These 100 Companies Are Leading the Way in A.I.

Whether you fear it or embrace it, the A.I. revolution is coming—and it promises to have an enormous impact on the world economy. PwC estimates that artificial intelligence could add $15.7 trillion to global GDP by 2030. That’s a gargantuan opportunity. To identify which private companies are set to make the most of it, research firm CB Insights recently released its 2018 “A.I. 100,” a list of the most promising A.I. startups globally (grouped by sector in the graphic above). They were chosen, from a pool of over 1,000 candidates, by CB Insights’ Mosaic algorithm, based on factors like investor quality and momentum. China’s Bytedance leads in funding with $3.1 billion, but 76 of the 100 startups are U.S.-based.

COMPANY COUNTRY SECTOR FUNDING($ Mil.) AEYE U.S. AUTO TECH 16.27 Affirm U.S. FINTECH & INSURANCE 525 Afiniti U.S. MARKETING, SALES, CRM 80 AiCure U.S. HEALTHCARE 30.74 Algolia U.S. ENTERPRISE AI 74.02 Amplero U.S. MARKETING, SALES, CRM 25.5 Anki U.S. ROBOTICS 182 Appier Taiwan COMMERCE 81.5 Applitools U.S. SOFTWARE DEVELOPMENT & DEBUGGING 10.5 Appthority U.S. CYBERSECURITY 23.25 Aquifi U.S. COMMERCE 32.76 Arterys U.S. HEALTHCARE 42 babylon U.K. HEALTHCARE 85 Benson Hill Biosystems U.S. AGRICULTURE 34.21 Brain corporation U.S. ROBOTICS 114 Bytedance China NEWS & MEDIA 3,100 C3 IoT U.S. IOT 130.94 Cambricon China HARDWARE FOR AI 101.4 Cape Analytics U.S. FINTECH & INSURANCE 14 Captricity U.S. CROSS-INDUSTRY 49.02 Casetext U.S. LEGAL TECH 24.28 Cerebras Systems U.S. HARDWARE FOR AI 85 CloudMinds U.S. ROBOTICS 130 CognitiveScale U.S. CROSS-INDUSTRY 40 Conversica U.S. MARKETING, SALES, CRM 56 CrowdFlower U.S. ENTERPRISE AI 55.95 CrowdStrike U.S. CYBERSECURITY 281 Cybereason U.S. CYBERSECURITY 188.62 Darktrace U.K. CYBERSECURITY 182.3 DataRobot U.S. ENTERPRISE AI 124.61 Deep Sentinel U.S. PHYSICAL SECURITY 7.4 Descartes Labs U.S. GEOSPATIAL ANALYTICS 38.46 Drive.ai U.S. AUTO TECH 77 Dynamic Yield U.S. COMMERCE 45.25 Element AI Canada ENTERPRISE AI 102 Endgame U.S. CYBERSECURITY 96.05 Face++ China CROSS-INDUSTRY 608 Flatiron Health U.S. HEALTHCARE 313 FLYR U.S. TRAVEL 14.25 Foghorn Systems U.S. IOT 47.5 Freenome U.S. HEALTHCARE 79 Gong U.S. MARKETING, SALES, CRM 26 Graphcore U.S. HARDWARE FOR AI 110 InsideSales.com U.S. MARKETING, SALES, CRM 264.3 Insight Engines U.S. CROSS-INDUSTRY 15.8 Insilico Medicine U.S. HEALTHCARE 8.26 Invoca U.S. MARKETING, SALES, CRM 60.75 Kindred Systems Canada ROBOTICS 43 KYNDI U.S. CROSS-INDUSTRY 9.6 LeapMind Japan ENTERPRISE AI 13.4 Liulishuo China EDUCATION 100 MAANA U.S. IOT 40.14 Merlon Intelligence U.S. RISK & REGULATORY COMPLIANCE 7.65 Mighty AI U.S. AUTO TECH 27.25 Mobalytics U.S. E-SPORTS 2.65 Mobvoi China CROSS-INDUSTRY 257 MOOGsoft U.S. IT & NETWORKS 52.93 Mya Systems U.S. HR TECH 29.5 Mythic U.S. HARDWARE FOR AI 19.42 Narrative Science U.S. CROSS-INDUSTRY 47.87 NAUTO U.S. AUTO TECH 182.6 Neurala U.S. ROBOTICS 15.95 Numerai U.S. FINTECH & INSURANCE 7.5 Obsidian Security U.S. CYBERSECURITY 9.5 Onfido U.K. RISK & REGULATORY COMPLIANCE 59.53 Orbital Insight U.S. GEOSPATIAL ANALYTICS 78.7 OrCam Technologies Israel IOT 47 Osmo U.S. EDUCATION 38.5 PerimeterX U.S. CYBERSECURITY 35 Petuum U.S. ENTERPRISE AI 108 Preferred Networks Japan IOT 112.8 Primer U.S. CROSS-INDUSTRY 14.7 Prospera Israel AGRICULTURE 22 Recursion Pharmaceuticals U.S. HEALTHCARE 118.62 Reflektion U.S. COMMERCE 45.91 SenseTime China CROSS-INDUSTRY 637 Shape Security U.S. CYBERSECURITY 106 Sher.pa Spain PERSONAL ASSISTANTS 8.2 Shield AI U.S. PHYSICAL SECURITY 13.15 Shift Technology France CYBERSECURITY 39.72 Socure U.S. RISK & REGULATORY COMPLIANCE 33.25 SoundHound U.S. NEWS & MEDIA 114.1 SparkCognition U.S. CYBERSECURITY 43.88 Sportlogiq Canada SPORTS 7.2 Tamr U.S. ENTERPRISE AI 41.2 Tempus Labs U.S. HEALTHCARE 70 Text IQ U.S. RISK & REGULATORY COMPLIANCE 3.34 Textio U.S. HR TECH 29.5 Tractable U.K. CROSS-INDUSTRY 9.82 Trifacta U.S. ENTERPRISE AI 76.3 Twiggle Israel COMMERCE 35 UBTECH Robotics China ROBOTICS 521.39 Upstart U.S. FINTECH & INSURANCE 584.73 Versive U.S. CYBERSECURITY 57 Vicarious Systems U.S. ROBOTICS 118.03 Workey Israel HR TECH 9.6 WorkFusion U.S. RISK & REGULATORY COMPLIANCE 71.3 ZestFinance U.S. FINTECH & INSURANCE 268 Zoox U.S. AUTO TECH 290 Zymergen U.S. LIFE SCIENCE 174

