为小公司而生的律师匹配服务 Lexoo 获 40 万美元融资
在我自己的创业冒险旅程中，我得出了这样一条结论，无论你是成功还是失败，肯定能赚钱的只有两种人，云服务提供商和律师。关于前者，价格和竞争形势都相当的透明。然而律师费就远非如此了。这正是英国创业公司 Lexoo 正在着手解决的问题。
为了帮助实现这一愿景，这家提供了自称为“律师匹配”服务的伦敦公司刚刚完成了 40 万美元的种子轮投资，投资方为 Forward Partners——一家 由尼克·布里斯伯恩（Nic Brisbourne）领导 的风投公司及创业公司“工厂”——以及乔纳森·麦凯（Jonathan McKay，JustGiving 董事长）。
“对于小公司来说，找到一名优秀律师并在此过程中不挨宰是件难事，”Lexoo 联合创始人兼首席执行官丹尼尔·范·宾斯伯根（Daniel van Binsbergen）解释道，此前他曾是一位专注于金融与并购领域的律师。“名牌律师事务所通常都很棒，但管理费用繁杂，因此每小时收费很高。对于小公司而言，很难知道哪一位律师真的能干，是否擅长这一领域。最后，如果你采用一名推荐律师的话，很难判断你给出的价格是否合理。”
为了解决这一难题——可以将此视作网络将市场变得更加高效透明的经典案例——公司可以根据工作类型需求，通过 Lexoo 平台获得一批精选律师队伍的多方报价。在 Lexoo 平台发布工作后，你会从多个胜任该工作的律师收到富有竞争力的报价，通常在 24 小时内。
“我们确保只有真正擅长于特定法律领域的律师才能为某件工作报价，每一位律师加入平台之前，我们都会对他进行面试，”范·宾斯伯根说道，“客户可以将报价和律师履历进行比较，做出更加明智的决定。我们的卖点在于，帮你找到曾在大型律师事务所摸爬滚打的资深律师，只不过他们现在因为生活方式的原因选择在家工作或者在小公司工作。这一点使得他们的常规报价只有之前每小时费用的一半左右。”
为此 Lexoo 不收取客户费用，不过律师需要将任何通过这一平台拿到的工作报酬的 10%作为佣金付给这家创业公司。
Lexoo Scores $400K For Its Lawyer-Matching Service For Small Businesses
During my own startup adventures, I came to the conclusion that, succeed or fail, the only people guaranteed to make money are cloud providers and lawyers. With regards to the former, pricing and competition is fairly transparent. But legal fees, not so much. That’s the problem UK startup Lexoo has set out to solve.
To help with that mission, the London-based company, which offers what it calls a ‘lawyer-matching’ service, has just scored a $400k seed round from Forward Partners — the venture firm and startup ‘foundry’ headed up by Nic Brisbourne — and Jonathan McKay (Chairman of JustGiving).
“It’s difficult for small businesses to find a good lawyer and not get overcharged in the process,” explains Lexoo co-founder and CEO Daniel van Binsbergen, who previously worked as a lawyer focussing on finance and M&A. “Brand name law firms are usually great but have a lot of overheads and therefore high hourly rates. With smaller firms it is hard to know which lawyers are actually good and specialised. Finally, if you go with a recommended lawyer it is hard to judge whether the price you are quoted makes sense or not.”
To tackle this — in what can be considered a classic example of how markets can be made more efficient and transparent by moving them online — Lexoo enables businesses to get multiple quotes from a curated line-up of lawyers, based on the type of work required. After posting a job, you receive multiple and competitive quotes from the qualified lawyers on the platform, usually within 24 hours.
“We make sure that only lawyers who are actually specialised in a certain area of law are invited to quote for a job and we screen and interview every single lawyer before they can join the platform,” says van Binsbergen. “Customers can then compare the quotes and lawyer profiles and make a much more educated decision. Our sweet spot is finding lawyers who trained at the largest firms but for lifestyle reasons are working at home, virtually or in a small office. This enables them to usually charge only 50 per cent of their previous hourly rates.”
To that end, Lexoo is free for clients, but lawyers pay the startup a 10 per cent commission on any work they obtain through the platform.
来源：techcrunch
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2015年01月19日
硅谷
Facebook宣布开源深度学习人工智能工具[摘要]Facebook的人工智能研究团队今天宣布，将开源其深度学习人工智能工具。
Facebook的人工智能研究团队今天宣布，将开源其深度学习人工智能工具。
Facebook将通过Torch库发布这一软件。Torch是一个协助机器学习技术开发的开源环境，被学术界，以及谷歌、Twitter和英特尔等公司在研究中广泛使用。
Facebook表示，相对于Torch中的默认软件，其深度学习模块的执行速度要更快。这将帮助研究人员在更短的时间内开发规模更大的神经网络。
Facebook希望，分享这一工具将推动整个深度学习研究领域的发展。（李玮）
Facebook open-sources its deep-learning AI tools
Facebook is sharing some of its technology. The company’s artificial intelligence research team today announced that it is open sourcing its deep-learning AI tools.
