.” [49]

As with other non-von Neumann architectures, software must be developed from scratch to enable the capabilities of neuromorphic computing. Companies such as Applied Brain Research are developing software such as Nengo and Spaun to simulate the mind, while the military and private companies such as Inilabs , sponsors of the Misha Mahowald Prize for Neuromorpic Engineering, and PegusApps are targeting this software gap as an opportunity in machine vision and acoustic pattern recognition . [50] [51] [52] [53]

It remains to be seen how advantageous mimicking the mind is for AI. Neuromorphic computing shows promise, but major advances are still needed in hardware (to provide more ‘neurons’ and their connections) and compatible software.

Conclusions from Part I

There is now no longer a single Moore’s Law, but rather a suite of them, with each new law a function of compute architecture, software and application. Major impetuses for this semiconductor speciation are the intense power and speed demands from AI. As more hardware and software tools become available to AI researchers, specialization will inevitably occur. GPUs, ASICs and FPGAs lead the pack now in terms of AI commercial applications, but if sufficient advances are made in quantum computers and neuromorphic computers we may see wholly new, currently inconceivable applications of AI. [54] This promise makes it potentially timely to invest early in some of these technologies. [55]

Part II of this series will explore further emerging compute technologies such as nanomaterials, optical computing, and DNA and other biological compute strategies. Such capabilities may influence future generations of commercial AI.

Notes

[1] Gordon E. Moore, "Cramming more components onto integrated circuits" Electronics, pp. 114-117, April 19, 1965.

[2] The 50th anniversary of Moore’s Law was celebrated in 2016.

[3] Central Processing Unit

[4] A “von Neumann design” refers to a traditional, linear compute CPU.

[5] Graphics Processing Unit

[6] Application Specific Integrated Circuit

[7] Field Programmable Gate Array

[8] As of January 2018. The semiconductor field moves so fast that much of what is written here will be outdated in terms of specific chip and other descriptions within weeks of this publication; however, broad concepts should remain relevant. It should also be noted that we don’t attempt to be comprehensive in terms of companies or specific hardware in this memo.

[9] http://www.nvidia.com/object/what-is-gpu-computing.html , accessed January 2018.

[10] https://www.analyticsvidhya.com/blog/2017/05/gpus-necessary-for-deep-learning/ , accessed January 2018.

[11] https://www.cnet.com/news/nvidias-3000-titan-v-gpu-offers-massive-power-for-machine-learning/ , accessed January 2018.

[12] https://www.fool.com/investing/2017/12/06/nvidia-is-running-away-with-the-gpu-market.aspx , accessed January 2018.

[13] Original equipment manufacturers

[14] https://en.wikipedia.org/wiki/Application-specific_integrated_circuit , accessed January 2018.

[15] Technically, a GPU itself is an ASIC designed for gaming calculations, but we won’t split hairs.

[16] https://www.forbes.com/sites/moorinsights/2017/08/04/will-asic-chips-become-the-next-big-thing-in-ai/#61aabc7211d9 , accessed January 2018.

[17] https://www.nextplatform.com/2017/05/17/first-depth-look-googles-new-second-generation-tpu/ , accessed January 2018.

[18] https://www.theverge.com/circuitbreaker/2017/10/17/16488414/intel-ai-chips-nervana-neural-network-processor-nnp , accessed January 2018.

[19] https://newsroom.intel.com/news-releases/intel-ai-day-news-release/ , accessed January 2018.

[20] https://www.theverge.com/2017/12/8/16750560/tesla-custom-ai-chips-hardware , accessed January 2018.

[21] https://en.wikipedia.org/wiki/Field-programmable_gate_array , accessed January 2018.

[22] https://software.intel.com/en-us/articles/machine-learning-on-intel-fpgas , accessed January 2018.

[23] http://www.newelectronics.co.uk/electronics-technology/fpgas-key-to-machine-learning-and-ai-apps/156849/ , accessed January 2018.

[24] https://seekingalpha.com/article/4101617-intel-fpgas-powers-microsofts-real-time-artificial-intelligence-cloud-platform , accessed January 2018.

[25] https://www.nextplatform.com/2017/03/21/can-fpgas-beat-gpus-accelerating-next-generation-deep-learning/ , accessed January 2018.

[26] https://dl.acm.org/citation.cfm?id=3021740 , accessed January 2018.