Source: Fortune-These 100 Companies Are Leading the Way in A.I.

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9 AI trends to look for in 2018 RPA 2.0 initiatives

WorkFusion’s president Alex Lyashok originally wrote this post for his own feed, but we thought it was so fascinating that we decided to publish it here.

We all know that Artificial Intelligence is developing at breakneck speed. But what you may not know, is how these AI technology advancements will benefit your Intelligent Automation or RPA 2.0 programs, making them more powerful and manageable.

Here are nine trending solutions for common automation issues that will help your digital workforce improve and evolve in 2018.

Theme: AI in a Dynamic Enterprise

1. Continual Learning

Most Machine learning (ML) models are trained very infrequently (maaaybe hourly). At the same time, they are making decisions on inputs very frequently (in seconds or less). This can cause models to make incorrect decisions when they operate in a dynamic, quickly shifting environment. If a model was trained on a set of documents from one market and documents from another market start to rapidly come in, this can cause problems.

Look for:

  • Online Learning where models are trained in real-time as new data arrives.
  • Ensembles that combine frequently trained models with infrequently trained (think long-term/short-term memory).
  • Reinforcement Learning that focuses on learning the policies (not the inputs) and updating the policies based on feedback.

2. Robust Decisions

Making important decisions with machine learning means being able to deal with noisy or even adversarial inputs. For example, decisions made on inputs that the model has never seen before can be hard to evaluate.