The software will be available on the Torch library, which serves as an open-source environment for machine learning development. Torch is widely used for research in academia, as well as by companies like Google, Twitter and Intel.
Facebook claims its deep-learning modules are significantly quicker than the default ones available through Torch, and allow the company to work on larger neural networks in less time.
Notable improvements include a 23.5x speed-up over publicly available convolutional layer codes, and the ability to parallelize neural networks training over GPU cards.
The company hopes sharing its tools will help optimize progress across the entire deep-learning research landscape.
来源：TNW
关注员工健康是趋势 促进企业员工运动的应用 Yomp 完成种子轮融资
位于伦敦、此前名为 PleaseCycle 的健康创业公司 Yomp 已从 5 名天使投资人处融资 31.5 万美元（20 万英镑）。该公司将利用这笔资金去拓展其健康平台，这一平台帮助企业员工以游戏的方式形成良好的健康习惯。
Yomp 的平台设计能鼓励企业员工参与运动，从而提高效率，降低缺勤率，让你能喜欢你的老板。
Yomp 的移动应用能记录距离，追踪数据，帮助用户组建团队，设置目标，并赢得积分。这就像是健康市场的 AirMiles。这些数据随后可以换取在线折扣券，奖励，或是向慈善组织的捐款。
已签约使用该应用的公司包括电通安吉斯传媒、彭博社和 EDF Energy，前者已经在 9 个国家部署了这款应用。
Yomp 现年 26 岁的创始人及首席执行官莱伊·摩根（Ry Morgan）表示：“我们此前一直小本经营，利润微薄。不过目前，随着资本的注入，我们将推动业务向前发展。”
Yomp 的竞争对手包括 Global Corporate Challenge、Keas（谷歌前高管创立的一家公司，已实现高速增长）、EveryMove、GetHealth、MaxwellHealth，以及 Challenge forChange / Love2Ride。
这些“量化自我”公司也开始探索 B2B 市场，例如 Jawbone 近期推出 了“Up for Groups”。
传统的员工福利及医疗健康提供商也来到了这一市场，不过它们已经落后，例如 PruHealth 的 Vitality。
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Yomp, An App To Get Employees Fit And Healthy, Raises An Angel Round
Londonbased health startup Yomp, formerly known as PleaseCycle, has raised $315,000 (£200,000) from five angel investors, to expand its heath and wellness platform which ‘games’ company employees into healthy habits.
Yomp’s platform is designed to encourage physical activity amongst a company’s employees, helping to increase productivity, reduce absenteeism and make you like your bosses (ok, maybe not that last bit).
A mobile app logs trips, tracks stats, lets you form teams, set goals and earn points – like an AirMiles for being healthy. These are then used to get online discounts, win prizes, or give donations to charity.
Companies signed up to use the app include Dentsu Aegis Media, Bloomberg and EDF Energy, with the former deploying it in nine countries.
Ry Morgan, Yomp’s 26 year-old Founding CEO, says, “We’ve built everything to-date on a shoestring and retained profits, but we’re now at a stage where an influx of capital will help drive the business forward.”
Competitors to Yomp include the incumbent in the space, Global Corporate Challenge, Keas (ex-Google founders with huge growth), EveryMove, GetHealth, Maxwell Health (bigger remit but similar budgets) and Challenge for Change / Love2Ride .
The QS companies are also starting to explore the B2B space, such as Jawbone’s recent launch of “Up for Groups”.
Traditional employeebenefit and healthcare providers are also in this space too, although they’re lagging behind, such as PruHealth’s Vitality.
来源：techcrunch
硅谷
2015年01月11日
硅谷
大数据＋机器学习＋平台，Dato拿了1850万美元B轮融资大数据在硅谷炙手可热，拿融资自然也不再话下，拿到千万级美元融资的也有不少，不久前大数据分析云服务GoodData就获得了Intel Capital领投的2750万美元。据华尔街日报消息，机器学习平台 GraphLab 刚刚改名Dato，并获得了 1850 万美元新融资，投资方为 Vulcan Capital 、Opus Capital 、New Enterprise Associates、Madrona Venture Group。此前他们曾获得680万美元融资。