[27] https://www.forbes.com/sites/moorinsights/2017/08/28/microsoft-fpga-wins-versus-google-tpus-for-ai/#daa964939045 , accessed January 2018.

[28] https://www.forbes.com/sites/bernardmarr/2017/09/05/how-quantum-computers-will-revolutionize-artificial-intelligence-machine-learning-and-big-data/#2d0bf8df5609 , accessed January 2018.

[29] https://www.quantamagazine.org/the-era-of-quantum-computing-is-here-outlook-cloudy-20180124/ , accessed January 2018.

[30] “Quantum annealing is a metaheuristic for finding the global minimum of a given objective function over a given set of candidate solutions (candidate states), by a process using quantum fluctuations.” https://en.wikipedia.org/wiki/Quantum_annealing , accessed January 2018.

[31] https://blogs.scientificamerican.com/guest-blog/is-it-quantum-computing-or-not/ , accessed January 2018.

[32] https://www.dwavesys.com/press-releases/d-wave-2000q-system-be-installed-quantum-artificial-intelligence-lab-run-google-nasa , accessed January 2018.

[33] https://futurism.com/ibm-announced-50-qubit-quantum-computer/ , accessed January 2018.

[34] https://arstechnica.com/gadgets/2017/12/microsofts-q-quantum-programming-language-out-now-in-preview/?utm_source=Daily+Email&utm_campaign=bfc784b865-EMAIL_CAMPAIGN_2017_12_12&utm_medium=email&utm_term=0_03a4a88021-bfc784b865-248705265 , accessed January 2018.

[35] https://www.ft.com/stream/ab49fad4-07f5-3950-9aa0-87cdd74ce0b8 , accessed January 2018.

[36] https://www.technologyreview.com/s/609804/a-startup-uses-quantum-computing-to-boost-machine-learning/?utm_source=twitter.com&utm_medium=social&utm_content=2017-12-18&utm_campaign=Technology+Review , accessed January 2018.

[37] Clustering is a machine-learning technique used to organize data into similar groups.

[38] Ibid.

[39] https://bootstraplabs.com/artificial-intelligence/applied-ai-conference-2016/ , accessed January 2018; one author [TAC] is a special advisor to BootstrapLabs Group, LLC.

[40] That we only use 10% of our brains is a myth – e.g., http://www.wired.com/2014/07/everything-you-need-to-know-about-the-10-brain-myth-explained-in-60-seconds/ , accessed January 2018. Even sleeping, we use most of our neurons to dream, breathe, digest food, beat our hearts, etc.

[41] P.A. Merolla, et al., “A million spiking-neuron integrated circuit with a scalable communication network and interface,” Science , 345 (6197), pp. 668-673, 8 August 2014.

[42] http://ieeexplore.ieee.org/document/880850/?reload=true , accessed January 2018.

[43] http://science.sciencemag.org/content/345/6197/668 , accessed January 2018.

[44] “The Josephson effect is the phenomenon of supercurrent—i.e., a current that flows indefinitely long without any voltage applied—across a device known as a Josephson junction (JJ), which consists of two superconductors coupled by a weak link.” https://en.wikipedia.org/wiki/Josephson_effect , accessed January 2018.

[45] https://phys.org/news/2018-01-nist-superconducting-synapse-piece-artificial.html , accessed January 2018.

[46] Complementary metal-oxide-semiconductor

[47] https://spectrum.ieee.org/nanoclast/semiconductors/devices/memristordriven-analog-compute-engine-would-use-chaos-to-compute-efficiently , accessed January 2018.

[48] https://newsroom.intel.com/editorials/intels-new-self-learning-chip-promises-accelerate-artificial-intelligence/ , accessed January 2018.

[49] https://www.engadget.com/2017/09/26/intel-loihi-neuromorphic-chip-human-brain/ , accessed January 2018.

[50] http://www.wired.co.uk/article/ai-neuromorphic-chips-brains , accessed January 2018.

[51] https://www.globalsecurity.org/military/library/budget/fy2016/army-peds/0601102a_1_pb_2016.pdf , accessed January 2018.

[52] https://inilabs.com/ , accessed January 2018.

[53] https://www.pegusapps.com/en/services , accessed January 2018.