Look for:

  • Data Provenance to track and understand where exactly the training inputs came from.
  • Confidence Management to develop more nuanced understanding of the ML model output (confidence intervals) and manage and detect unforeseen inputs.

3. Explainable Decisions

Decisions that are made in regulated or sensitive industries, such as banking or healthcare, need to be explained to people in the context of the regulatory or legal framework in which they were made. This means that you need to establish a set of preventive controls to support audits for an automated process.

Look for:

  • Interpretation to be able to interactively review a model in terms and concepts that SME is familiar with.
  • “What-if” Simulation to understand what other inputs could have led to the same decision.
  • Record and Replay to be able to trace, repeat and analyze computations that led to the decision.

Theme: Secure AI

4. Secure Enclaves

Securing AI means being able to run models in an isolated (software or even hardware) environment.

Look for:

  • Enclaves to run code in a secure environment that protects data, privacy, and decision integrity.
  • Secure Modularization to split AI code into parts where the smaller, sensitive part can be run in an enclave and the larger unprotected part can be run in an untrusted environment

5. Adversarial Learning

The adaptive nature of ML systems makes them vulnerable to new types of attacks. Broadly, they can be classified into evasion and data poisoning attacks. Evasion attacks target the inference stage, where data that can be correctly processed by a person, but is processed incorrectly by a ML model is crafted. For example, two documents may look the same to a person, but get classified differently by a computer. Data poisoning attacks target the training stage, where data is injected into the training set to cause the ML model to behave incorrectly in the future.

Look for the Data provenance and Explainable Decision capabilities described above. They can mitigate these risks and can be effectively combined with human-in-the-loop (HITL) capabilities to create reliable preventive controls in ML-based automation.

6. Shared Learning on Confidential Data

When you conduct learning across data that belongs to multiple organizations, you will derive much better results. However, when you train models on sensitive data, prohibiting leaks of confidential information can be a challenge. Hence, exploring secure multi-party learning is increasingly becoming a priority for many organizations.

Look for:

  • Differential Privacy to mix noise into data to securely obscure sensitive inputs.
  • Multi-party Computation (MPC) to allow each party to compute private inputs on joint models without learning of other party’s inputs.

Theme: AI-specific Enterprise Architecture

7. Composable AI systems

Modularity is essential to scaling systems. Breaking down complex software into modules helps reduce cost and improve manageability at scale.

Look for:

  • Model Composition to be able to assemble smaller models into ensembles where each individual model can be added or removed to improve overall output and manageability.
  • Action Composition to combine model outputs into options thereby shifting decision making to higher levels of concepts. For example, options to decline or accept claim vs. data on specific document fields.

8. Cloud-edge systems

Cloud systems are used extensively today to run and manage AI systems. At the same time, most enterprises operate systems in their data centers or on the edge of the cloud. Systems that combine cost benefits of the cloud with control advantages of the edge systems can bring the best of the both worlds together.

Look for Model Composition and Action Composition described above to be able to take advantage of secure, fast learning on the edge with the power of centralized cloud systems.

6. Domain specific hardware

Many enterprises increasingly adopt hardware architectures that increase performance, reduce cost, or improve security of AI systems.

Look for:

  • GPU and FPGA Support to reduce cost and improve scalability in data centers or public/private clouds
  • Google Tensor Processing Unit (TPU) Compatibility to accelerate certain compatible AI payloads
  • Enclave Support to take advantage of secure computation such as Intel’s SGX and ARM’s TrustZone

Visit workfusion.com to learn more about AI, Cognitive Automation, and how to expand your RPA program to take advantage of all our Intelligent Automation techniques.

Source: blog.workfusion.com-9 AI trends to look for in 2018 RPA 2.0 initiatives

B2B PAYMENTS UiPath Lands $3B Valuation For Robotics Process Automation Tech

robotics process automation (RPA) startup has just secured a $3 billion valuation as B2B FinTech continues to explore how the technology will disrupt the market. A press release issued Tuesday (Sept. 18) said CapitalG and Sequoia Capital led the $225 million Series C round for UiPath.