GraphLab 提供了一个完整的平台，让客户能够使用可扩展的机器学习系统进行大数据分析。简单来说，就是从别的应用程序或者服务中抓取数据，让机器学习这个模型，并将学到的知识作为基础，自动地进行准确的预测和决策制定。这么讲挺抽象，我们还是具体举几个例子吧。可能最好理解的就是民主国家政府的民意调查，可以通过社交网络、媒体等提取数据，分析出民众到底在关心什么，分析出哪些区域的哪些问题必须关注、解决。其实，生物医学研究团队也会使用 GraphLab，主要是来分析临床记录，从而预测病人的病情发展趋势。零售业可以做价格预测、用户推荐；金融服务业可以做诈骗预警；市场公司则可以通过情绪分析锁定关键客户。现在 GraphLab 的客户已经包括 Zillow、Adobe、Zynga、Pandora 等。
那为什么是 GraphLab（现在应该叫 Dato 了）会获得这么多客户的青睐呢？ 其实，将原始数据转化为决策依据，并作出预测，这个过程还是很复杂的。往往需要大量的数据处理工具，收集、清洗数据，再建模分析，得出结论，进行展示；还需要大量的数据科学家或同样知识渊博的软件工程师来配合完成。既耗时费力，还投入不菲。所以 GraphLab 这样的平台，可以让毫无编程经验的数据科学家，快速地将理念转化为生产环境可以使用的产品，提高企业的生产效率，自然受欢迎。值得一提的是，Dato 现在能处理各种数据类型。
GraphLab 的创始人 Carlos Guestrin 是机器学习界国际公认的大牛，曾被 Popular Science 杂志评为 2008 年 “Brilliant 10”，还获得过美国青年科学家总统奖。2008 年在卡耐基梅隆大学带着两个学生研发了 GraphLab 的原型，2012 年被 Jeff Bezos 游说去了华盛顿大学。
在 Madrona Ventures 和 NEA 的资金支持下，2014 年 3 月创办了 GraphLab，并以测试版的形式推出了第一个商业版。2013 年 10 月，增加了机器学习功能，推出了新版本。现在的 GraphLab 已经不仅仅是图谱分析了，更是一家基于 AI 的大数据公司，能够处理各种数据类型，所以公司也改名叫 Dato 了。
在美国现在的大数据公司主要有四类：
数据的拥有者、数据源：特点是业务优势能收集到大量数据，就像煤老板垄断一个地区的矿一样。其实大多数有能力产生或收集数据的公司都属于这类型，比如Vantage Sports和收集了PB级数据的包子铺。
大数据咨询：特点是非常技术，提供从基础设施规划建设维护到软件开发和数据分析等的服务，但不拥有数据，比如Cloudera这家不到500人的startup是最著名的Hadoop架构咨询公司。
做大数据工具的：比如AMPLab出来的Databricks和Yahoo人主导的Hortonworks。
整合应用型：特点是收集拥有或购买一些数据，然后结合AI来解决更多实际的痛点。
像 Dato 这种做整合应用型的大数据公司才有可能有希望。未来是 AI 的，而 AI 的食物是数据。就像很多产业链一样，最困难且最有价值的创新往往发生在接近最终用户的那端，比如 iPhone。大数据行业最有价值的部分在于如何利用机器去处理数据得到洞见，影响组织和个人的行为，从而改变世界。收集和整理数据在未来会变得标准化和自动化，而利用 AI 进行分析的能力会变得更为关键。
[36氪，作者: 小石头]
硅谷
2015年01月09日
硅谷
新加坡数据分析初创公司TookiTaki 筹资100万美元以开拓亚太市场
新加坡数据分析初创公司 TookiTaki 于今天宣布，该公司已筹得 100 万美元的种子资金，这笔资金由 Jungle Ventures 领投，Rebright Partners 和 Blume Ventures 跟投。该资金将用于扩大 TookiTaki 作为软件平台在东南亚、日本、澳大利亚和新西兰的新市场。
TookiTaki 最初是一家广告技术公司，帮助用户解决在社交媒体网络和其他在线平台上在哪里打广告的问题。经过不断发展，TookiTaki 扩大其业务范围，为客户提供数据分析，以及有关客户行为、市场营销和产品销售的预测服务。
创始人兼首席执行官阿布舍克·查特吉（Abhishek Chatterjee）称，TookiTaki 的竞争对手包括为公司解读社交媒体数据的 Quantifand，以及自动购买针对性在线广告的 Taykey。查特吉称 TookiTaki 的不同之处在于，它着眼于从不同来源收集多个数据集，包括公开数据和线下数据，并利用这些数据集预测消费者行为，这能够更有效地帮助客户规划营销和销售活动。
除了开拓新市场，TookiTaki 种子资金的一部分也将用于研发。目前，该公司正致力于开发一个能对消费者如何发现特定品牌或产品这一行为做出预测的平台。
Singaporean Data Analytics Startup TookiTaki Raises $1M To Expand In Asia-Pacific
TookiTaki, a Singaporean data analytics startup, announced today that it has raised $1 million in seed funding led by Jungle Ventures, with participation from Rebright Partners and Blume Ventures. The capital will be used to expand its software platform into new markets in Southeast Asia, Japan, Australia, and New Zealand.
TookiTaki started out as an ad tech company that helped users figure out where to place advertisements on social media networks and other online platforms. Eventually, TookiTaki expanded its services to provide data analysis and predictions about customer behavior, marketing, and product sales for companies.
Founder and CEO Abhishek Chatterjee says that his company’s competitors include Quantifand, which interprets social media data for companies, and Taykey, which automates the purchase of targeted online ads. Chatterjee says TookiTaki differentiates by focusing on gathering multiple data sets from different sources—including publicly available data and offline data—and using them to produce predictions about customer behavior that can help companies more effectively plan marketing and sales campaigns.
In addition to expanding into new markets, part of TookiTaki’s seed funding will also be used for research and development. The company is currently working on a platform that will give predictions about how consumers find out about specific brands or products.
来源：TC
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