[54] https://www.theverge.com/2018/1/27/16940002/google-clips-ai-camera-on-sale-today-waitlist ; https://www.prnewswire.com/news-releases/crowdflower-announces-third-wave-of-ai-for-everyone-challenge-winners-300587610.html , accessed January 2018.

[55] For example: “NVIDIA (NASDAQ:NVDA) stock has returned 80.1% so far in 2017, through Dec. 15, continuing its rapid rise that began last year, when the stock returned a whopping 227%.” [ https://www.fool.com/investing/2017/12/18/how-nvidia-crushed-it-in-2017-a-year-in-review.aspx , accessed January 2018.]. This growth is driven primarily by the intense demand for GPUs because of AI. We might anticipate similar stock price surges elsewhere as other advanced architectures come online for AI.

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Diplomacy turmoil in age of coronavirus

Diplomacy Turmoil During and Post-COVID19

By Thomas A. Campbell, Andrew Hyde, Geoffrey M. Odlum, Ariel Ahram • 11 Jun, 2020
The coronavirus pandemic has utterly transformed international diplomacy. Statecraft has long been driven by face-to-face interactions that expand through informal mechanisms, such as body language, spontaneous conversations, and an understanding of the views of other actors beyond rote statements of positions. Such meetings are now difficult if not impossible in the era of social distancing and work from home mandates. Meetings postponed or canceled altogether have slowed a range of negotiations while many important international conferences have also been delayed or gone virtual. Cybersecurity risks have amplified as unsecure networks are leveraged for conversations that would have normally occurred face to face. Diplomats must find new ways to work in this pandemic era, as well as what the current turmoil in operations may mean post-COVID19. The practice of diplomacy has always been intertwined with state-of-the-art methods of information and communications technology (ICT). For example, telegraphy revolutionized the practices of diplomacy by accelerating the speed of communication between embassies and the metropole. The internet has had similar revolutionary impact on the practices of statecraft and diplomacy. Web-enabled ICT multiplied the number of voices and interests involved in international policymaking; complicated international decision-making; reduced exclusive control of States in the process; accelerated and freed the dissemination of information, accurate or not, about any issue or event; and enabled traditional diplomatic services to be delivered faster and more cost-effectively, both to ones’ own citizens and government, and to those of other countries. Following the development of social media, multiple government agencies are now avid posters on Twitter and other platforms in their attempts to shape public discourse at home and abroad. _________ TURN TO TURMOIL The novel coronavirus has imposed suddenly a full-court-press for diplomats to take to internet ICT, but accompanying this rapid change are new communications stresses and nuances. The functionalities and efficiencies of diplomatic ICT - including phone, email, social media messaging, and virtual meetings – are now routinely in question. The limitations of video-conferencing and other ICT-driven modes of communication have long been documented . U.S. Foreign Service Officers (FSOs) are specifically trained in face-to-face interactions and talking to groups. Exclusive reliance upon teleconference platforms such as Zoom and WebEx affect multiple core capabilities of the FSO. It is difficult to detect and project desired body language, thus introducing challenges in convincing foreign interlocutors to share private thoughts and intentions over a monitor. Negotiations are both enabled and constrained online, thus pushing the dialogue in sometimes unexpected directions. Reduced or no travel, fewer in-person meetings, and overall, limited personal interactions compromise statecraft. Video engagement changes how information and intelligence is collected, thus impacting institutional memory. Diplomatic activity and initiatives have slowed considerably as many major meetings and conferences have gone online, with others outright canceled. US Government agencies have suspended many pre-pandemic priorities and pivoted almost entirely to focusing now on coping with the virus. Security in the White House has increased with individuals working near POTUS being given daily coronavirus checks , thus self-limiting the frequency of high-level meetings. Intelligence officers at the CIA are working shifts , such as three days on followed by three days off, to improve social distancing by reducing the given number of people in buildings at any given time. In New York City, itself a pandemic epicenter, all aspects of life have been severely compromised for over a month now, detrimentally affecting United Nations Headquarters operations and activities. For example, COVID-19 has prompted changes in the working methods of the UN General Assembly : “Physical distancing and stay-at-home restrictions mean representatives can no longer meet in person, including to vote on resolutions, which are now circulated under a process known as ‘silence procedure.’ Ambassadors are given a 72-hour window to consult their capitals. If they all agree on a resolution, it is passed. If not, the resolution is not adopted as the ‘silence’ has been broken.” Webinars and online meetings are the new norm, but they impact the large-scale and often slow-moving collaborative work of drafting and editing of diplomatic agreements and statements. These processes now often involve dozens of people all working remotely using a "track changes" mode of interaction. But direct contact and interaction often undergird these collaborations, as interlocutors know each other and can converse face to face before they move into the web environment. Going straight to the web without initial constructive in-person discussions in the best case merely compromises the speed of production; in the worst case it stymies it entirely. Cybersecurity risks increase when executing all diplomacy online. A singular reliance on ICT raises new questions about the reliability and vulnerability of networks to disruption or espionage; unpreparedness opens new cyber-attack vectors for hackers, both amateurs and State-sponsored professionals. Governments may not have the requisite cyberinfrastructure and trained employees to seamlessly continue diplomacy operations from home offices. Sharing of sensitive documents either cannot be done at all, or only in a limited manner. Diplomatic online sessions can be recorded and played back with deep analyses of verbatim words and body language – normally hurried notes taking and brief summarizations are what constitute the record for face-to-face conversations. Since different organizations and different countries can adopt disparate and non-interoperable technological approaches, ICT compatibility confusion throws additional barriers at smooth statecraft. Some diplomats may avoid negotiations altogether because of security concerns, thus compromising statecraft progress in general. _____________ ACCESSING THE ADVANTAGE State engagements must continue apace regardless of the pandemic. Negotiations of peace, non-virus global health issues, human rights, climate change, nuclear nonproliferation, and trade all must occur, in whatever limited way. Embassies need to find ways to provide emergency consular services to visiting or expatriate citizens in need without putting diplomats at risk of infection. Diplomats face challenges in assessing emerging political, economic, and social trends when prevented from face-to-face meetings or travel around a country. National elections scheduled during this period of social distancing may take place without the confidence-building presence of foreign election observers, increasing potential for corrupt or anti-democratic practices to alter election outcomes. Diplomatic professionals will have to consider how the new online reality impacts their ability to advance their national interests, to understand rapidly shifting geopolitical events, and to reach publics overseas. Key is to understand how diplomacy morphs into a new normal in the coming months and years, and to identify means that diplomats and senior policymakers can prepare. Tactical challenges are to identify best ways of doing business and to implement them in real time during the pandemic. Strategic challenges are to consider how diplomacy following the pandemic might inalterably change, what new opportunities are present, and how to position diplomats accordingly. Advantages and opportunities exist in this new online reality, nevertheless. Frictionless meeting logistics with accompanying financial savings for internet-only engagements; increased opportunities to hear directly from key players; younger diplomats shining with their greater digital skills – all these and more are coming to light in the pandemic era. As stated by the United Nations’ Under-Secretary General for Political and Peacebuilding Affairs, Rosemary DiCarlo , “Although we recognize that the limitations of the processes in which face-to-face meetings are restricted, the increased use of technology has the potential to create new opportunities [and] enhance the inclusivity of peace processes - for example, including the participation of women and young people.” Concomitant with this new reality is the increased potential to leverage big data, predictive analytics, and other digital technologies. Investments should be amplified in artificial intelligence (AI) to analyze all data that can be extracted on any given country, region, or topic. Predictive analytics using machine learning (and its subset, deep learning) can then be applied to offer novel insights for meeting preparedness and briefings to senior policymakers; such approaches offer new opportunities in optimized human-machine hybrid decision-making. AI can also be used to automatically assess treaty compliance, as well as to monitor and predict events before they occur . In-situ machine language translations and real-time content assessment for both verbal and written communiques are feasible now. Increased security of voting processes via blockchain holds great potential. Virtual reality avatars can be applied to replicate the feeling of sitting across a table from a counterpart. Ultimately, there is a wide range of technological applications and solutions that await adoption and deployment by forward-leaning diplomatic corps. However long the pandemic lasts, diplomacy has been forever changed. How diplomats respond to the recent step-change in personal interactions and resultant altered statecraft will dictate whether national objectives are met, whether treaties are negotiated, and whether normal operations return - or diplomacy turmoil continues. The opinions and characterizations in this piece are those of the authors and do not necessarily represent those of the U.S. Government. Thomas A. Campbell, Ph.D. (tom.campbell@futuregrasp.com) is Founder & CEO of FutureGrasp, an advisory group that works with organizations globally to identify policy and business opportunities from emerging and disruptive technologies, especially artificial intelligence. Previously, he was the first National Intelligence Officer for Technology with the National Intelligence Council, Office of the Director of National Intelligence (ODNI). Andrew Hyde (andrew.hyde@futuregrasp.com) is a Nonresident Fellow at the Stimson Center, a Washington, D.C. based think tank, with the Transforming Conflict and Governance Program, and a Senior Advisor with FutureGrasp. Previously, he was a Foreign Service Officer at the U.S. Department of State and a Congressional staffer. Geoffrey Odlum (geoffrey.odlum@futuregrasp.com) served as a Foreign Service Officer at the U.S. Department of State from 1989 to 2017. He is currently the President of Odlum Global Strategies, a national security and technology policy consulting firm, and is a Senior Advisor with FutureGrasp. Ariel I. Ahram, Ph.D. (ahram@vt.edu) is Associate Professor and Chair of Government and International Affairs (GIA) at the School of Public and International Affairs, Virginia Tech.