The funding, which also saw Accel participate, followed only a few months after the company raised $153 million, pulling UiPath’s valuation up to $1 billion. The latest investment means UiPath has tripled its valuation in less than six months, reports noted.

UiPath’s RPA technology links businesses with software that can deepen their automation capabilities beyond less sophisticated automation solutions. Reports pointed to corporate processes like accounts payable (AP), procurement and reconciliation as key areas that are prime for RPA disruption, particularly as the technology negates the need for businesses to replace their existing systems.

In its announcement, UiPath pointed to its accelerated growth, which saw its annual revenues spike from $1 million to $100 million, though it did not indicate how quickly it achieved that milestone. Still, the company said it could become “the fastest growing enterprise software company in history.” At present, the startup has 1,800 corporate customers averaging six new clients added each day. By the end of the year, the company expects its annual revenues to have quadrupled from 2017 levels.

We are enabling a future where employees at every organization are empowered to automate tedious and time-consuming work, enabling them to focus on creative, challenging problems, said UiPath Co-founder and CEO Daniel Dines in a statementWe are delighted by the strong support of our customers, partners and investors toward making this future of automation a reality. UiPath is driven by the incredible potential for our platform to be the gateway to transform our customers’ digital business operations with machine learning and AI.

UiPath did not specify what it plans to do with the latest funding round.

Source: PYMNTS-B2B PAYMENTSUiPath Lands $3B Valuation For Robotics Process Automation Tech

Cultural Counterparts: The Advantage of Nearshoring

When a business is choosing which company to outsource with, location can often be overlooked in favour of the most appropriate specialist for the project. However, location – and especially proximity – should be a critical part of the decision process. For example, if your company is based in Europe, it will be more difficult to outsource from a provider based in Asia, due to a mixture of time, travel, language, and perhaps cultural differences. To ease this dilemma, nearshoring offers greater potential in outsourcing because it has the strongest collaboration opportunities for a number of additional reasons.

Shared culture 
The key benefits of nearshoring are the shared time zones and ease of travel to meet your outsourcing partner. The further apart the companies are, the more expensive flights will be, and with the added flight time, more work time will be hindered. In addition, the working hours of the two companies based nearshore will be similar, so if a problem occurs, the teams can organise an immediate call to fix the problem. Alongside this, if both companies have working hours that are either within the exact same time zone, or very similar, weekly meetings can also be secured easily, as opposed to one party having to use time in their early morning or evening.

On top of this, you’re far more likely to be aware of—and share—the national holidays of your nearshoring partner, and will consequently be aware of which working days your outsourced partner won’t be present in their office. With these dates in mind, project deadlines can be calculated accordingly. If a country has a national holiday shortly before a deadline, you will be able to adapt the workload accordingly, rather than find out about the holiday last minute.

Communication 
The work culture of each country also impacts how companies communicate with each other. For example, misreading a message can have disastrous consequences if interpreted incorrectly, affecting both the direction and development of your partnership. The same applies to languages. If both company representatives share the same language, or at least share knowledge of a language, then the two companies can work much easier. This is far more likely to happen in regions where one language is commonly used amongst neighbouring countries.

Local legislation 
And yet, other benefits to nearshoring, such as the company having a greater understanding of the country’s changes in legislation and law, are more often ignored. For example, companies based in the EU, or with customers in the EU that store customer data will be impacted by the incoming General Data Protection Regulation (GDPR) legislation, but an outsourcing partner in Asia may not be aware of this new legislation. By nearshoring, the businesses will be aware of changing legislations or laws, and can consequently understand new processes that the company needs to undertake.

Shared culture and location are undoubtedly the biggest advantages to nearshoring. With smoother and more efficient communication and date planning comes a product that is created quicker and with greater quality, with a higher chance of having as little miscommunication dilemmas along the way as possible.

Source: futureofsourcing.com-Cultural Counterparts: The Advantage of Nearshoring