AI National Plans: Their Necessity and Core Components

By Thomas A. Campbell, Ph.D. • 04 Apr, 2019
It is the position of FutureGrasp, LLC that every nation state should have an AI national plan. To remain economically competitive internally, as well as to facilitate collaboration across borders, it is imperative that governments recognize the need to channel whatever resources they possess to produce plans that enable strategic directions in the rapidly growing technology sector of AI.

Digital Twins and Digital Doubles: Populating our Virtual Worlds

By Tom Campbell, PhD; Founder & President; FutureGrasp, LLC • 07 Sep, 2018
As we move into future years with more advanced compute and exponential data availability, there will be an inexorable push to simulate more of our world. Populating our digital worlds with digital twins and digital doubles will ultimately serve us greatly in our increasingly complex real worlds.

Terminator Conundrum: Perhaps a Middle Ground?

By Tom Campbell, PhD; Founder & President; FutureGrasp, LLC • 10 Aug, 2018
It is often said that history repeats itself—we are seeing these same trends today with autonomous weapons.

Opportunities and Challenges from Artificial Intelligence for Law Enforcement

By Tom Campbell, PhD, Founder & President, FutureGrasp, LLC • 20 Jul, 2018
We offer here a review of several facets of artificial intelligence (AI) that could benefit or compromise law enforcement (LE). As AI becomes more advanced, the potential for its application by LE experts (and criminals) will only increase. Let us hope that LE officers in the field and analysts in the office are able to leverage AI to protect people around the world.

Why Stop at Voice?

By Tom Campbell, Founder & President, FutureGrasp, LLC • 19 Jun, 2018
Voice is all the buzz (no pun intended) in Silicon Valley. Many technology leaders (Apple, Amazon, Microsoft, etc.) are betting that verbal commands will soon be the major means of communication for our numerous devices. But is verbal chatter the terminal point for our computer communications?

Quantum Supremacy is Near

By Thomas A. Campbell, Ph.D., FutureGrasp, LLC • 04 May, 2018
Quantum supremacy, the point at which a quantum computer exceeds the capabilities of any classical computer , is now near at hand. I explain why and what that means for computer science.

Next-Generation Compute Architectures Enabling Artificial Intelligence—Part II of II

By Thomas A. Campbell, Ph.D., FutureGrasp, LLC & Robert Meagley, Ph.D., nR, LLC; ONE Nanotechnologies LLC • 08 Feb, 2018
We review the state of the art in advanced semiconductors as relevant to artificial intelligence (AI).

Identifying the Next Big Thing: Qual vs. Quant

By Thomas A. Campbell, Ph.D., FutureGrasp, LLC & Matt Wolfe, Ph.D., Virginia Tech Applied Research Corporation (VT-ARC) • 09 Jan, 2018
We review the challenges to qualitative forecasting of science and technology, and detail the advantages to taking a more quantitative approach using big data analytics and AI.

The Need For Artificial Intelligence: Increasing Global And Human Complexity 

By Tom Campbell • 04 Dec, 2017
The only way we can survive and thrive in this constantly changing and increasingly complex world is to get assistance from our machine creations. Artificial intelligence (AI) must be a core aspect of that help, as it offers unprecedented capabilities to monitor, control and assess a wide range of situations. AI will not (at least in the near term) remove the human from the loop; instead, it will augment our capacities for better data collection, analysis and decision making.
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